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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 6Week of Sep 21

Latest papers 39

Oct 1, 2026eess.SY

Finite-Data Safety Informativity Under Dynamic Asymmetric Actuation

When the system model is not fully known, measurement error and limited excitation can leave several models consistent with the same finite data. A command judged safe for one model may fail for another, while limited control authority can prevent the corrective action needed to preserve safety. To ensure safety under model uncertainty and asymmetric input limits, we develop a finite-data certificate that determines whether a command can enforce a prescribed safety inequality. For a linearly parameterized safety channel with exactly known regressors and bounded aggregate residual error, we derive a support formula for the worst-case safety contribution of all data-consistent models. The formula identifies the regressor directions that admit a finite bound, allowing rank-deficient records to contribute to safety certification. Using certified componentwise bounds on actuator tracking error yields an affine inequality with a necessary and sufficient test for pointwise command feasibility. The affine inequality reduces computation of the closest certified command to a scalar root-finding problem. It also yields a closed-form gate that selects the largest certified fraction of a prescribed command segment. The proposed certificate guarantees output safety within its operating domain, provided the feedback is locally Lipschitz and the uncertainty bounds remain valid. Domain retention and full-state continuation extend this guarantee to all time. A vehicle study demonstrates that output safety can be certified from finite measurements in a safety-critical setting with model and actuator uncertainty.

Abhinav Sinha, Praveen Kumar Ranjan, Yongcan Cao

GALACxIS Lab, Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH 45221, USA · Unmanned Systems Lab (USL), Department of Electrical Engineering, The University of Texas at San Antonio, TX 78249, USA

Probabilistic Safety Finite-Sample Certificates Robust Control Actuator Upper Bounds Continuation

Oct 1, 2026cs.RO

EIDA: Execution-Interface Dynamics Adaptation for Real-to-Sim-to-Real Robot Navigation

Simulation-to-robot transfer can fail when velocity commands produce motion and feedback that differ from those modeled during policy training. We present execution-interface dynamics adaptation (EIDA), which fits these responses from target-platform execution data without reconstructing actuator dynamics. A model of body-frame pose increments updates simulator geometry, while a separate model predicts the velocity feedback observed by the policy; a short history of velocity feedback is included in the policy input. The fitted models are used within a lightweight GPU-parallel simulator. On the full Jackal and Go2 validation sets, the fitted models reduced position and yaw prediction errors relative to the simulator's predefined motion model. Across 100 benchmark navigation environments evaluated in a separate physics-based simulator, EIDA achieved the highest success rate and navigation score among the compared learned policies, both with and without global guidance. Feedback ablations further supported the need to match policy-facing velocity estimates. On a physical Unitree Go2, EIDA reached the goal without collision in all 20 static-scene trials, compared with 4 of 20 for the baseline. These results show that execution-interface adaptation can improve navigation transfer without detailed actuator simulation.

Yiwei Qian, Shanze Wang, Qingyuan Hu +2

Eastern Institute of Technology, Ningbo, China · National University of Singapore, Singapore · Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong +1

Inverse Dynamics Actuator

Sep 30, 2026cs.RO

Making Waves: A Membrane-Coupled Delta Array for Manipulating Objects Below the Actuator Spacing

Distributed manipulator systems manipulate objects through the coordinated motion of many actuators. However, an object must be supported by several actuators at once, so the centre-to-centre actuator spacing imposes a hard lower bound on manipulable object size. We remove this bound by coupling the end-effectors of an 8 x 8 array of three degrees-of-freedom delta robots with a stretchable fabric, turning 64 discrete contacts into a continuous surface capable of manipulating objects smaller than the actuator spacing. Viewing the array as a displacement field over that surface, we investigate local quasi-static and cyclic fields as manipulation primitives. These primitives can be applied globally across the array or locally confined around each tracked object to independently manipulate several objects in parallel. We then train a policy acting on low-order discrete cosine transform coefficients: at equal action dimension, commanding a nineteen-delta neighbourhood halves the placement error of commanding the whole array. The policy transfers to hardware without adaptation at 76% success. The platform manipulates objects from 15mm-90mm, a six-fold range spanning both sides of the actuator spacing, 43.3mm, on a single surface.

Bailey Dacre, Andrés Faíña, Oliver Kroemer +1

Sep 30, 2026cs.RO

Residual Wrench Certification and Margin-Aware Control Synthesis for Aerial Physical Interaction

We develop a task-relative framework for certifying residual wrench authority after hover and contact loading in multirotors with bounded actuators. Using convex geometry, we derive signed margins for prescribed convex reserves, including Euclidean balls and weighted ellipsoids. We obtain computable reserve certificates from actuator-interiority bounds to support slack-maximizing allocation. To preserve the required reserve, we propose command projection onto a tightened feasible set. We then connect the available reserve to structured gain synthesis and certify a local tracking region that respects actuator limits. Within this region, we establish nominal exponential convergence and robust ultimate boundedness. For the prescribed task and morphology family, we show that the optimized octarotor attains a larger margin than the optimized hexarotor at equal total thrust. We evaluate the proposed framework in closed-loop simulations of sustained rigid-wall contact using a fixed-geometry octarotor and a variable-tilt quadrotor with matched installed thrust. The quadrotor's local certificate admits a higher normalized push limit for the prescribed task family. In tests beyond the realizability boundaries, we observe the predicted loss of authority through rotor-thrust limits for the octarotor and tilt-servo limits for the quadrotor.

Abhimanyu Khadga, Abhinav Sinha, Shashi Ranjan Kumar

Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering, University of Cincinnati, OH 45221, USA · Intelligent Systems and Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

Quadrotor Contact Wrench Estimation Actuator Adaptive Control

Sep 28, 2026cs.RO

Test-Time Adaptation of Manipulation Policies Under Actuator Degradation

Robot manipulation policies are usually trained under the assumption that a commanded action produces the same motion as it did during training even after hours of operation. Real hardware violates this assumption as the motors gradually heat up, current saturates near contact, voltage sags under load, thus the same policy action can produce a weaker, delayed, or noisier motion. These conditions are already measured by onboard telemetry, such as joint temperature, motor current, and supply voltage, yet this signal is typically used only for logging or safety checks rather than policy adaptation. We introduce Telemetry-Aware Action Rectification (TeAR), a policy-agnostic method that turns a frozen manipulation policy into a telemetry-conditioned policy by rectifying its outgoing action before it reaches the low-level controller. TeAR learns a lightweight Transformer that combines the proposed action with live actuator telemetry and amplifies, damps, or biases individual action components. We evaluate TeAR across 18 policy-task pairs spanning 8 policy families and 5 manipulation tasks. In an additional paired evaluation with degradation-model mismatch, TeAR achieves 31.8% success, compared with 25.6% for the base policy and 30.6% for an assumed-model inverse. On a physical arm, TeAR improves success under heating by 10-15% without on-robot fine-tuning.

Som Sagar, Ransalu Senanayake

School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona

Actuator Goal-Conditioned Value Function Test-Time Adaptation Rectification Test Time

Sep 24, 2026cs.RO

A Tendon-Driven Robotic Jellyfish with Constrained Soft Actuation and Depth Control via Reinforcement Learning

Jellyfish-inspired robots offer a compliant and efficient approach to underwater locomotion, but achieving large deformation together with repeatable actuation and closed-loop control remains challenging. In this work, we present a tendon-driven robotic jellyfish with constrained soft actuation. Each actuator combines a flexible substrate with discrete constraints, enabling bending up to

150^\circ

with an approximately linear tendon displacement-bending relationship. Eight actuators driven by four servos allow the robot to perform stable swimming, attitude adjustment, and self-righting. Based on the linear actuation, a reinforcement-learning controller is further developed, enabling closed-loop depth regulation in both simulation and physical experiments. These results show that mechanical constraints can improve the controllability of soft actuation while preserving compliant jellyfish-like motion, providing a route toward manoeuvrable and autonomous jellyfish robots.

Jiarui Peng, Yutong Wu, Zelong Wang +5

Hong Kong Embodied AI Lab, Hong Kong SAR, China · The Chinese University of Hong Kong, Hong Kong SAR, China · The Hong Kong University of Science and Technology, Hong Kong SAR, China

Soft Robotics Actuator

Sep 24, 2026cs.RO

CALM: Current Aligned Link Manipulation for Single Arm Oversized Object Lifting

Most robots manipulate objects solely with their end effectors, whereas humans flexibly leverage different body parts, such as the forearm and elbow, especially when handling oversized objects. Learning such whole-arm manipulation is chal-lenging due to long-horizon sparse rewards, limited contact sens-ing, and the sim-to-real gap in contact and actuator dynamics. To address these challenges, we propose Current-Aligned Link Manipulation, a framework for learning long-horizon contact-rich manipulation using motor current as joint load related feedback. Three stage-specific policies first learn repositioning, grasping, and lifting using privileged simulation information, and a stage router sequences them to generate complete task demonstrations. For sim-to-real transfer, a causal current mapper predicts physical motor current from simulated joint histories, aligning the actuator current observation between simulation and hardware. A unified student policy then learns from these demonstrations using only deployable sensor observations and is further refined with DAgger. The task policies are trained entirely in simulation, and the final student is deployed on hardware. Experiments demonstrate 76.2% (762/1000 trials) complete-task success in simulation and 73.3% success (22/30 trials) on the physical robot for sequential oversized-object lifting.

Jun Hu, Sihan Chen, Kosta Jovanovic +4

School of Advanced Engineering, Great Bay University, Dongguan, Guangdong, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China +2

Actuator Objects Soft

Sep 22, 2026cs.RO

SG-CPG: Severity-Gated Central Pattern Generators for Adaptive Quadruped Locomotion under Continuous Actuator Degradation

An animal with a weakened limb does not necessarily switch its gait, instead it unloads the affected limb, re-coordinates the remaining limbs, and scales its response with injury severity. This graded adaptation allows locomotion to persist despite partial loss of limb strength, rather than requiring a discrete transition between healthy and failed. Inspired by this behavior, we propose SG-CPG, a central pattern generator (CPG) for quadruped locomotion under continuous actuator degradation. SG-CPG preserves a frozen healthy CPG policy and introduces two severity-driven gates: a residual gate that re-coordinates all four legs and an amplitude gate that progressively shortens the weakened leg's stride as degradation increases. We emulate progressive degradation through two mechanisms: lowering the joint torque ceiling (ceiling mechanism) and scaling its low-level controller gains (gain mechanism), representing distinct forms of actuator weakening. Our simulations on a Unitree Go2 show that SG-CPG maintains a trot gait with 100% survival across an omnidirectional command schedule under 95% joint strength loss while tracking commands within 8%. Under a lowered torque ceiling, removing either severity path, the residual's severity observation or the amplitude gate, raises clipping at the weakened joint from 4.4% to 13.6% and 26.3% of steps at an 80% loss. On a real Go2, SG-CPG survives 28 of 29 forward and turning trials with up to 93% calf torque degradation. These results show that severity-gated adaptation can extend a healthy locomotion policy to progressive actuator degradation without treating the fault as a discrete failure.

Adarsh Kumar Kosta, Kaushik Roy

Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906, USA

Gait Dynamics Actuator Locomotion

Sep 20, 2026cs.RO

PINGU: Extending Air-Bearing Spacecraft Emulators with Open-Source Actuators and Learned Control for Contact-Rich Proximity Operations

Low-cost planar air-bearing testbeds have matured into a standard proxy for free-flying spacecraft GNC, but they remain largely thruster-only and are rarely equipped for contact-rich, inertia-coupled manipulation. Building on the open-source ATMOS testbed, we contribute a reaction wheel and two force/torque-sensed robotic arms (LEVION) with interchangeable end-effectors, integrated as first-class control actuators through a unified ROS 2 abstraction layer. On top of the software stack we build a reinforcement-learning training environment and digital twin, and a controller that exploits these added degrees of freedom, letting classical optimal controllers and learned policies be swapped on the same hardware without modification. We validate the integrated system, PINGU, across four benchmark tasks: point-to-pose navigation (classical LQR vs. sim-to-real PPO), dynamic disturbance rejection under arm-induced center-of-mass shifts, reaction-wheel momentum stabilization, and force-controlled docking. The results show that these additions extend an ATMOS-class emulator into the contact-rich regime and bridge classical optimal control and reinforcement learning on one reproducible platform.

Ricard Marsal I Castan, Akiyoshi Uchida, Aman Arora +7

University of Luxembourg · Tohoku University · Georgia Institute of Technology

Actuator Learning-Based Control

Sep 17, 2026cs.RO

Resilient Motion Planning for Free-Flying Space Robots under Actuator Failures

Free-flying robots rely on multiple thrusters to maneuver in space. If one or more of these thrusters fail, the robot may lose control authority and risk mission failure. At the same time, their free-flying nature implies that, even in the absence of actuation, they continue along (locally) straight-line trajectories. In this work we present a probabilistic, proactive, motion planning framework that explicitly accounts for actuator failures in space. We model actuator failure modes as a Markov chain and propagate the probability of successfully reaching the goal along the planning horizon. Precomputed reachable sets evaluate the robot's capabilities of reaching waypoints under potential failures and an RRT

^*

-based planner concatenates these waypoints. The resulting algorithm maximizes the overall target-reaching probability, providing maximally resilient motion plans utilizing free-flying properties. We validate our approach experimentally on a physical free-flyer platform with injected actuator failures.

Nicolas de Maddalena, Joris Verhagen, Jana Tumova

Institute for Dynamic Systems and Control, ETH Zürich, 8092 Zürich, ZH, Switzerland · Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden, and also affiliated with Digital Futures.

Motion Planning Bidirectional Thrust Actuator Robot Systems

Sep 8, 2026cs.RO

Actuator Dynamics Curricula for Narrow-Viability Tasks in Legged Robot Learning

Reinforcement learning has produced capable controllers across a broad range of legged-robot tasks, but a subset of these tasks fail to converge under standard training: those for which most exploration trajectories terminate before producing useful gradient signal. To address such tasks we introduce the \emph{Actuator Dynamics Curriculum}, a procedure that initializes joint stiffness at a high value and anneals it toward the system-identified value as completed episode lengths grow. Using a cart-pole system as a representative example, we show that higher closed-loop joint natural frequency under critical damping enlarges the viability kernel of the underlying Markov Decision Process, increasing the fraction of initial states from which the task is feasible. We validate the kernel monotonicity on the cart-pole and apply the curriculum to a quadrupedal-to-handstand transition on the Boston Dynamics Spot, a narrow-viability task where training under fixed identified stiffness plateaus at a policy that never completes the transition. The trained policy executes the transition in simulation across 10 seeds and transfers to hardware. More broadly, our results suggest that simulated actuator dynamics is a useful axis along which to design curricula for tasks in which exploration is bottlenecked by termination conditions rather than by reward signal.

Kousheek Chakraborty, Chandan K. Rajendra, Ayham Alharbat +1

Saxion University of Applied Sciences · University of Groningen · University of Twente

Actuator Agile Locomotion Closed-Loop Feedback Legged Robots Curriculum Robot Systems+1

Aug 7, 2026cs.RO

Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadrupeds, where increased mass and tighter actuation limits reduce the feasibility of aggressive, high-frequency compensation strategies often observed on smaller platforms. In this work, we propose a deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss. The method employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive observations. We introduce a latent-alignment loss that encourages consistency between actor and critic representations. Additionally, we augment the action space with a learnable gait frequency parameter, enabling adaptive gait timing in response to terrain variations and actuator degradation without predefined faulty-leg strategies. The approach is validated in high-fidelity simulation on uneven terrain and real-world experiments on flat ground using a 68 kg quadruped robot.

Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo +2

Humanoids and Human-Centered Mechatronics Research Line, Italian Institute of Technology, Genoa 16163, Italy

Legged Robots Gait Dynamics Actuator Gait Analysis

Aug 2, 2026cs.RO

Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots

Musculoskeletal robots require an internal body schema that remains consistent under a wide range of physical state changes, including abnormalities such as muscle rupture and actuator jamming. Conventional approaches based on autoencoders or variational autoencoders learn average behaviors by projecting sensor and actuator signals into a low-dimensional latent space; however, exploration within the latent space alone has limited capability to handle out-of-distribution or abnormal states that are not included in the training data. To address this limitation, this study proposes a diffusion-based framework for body schema learning in musculoskeletal robots. Unlike generative models that operate through low-dimensional latent spaces, diffusion models can directly and iteratively estimate physically consistent sensor and actuator values in the high-dimensional space through a denoising process, even under partial observations and constraints, without requiring retraining. By formulating body schema adaptation as a gradient-guided denoising process, the proposed method enables adaptive estimation of appropriate muscle lengths and muscle tensions even under abnormal conditions such as muscle rupture and actuator jamming. The validity of the proposed framework is verified through simulation experiments using a musculoskeletal robot model.

Kento Kawaharazuka, Shuhei Ikemoto

Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. · Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 7230, Hibikino 2-4, Wakamatsu, Kitakyushu, Fukuoka, 808-0135, Japan.

Scalable Robot Learning Biomechanics Diffusion Models Robot Systems Actuator Schema

Aug 1, 2026cs.RO

A Robotic System for Automated Manufacturing of Dielectric Elastomer Actuators

This letter presents an automated robotic manufacturing system for soft capacitors which operate as actuators and sensors. Emphasis is placed on the two processes that most directly govern device quality, dielectric layer formation by spin coating and carbon nanotube (CNT) electrode application by stamping. Twenty multilayer DEAs, each comprising 12 dielectric layers with a mean thickness of 55.37 +- 2.04 um and 11 alternating CNT electrodes, were fabricated reducing total process time by 14.2% and removing the operator from 56.1% of it.

Van Remenar, Anatol Gogoj, Ang Li +4

Soft Pneumatic Actuators Actuator Industrial Automation Robotic Systems

Jul 31, 2026cs.RO

Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies

Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their nonlinear dynamics. However, DRL methods often require extensive training time, and the resulting policies do not generalize well to different robot configurations or varying numbers of actuators. In this article, we introduce a novel DRL approach for controlling CDPRs that does not depend on the specific robot configuration. Our method trains an actuator-level policy that controls each motor to achieve its target cable length, in contrast to conventional DRL approaches that learn to control the entire robot to reach a desired end-effector position. To the best of our knowledge, this is the first work to apply DRL to control CDPRs using an actuator-level policy. This approach offers two main advantages: (i) a single shared policy can be applied to any CDPR configuration, regardless of actuator count, and (ii) reliance on inverse kinematics, avoiding the more challenging forward kinematics problem. Training is performed in simulation, and the learned policy is successfully transferred to a real CDPR. Experimental results show that the actuator-level policy (ALP) surpasses traditional reinforcement learning methods in both robustness and precision. We further control a real 8-motor CDPR with 3D motion using a policy trained on a simulated 4-motor planar CDPR operating in 2D. This illustrates that the proposed method is applicable to any CDPR configuration, independent of actuator number or placement.

Abir Bouaouda, Mohamed Boutayeb, François Charpillet +2

CRAN, UIR · Université de Lorraine, INRIA, CNRS, LORIA, Nancy, F-54000, France · International University of Rabat, Rabat, 11103, Morocco +5

Reinforcement Learning Control Robotic Manipulator Offline Reinforcement Learning Actuator

Jul 28, 2026cs.RO

Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics

Unmanned aerial vehicles (UAVs), particularly quadcopters, present unique challenges for autonomous control due to their underactuated dynamics: only four available control inputs must govern six degrees of freedom. This paper investigates a physics-aware, end-to-end deep reinforcement learning (DRL) approach that acts directly on low-level body inputs, total thrust and body torques

(T, τ_x, τ_y, τ_z)

, and closes the loop through a high-fidelity Simulink environment. Our simulator integrates a 12-state rigid-body model (MATLAB Level-2 S-Function) with (i) an Action2RPM allocation based on the Moore-Penrose pseudo-inverse of a coefficient matrix derived from thrust and drag terms, and (ii) first-order actuator dynamics for each motor (time constant

T_m = 0.076

s), including rotor gyroscopic coupling. A shaped reward balances goal-reaching and stability using an exponential position well, attitude penalties, and quadratic velocity costs. Four DRL algorithms, DDPG, TD3, PPO, and SAC, are evaluated in two stages: (S1) thrust-only hover and (S2) hover with pitch torque and a translated goal. Results show that SAC and TD3 achieve superior stability and exploration efficiency, while PPO is less sample-efficient. The study highlights the significance of modeling actuator lags and aerodynamic moments for stable low-level control and provides a reproducible benchmark for quadcopter DRL.

Ya-Chia Shen, Woei-Leong Chan

Department of Computer Science and Information Engineering, National Cheng Kung University · Department of Aeronautics and Astronautics, National Cheng Kung University

Quadrotor Underactuated Dynamics Reinforcement Learning Control Actuator Unmanned Aerial Vehicles

Jul 25, 2026eess.SY

Actuator-Aware Spatiotemporal Tube Synthesis for Temporal Reach-Avoid-Stay Tasks

This work proposes an actuator-aware spatiotemporal tube (STT) synthesis framework to accomplish temporal reach-avoid-stay (T-RAS) tasks for an unknown nonlinear multi-input and multi-output (MIMO) system under actuator constraints. Existing STT synthesis methods address actuator saturation after the tube generation either through repeated online re-optimization or controller redesign. Instead, the proposed framework incorporates actuator constraints directly into the tube synthesis process. The STT centerline and width are parameterized using Bernstein polynomial basis functions, whose convex-hull property enables sample-free enforcement of geometric and derivative constraints. By analyzing the worst-case closed-loop error dynamics of an approximation-free prescribed performance controller (PPC) used for STT tracking, we derive a linear actuator feasibility constraint. The constraints are embedded directly in terms of the tubes' Bernstein control points into the STT synthesis optimization for actuator-feasible tube generation, eliminating the need for online re-optimization or controller redesign. A simulation study on an omnidirectional mobile robot performing a T-RAS task shows that the proposed framework adheres to the prescribed actuator limits throughout the task and reduces required control effort by approximately

50\%

compared with an existing STT synthesis method.

Keshab Patra, K Madhava Krishna

Robotics Research Center, International Institute of Information Technology - Hyderabad Hyderabad, Telangana, India

Spatiotemporal Tube Actuator Curves

Jul 19, 2026cs.RO

SHAPE: Simultaneous Water Hydraulic Actuation and Position Estimation of a Sensorless Remote Actuator through a Thin and Long Flexible Tube

Robot sensors and electronic equipment are prone to failure in harsh environments. With water hydraulic drive, thin and long tubes enable remote operation without actuator-side sensors. Furthermore, the elasticity of the tubes reduces the impedance of the joints (actuators), benefiting robot tasks involving unexpected contact with the environment or vibrations. However, owing to the low impedance and limited camera visibility, accurately positioning the joint (or end effector) to the target location under varying load conditions is challenging. This study proposes a novel method that employs water-filled flexible tubes to enable the transmission of driving power and actuator-side information to and from the actuator, respectively, without actuator-side sensors. By modeling volumetric loss during transmission based on pressure fluctuations and incorporating minor air entrapment, simultaneous power transmission and position estimation is achieved through a tube up to 50 m. Thus, it becomes possible to use a feedback control framework that was previously difficult to implement in sensorless systems. Experimental validation confirms stable position control of a sensorless water hydraulic cylinder under varying loads. Furthermore, a field parameter-identification method accounts for tube and air entrainment variability without requiring actuator-side sensors. These contributions promote reliable remote control of robots in harsh environments.

Yuki Nakamura, Shuto Yoshimura, Tomoyuki Noda +1

Department of Mechanical and Intelligent Systems Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo, 182-8585, Japan · Chugai Technos Corporation, 4-3-1 Ishiuchikita, Saeki-ku, Hiroshima, 731-5109, Japan · Department of Brain Robot Interface, ATR Computational Neuroscience Laboratories, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan

Spatiotemporal Tube Actuator End-Effector Pose

Jul 14, 2026cs.RO

Design and Characterization of a Limb Encircling Actuator

Lower-limb powered exoskeletons have demonstrated substantial improvements in mobility and function, but most designs place actuation components lateral to the legs or remotely at the waist or back. These configurations often extend beyond the body's natural envelope, making devices intrusive in everyday use and potentially limiting societal adoption. We posit that rethinking actuation geometry could enable exoskeletons that conform more closely to the body. Here, we explored an actuation layout in which the actuator encircles the limb in a plane orthogonal to the limb axis, potentially reducing its spatial footprint around the body. We developed the Limb-Encircling Actuator (LEA) and characterized its electromechanical properties using a custom-built testbed. The LEA also features a novel radial bearing layout with potential as a lightweight or lower-cost alternative to traditional large-diameter bearings. The actuator achieved a continuous torque density of 7.5 Nm/kg with a mass of 894 g. Despite this high torque density and innovative layout, the system remained difficult to contain close to the body. These results highlight opportunities and challenges in limb-encircling actuation and provide insights into torque-dense exoskeleton designs that could integrate more readily into everyday apparel if challenges in actuator sizing and geometry are overcome.

Japmanjeet Singh Gill, Gray Cortright Thomas, Nikko Van Crey +2

Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA

Exoskeleton Actuator Wheel

Jul 13, 2026cs.RO

NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception

Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error. This is particularly acute on low-cost platforms, where the linear current-to-torque relation

τ= K_tI

becomes unreliable during commanded-target tracking because of friction, hysteresis, backlash, and thermal effects. We present NeuralActuator, a neural actuator model that jointly predicts (i) a simulator-equivalent generalized-effort surrogate for trajectory propagation on low-cost servo platforms, (ii) external force with a contact-probability gate for sensorless force perception, and (iii) a motor-condition score for the supervised joint. We also introduce the Neural Actuation Dataset (NAD), collected with a twin-arm teleoperation system that records robot states and actuator telemetry together with external-force labels. The torque-surrogate head is trained through differentiable simulation from pose trajectories without direct generalized-effort labels, while the force, gate, and motor-condition heads receive direct supervision. A Transformer captures temporal dependencies while supporting real-time inference. We evaluate NeuralActuator on a 5-DoF OpenManipulator-X, a 6-DoF SO-101, and a 7-DoF Franka Emika Panda, spanning three actuator families and platforms costing approximately USD 500 to over USD 30,000. The low-cost platforms support dynamics and force evaluation, while the offline Franka experiment provides an additional payload-force-estimation benchmark. Experiments further demonstrate its application for motor condition estimation on OpenManipulator-X and improved behavior-cloning performance when NeuralActuator is used as a pretrained module.

Zhiyang Dou, John U. Onyemelukwe, Hangxing Zhang +9

1MIT · 2Amazon Robotics

Actuator Inverse Dynamics Ground Reaction Forces Learning-Based Control Force Neural Dynamics+2

Jul 9, 2026eess.SY

Input-Constrained Spatiotemporal Tubes for Safe Navigation of Unknown Euler-Lagrange Systems in Dynamic Environments

Safe navigation in dynamic environments is challenging when system dynamics are unknown and actuator inputs are limited. Existing methods either rely on accurate models, require online optimization, or do not explicitly account for input constraints. This paper presents a real-time control framework for unknown Euler-Lagrange systems that guarantees finite-time reach-avoid-stay (FT-RAS) specifications while respecting actuator limits. We extend the spatiotemporal tube (STT) framework by incorporating input constraints into the controller design and derive offline-verifiable feasibility conditions that relate the available control authority to the tube design and uncertainty bounds. The resulting framework is approximation-free and computationally efficient, making it suitable for real-time implementation. The proposed approach is validated through simulations on a mobile robot, a quadrotor, and a spacecraft, together with hardware experiments on a mobile robot, demonstrating safe navigation while satisfying actuator constraints.

Siddhartha Upadhyay, Ratnangshu Das, Pushpak Jagtap

Department of Cyber-Physical Systems Indian Institute of Science, Bengaluru, India · Department of Cyber-Physical Systems Indian Institute of Science„ Bengaluru, India

Spatiotemporal Tube Actuator Safety Constraints Lagrangian Methods Safe Navigation Dynamic Environments

Jul 6, 2026cs.RO

Power from Potential: A Survey of Electrostatic Actuators for Haptics

As haptic interfaces integrate more seamlessly into wearables and everyday environments, they increasingly require actuators that are soft, thin, silent, and energy efficient. However, conventional motors and temperature-responsive polymers often struggle to deliver these properties due to their bulky form factors and high power consumption. High-Voltage Electrostatic Actuators (HVEAs), which generate force by applying an electric field to localized charge concentrations using high voltages and ultra-low currents, have recently emerged as a compelling alternative due to their fast, silent, and low-power operation within highly customizable and compliant form factors. This paper presents a focused review of HVEAs for haptics, examining four major classes: electrostatic switchable adhesives, dielectric elastomer actuators, soft electrohydraulic actuators, and electrokinetic pumps. For each class, we describe their mechanisms that enable haptic output; characterize their bandwidths, force densities, and spatial scalability; and evaluate their versatility for rendering cutaneous and kinesthetic feedback across wearable and world-grounded interfaces. Through this cross-technology analysis, we identify common design constraints and emerging strategies for improving ergonomics, streamlining fabrication, and integrating self-sensing. We conclude by outlining where HVEAs are uniquely positioned to advance haptic interaction and highlighting key research directions needed to translate these technologies into practical systems.

Ahad M. Rauf, Ran Zhou, Eric Acome +5

Department of Mechanical Engineering at Stanford University, Stanford, California, United States of America · Division of Media Technology and Interaction Design at the KTH Royal Institute of Technology, Stockholm, Sweden · Artimus Robotics, Boulder, Colorado, United States of America +4

Soft Pneumatic Actuators Haptic Feedback Actuator Wearable Devices

Jul 3, 2026cs.RO

Layout-independent actuation allocator for fin-actuated marine robots

In this study, we propose a layout-independent control allocator capable of zero-shot deployment across diverse actuator configurations. The proposed method utilizes a learning pipeline that integrates a Graph Neural Network (GNN) and a Transformer to represent the robot's geometric layout as a graph, along with a Mixture Density Network (MDN) to predict multi-modal control command distributions. Furthermore, by incorporating a differentiable physics surrogate model, we achieve command refinement during inference to minimize target wrench tracking error and energy consumption. A single generalized model using randomly generated actuator layout data demonstrated high trajectory tracking performance on different actuator layout robots outside the training distribution. Additionally, in real-world pool experiments, our approach achieved performance nearly equivalent to conventional controllers designed to specific layouts.

Yuya Hamamatsu, Maarja Kruusmaa, Asko Ristolainen

Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia

Actuator Robot Systems Graph Neural Networks

Jun 26, 2026cs.NE

Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation

Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems the actuator can rival or exceed an efficient controller in energy. An efficient controller is therefore necessary but not sufficient, because the actuator becomes the cost worth reducing once inference no longer dominates it. Here, we introduce energy-aware learning, an approach that incorporates actuator energy directly into the reinforcement learning reward, and demonstrate it in closed-loop deep brain stimulation (DBS) for Parkinson's disease. A deep spiking Q-network, trained in a biophysical cortico-basal ganglia-thalamic circuit model, learns to suppress pathological alpha-beta oscillations by 45.2% while reducing stimulation charge by 80.0% relative to continuous DBS. Sparsity-constrained knowledge distillation compresses the policy onto the SynSense XyloAudio 3 neuromorphic processor at 0.52 mW inference power, yielding 28.1x lower energy per inference than an equivalent artificial neural network on conventional edge hardware. By co-optimizing stimulation energy and inference efficiency, the framework addresses both major power demands in implantable neuromodulation.

Binh Nguyen, Colleen Josephson, Mircea Teodorescu +2

Dept. of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA · Dept. of Bioengineering, University of California, San Diego, La Jolla, CA, USA

Adaptive Deep Brain Stimulation Neuromorphic Computing Actuator

Jun 24, 2026cs.RO

Reliability-Asymmetric Spacecraft Autonomy: Co-Designing a Capable Learned GNC Stack with a Verified, Adaptation-Aware Runtime Shield

Deep-space missions need onboard autonomy that is both capable and certifiable. Rule-based autonomy is certifiable but brittle, while learned autonomy is capable but hard to verify. We present AMPLE-GNC, a three-tier guidance, navigation, and control stack. Its capability path combines a small foundation-model commander that maps natural language to PDDL+, a constraint-screening verifier, and a fault-adaptive controller. All three are bounded by a runtime shield with nine linear-temporal-logic invariants whose predictor soundness is machine-checked by the Kind 2 model checker. On a 6-DOF Basilisk testbed, we make three contributions. First, we deploy an edge commander. Fine-tuning a pretrained 360M model with grammar-constrained decoding gives a hard output-validity guarantee and 84% planner-executable actions. On a de-leaked test, novel-phrasing generalization is 38% exact and 51% action, rising to 48% exact after phrasing-diversity re-finetuning; we separate syntactic validity from semantic accuracy. Second, we introduce a fault-adaptive controller. Rapid Motor Adaptation infers latent actuator faults online and recovers 97.8% of actuator-sign faults and 94.4% of continuous-gain faults within the training randomization envelope. Fault-unaware PD and from-scratch end-to-end RL both score 0%, while the strongest classical-adaptive baseline reaches 55% on continuous gain. Beyond the envelope, a split-conformant retrain scores 57-67%, and adding 4x more in-regime data worsens performance, showing that randomization breadth, not data volume, drives generalization. Robustness is flat under star-tracker noise to 0.005. Third, we show that a latching safe-hold shield can suppress even a capable controller. A split-conformal recovery-deadline certificate with adaptation-aware engagement reconciles safety and recovery, keeping the controller 94.5% autonomous while still catching non-recovery.

Alireza Shojaei

Myers-Lawson School of Construction, Virginia Tech, Blacksburg, VA 24061 USA

Planetary Exploration Actuator

Jun 17, 2026cs.RO

Selective Unit-Cell Actuation in Lattice Structures for Distributed Morphology in Soft Robots

Soft lattice structures are increasingly used in robotics to tailor compliance and guide deformation; however, actuation is typically introduced at the device or module level, with actuators inserted into otherwise passive architectures. In this work, we move actuator-lattice co-design to the unit-cell scale. We present an embedded pneumatic unit cell that integrates curved-strut lattice geometry with a bidirectional bellow actuator within a single monolithic element. When tessellated, the lattice functions as a distributed actuation field in which global morphology is governed by spatial actuation patterns rather than uniform pressurization. Experimental characterization of 1x1, 2x2, and 3x3 tessellations demonstrates scalable displacement and force generation with repeatable cyclic performance. Selective actuation of unit cells in a 3x3x3 array produces distinct global deformation modes, including bending and directional grasping, without altering hardware configuration. Additionally, coupling active and passive unit cells enables bending-driven crawling locomotion, demonstrating that heterogeneous tessellations can translate through asymmetric deformation. These results establish unit-cell-level actuation as a strategy for distributed morphing in lattice-based soft robots and provide a foundation for scalable, monolithic robotic architectures.

Trevor Exley, Altair Coutinho, Lucia Beccai

Soft BioRobotics Perception Lab of the Istituto Italiano di Tecnologia (IIT), Genova 16163, Italy

Soft Robotics Actuator Lattice Distributed Optimization

Jun 16, 2026cs.RO

Damage Adaptation in Seconds for Architected Materials

Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds. In this work we proprioceptively adapt to catastrophic damage in soft-actuated systems in under one minute. Architected materials are well equipped for adaptation: actuator failure occurs gradually rather than acutely, and damage can be described in a low-dimensional, discrete coordinate space. Surprisingly, latent damage representations plus a simple yet robust ensemble method is sufficient for adapting to unseen damage in real-time. Moreover, we identify conditions under which exponential sample complexity collapses to linear sample complexity for learned representations of architected materials, a concrete advantage over rigid components or continuum soft mechanisms. We demonstrate LEAP, our method for adaptive proprioception, via a tracing task for a 6DoF soft wrist based on Handed Shearing Auxetic (HSA) actuators. Our algorithm is able to adapt to cuts, burns, and actuator repairs, enabling simulation-free real-time adaptation that is critical for realizing the promise of soft robots outside the lab. Videos and more information are available at https://murpheylab.github.io/leap.

James Avtges, Jake Ketchum, Helena Young +3

Northwestern University, Evanston, IL, USA

Soft Robotics Actuator Proprioception Materials Repair Future Latent Representations

Jun 15, 2026eess.SY

Distributed Safe Consensus Under Asymmetric Input and Time-Varying Output Constraints

This paper studies safe distributed consensus for single-integrator multi-agent systems over connected undirected graphs under simultaneous asymmetric actuator constraints and output safety constraints. Each agent is equipped with a continuously differentiable asymmetric actuator dynamics that maps a commanded control signal to the realized plant input while keeping the latter strictly inside a prescribed admissible interval. To address output safety, a barrier-coordinate transformation is introduced over a common time-varying safe interval, and a distributed synchronization law is designed in the transformed coordinates. The resulting controller integrates a graph-based coordination layer with an actuator-side tracking layer, thereby enabling simultaneous enforcement of input admissibility, forward invariance of the safe output set, and asymptotic synchronization. For compact admissible sets of initial conditions, it is shown that the closed-loop solution is complete, all signals remain bounded, the actuator inputs remain strictly within their asymmetric bounds, and the agent outputs remain inside the prescribed safe interval for all time. Moreover, the transformed synchronization errors converge exponentially to zero, and the original agent outputs asymptotically synchronize to a designer-selected admissible trajectory embedded in the common safe interval. Numerical simulations validate the proposed framework and demonstrate safe consensus under both asymmetric actuation bounds and time-varying output constraints.

Abhinav Sinha, Shashi Ranjan Kumar

Guidance, Autonomy, Learning, and Control for Intelligent Systems (GALACxIS) Lab, Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, OH 45221, USA · Intelligent Systems and Control (ISaC) Lab, Department of Aerospace Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India

Safety Constraints Actuator Model-Based Multi-Agent Systems Information Asymmetry

Jun 11, 2026eess.SY

An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts

Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques. When flight enters regimes where nonlinear effects dominate, linear allocators exhibit reduced accuracy due to increased model mismatch, which subsequently degrades performance and robustness of the flight control system. High fidelity onboard models and black box data driven approaches can recover accuracy across the flight envelope, but respectively impose computational burdens prohibitive for real time allocation and sacrifice the interpretability required for verification and fault diagnosis. This paper addresses these limitations by learning an explicit, physics constrained analytical model of the control effectiveness mapping from representative flight data using Sparse Identification of Nonlinear Dynamics. The resulting mapping is compact, interpretable, and admits analytical derivatives, enabling efficient computation within nonlinear solvers that additionally incorporate actuator dynamics, without requiring an onboard model. An online adaptation mechanism monitors prediction residuals and refreshes the model when significant plant changes are detected, providing graceful reconfiguration under actuator failures and varying operating conditions. The methodology is evaluated on a high fidelity nonlinear benchmark aircraft across a range of aggressive maneuvers, achieving accuracy comparable to a full nonlinear onboard model while substantially reducing computational cost relative to established baselines.

Umut Demir, Aamir Ahmad, Walter Fichter

University of Stuttgart, Faculty of Aerospace Engineering and Geodesy, Institute of Flight Mechanics and Control (iFR), Germany.

Nonlinear Dynamics Optimal Control Aerial Navigation Actuator

May 27, 2026cs.RO

Mag-VLA: Vision-Language-Action Model for Bimanual Magnetically Actuated Microrobot Manipulation

Magnetically actuated microrobots have been used as wireless, non-contact manipulation tools at microscales, making them promising for minimally invasive applications. However, their control remains challenging due to indirect actuation, limited sensing, and nonlinear magnetic interactions. In this work, we propose Mag-VLA, a vision-language-action (VLA) model for dexterous magnetic microrobot manipulation using two robotic arms with mounted magnets for dynamic magnetic-field construction. Bimanual coordination enables capabilities such as microrobot reorientation that are difficult or infeasible with a single arm, but it also introduces coupled control challenges, as the policy must generate coordinated trajectories for both actuators within a shared workspace. Our framework adapts a Qwen2.5-VL-7B backbone using Low-Rank Adaptation (LoRA) to process visual observations and language instructions for action prediction. To capture task progression, we introduce a motion-aware phase classifier and a phase-conditioned Action Chunking Transformer (ACT) decoder for temporally coherent multi-step control. We further construct a teleoperated magnetic microrobot manipulation dataset covering three task configurations. Ablation studies show that the ACT-based decoder substantially outperforms alternative generative action heads. In real-robot experiments, Mag-VLA achieves a 90% approach success rate across all tasks and transport success rates of 80%, 70%, and 50% as task difficulty increases. These results demonstrate that hierarchical VLA modeling provides a promising framework for magnetic microrobot manipulation.

Yongchen Wang, Kangyi Lu, Lan Wei +1

Department of Bioengineering, Imperial-X, Imperial College London

Robotic Manipulation Actuator

May 22, 2026cs.RO

Investigating the Effect of a Series Elastic Actuation Retrofit to Black-Box Actuators

In robotic applications, actuators are typically designed to be stiff with minimal backlash to ensure precision and repeatability. However, this limits compliance, leading to potential damage and poor force control in uncertain environments. Series Elastic Actuation (SEA) introduces compliance to enhance disturbance rejection and enable force measurement via Hooke's Law but reduces system bandwidth. A custom Series Elastic (SE) element was retrofitted to a black-box actuator to mitigate non-linearities like backlash and static friction. Integrating the SE element enabled high-fidelity force measurements, improving force control bandwidth and performance. A torsional SE element was designed through Finite Element (FE) analysis, yielding a stiffness of 2155.4 Nm/rad. Open-loop force control bandwidth was measured for the original motor and the SEA-integrated configuration, while closed-loop bandwidth was assessed using feedback from the SEA and a commercial force sensor. The SEA module increased bandwidth from 10.32 Hz to 30.32 Hz, a 2.93X improvement. Additionally, it outperformed the commercial sensor by 7.63% despite costing 25 GBP, a fraction of the price.

Ivan Tregear, Ayhan Aktas, Ferdinando Rodriguez y Baena

Imperial College London, Mechanical Engineering Department, UK · KAIKAKU.AI, London, N1 6NG, UK · Imperial College London, Mechanical Engineering Department, SW7 2AZ, UK

Soft Pneumatic Actuators Actuator Black Box Impact Analysis

May 20, 2026eess.SY

Fully Actuated Manifold Constraint Based Output Feedback Control for Input-Constrained Uncertain Nonlinear Systems

This paper presents a low-complexity, model-free, output-feedback controller for a class of unknown time-varying nonlinear systems with unknown input constraints. The controller achieves the preset control accuracy when the actuator is not saturated and maintains flexible control accuracy after actuator saturation. This result extends existing constraint control methods for linear manifolds to a more general form, including the construction of nonlinear manifolds and various types of constraints, thereby achieving preset control accuracy within finite or fixed time. Additionally, flexible control under unknown saturation is achieved through the construction of an error-driven flexible constraint. Finally, second-order and higher-order control examples and simulations are provided.

Dianrui Mu, Changchun Hua, Yafeng Li +2

Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China

Robust Control Inequality Constraints Actuator Nonlinear Dynamics Manifolds

May 15, 2026cs.RO

A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm

This paper presents a reproducible and physically feasible dynamic parameter identification framework for CRANE-X7, a low-cost robot arm driven by modular smart actuators. To improve practical identifiability, products of inertia are removed according to approximate link symmetry, reducing the rigid-body model from 65 to 39 base parameters. Identification motions are hand-designed from structured single-joint and adjacent-joint primitives under practical joint-range limits. The proposed pipeline combines preprocessing, inverse-dynamics-regressor-based ordinary least squares (OLS), conditional semidefinite-programming (SDP) projection for feasibility recovery, and closed-loop input error (CLIE) refinement. Candidate solutions from 40 structured trajectories are analyzed in a common principal component analysis (PCA) space to select a statistically central representative model. Because statistical centrality alone does not ensure physical acceptability, the selected model is finally screened by an all-pose positive-definiteness audit of the inertia matrix and, when necessary, corrected by a localized post-CLIE SDP rescue step. Experiments show that the parameter cloud becomes progressively more concentrated from OLS to SDP and CLIE, while the final accepted model preserves high predictive accuracy on held-out validation motions. These results demonstrate a practical route to statistically coherent and physically feasible dynamic models for low-cost robot platforms.

Junji Oaki, Koki Yamane, Koki Inami +1

Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

System Identification Rigid-Body Dynamics Soft Robotics Actuator Robot Systems

May 11, 2026cs.RO

VRA: Grounding Discrete-Time Joint Acceleration in Voltage-Constrained Actuation

Discrete-time joint acceleration constraints are widely used to enforce position and velocity limits. However, under voltage-constrained electric actuators, kinematically admissible accelerations may be physically unrealizable, exposing a missing execution-level abstraction. We propose Voltage-Realizable Acceleration (VRA), a joint-level acceleration interface that grounds kinematic acceleration in voltage-constrained actuator physics by restricting commanded accelerations to voltage-realizable constraints. Hardware experiments on electric actuators and a wheel-legged quadruped show that VRA removes unrealizable accelerations, restores consistent near-constraint execution, and reduces constraint-induced oscillations.

Lingwei Zhang, Jiaming Wang, Tianlin Zhang +5

Department of Mechanical and Automation Engineering; Hong Kong Embodied AI Lab, The Chinese University of Hong Kong

Actuator Acceleration Standard Traffic Speed Benchmarks Discrete Grounding

May 5, 2026cs.LG

Constraint-Enhanced Reinforcement Learning Based on Dynamic Decoupled Spherical Radial Squashing

When deploying reinforcement learning policies to physical robots, actuator rate constraints -- hard limits on how fast each joint can move per control step -- are unavoidable. These limits vary substantially across joints due to differences in motor inertia, power bandwidth, and transmission stiffness, creating pronounced heterogeneity that existing methods fail to handle geometrically: the per-joint feasible region forms a high-dimensional box in action-increment space, yet QP projection and spherical parameterization methods impose isotropic ball-shaped constraints, exponentially under-covering the true feasible set as heterogeneity grows. This paper proposes Dynamic Decoupled Spherical Radial Squashing (DD-SRad), which resolves this mismatch by computing a position-adaptive radius independently for each actuator, achieving tight alignment with the true per-joint feasible region. DD-SRad satisfies per-step hard constraints with probability~1, preserves well-conditioned gradients throughout training, and admits exact policy gradient backpropagation with zero runtime solver overhead. MuJoCo benchmark experiments demonstrate the highest task return at zero constraint violation -- matching the unconstrained upper bound -- with 30%--50% improvement in constraint-space coverage over spherical baselines. High-fidelity IsaacLab simulations with Unitree H1 and G1 humanoid robots confirm end-to-end optimality parameterized directly from official joint specifications, validating a systematic pathway from hardware datasheets to safe deployment.

Qijun Liao, Zhaoxin Yu, Jue Yang

School of Mechanical Engineering, University of Science and Technology Beijing · Institute of Automation, Chinese Academy of Sciences

Actuator Humanoid Robotics Convex Sets Sphere

Apr 24, 2026cs.RO

False Feasibility in Variable Impedance MPC for Legged Locomotion

Variable impedance model predictive control (MPC) formulations often treat joint stiffness as an instantaneous decision variable. The resulting feasible set strictly contains the physically realizable set under first-order actuator dynamics. We identify this as a formulation error rather than a modeling approximation, formalize the distinction between the parameter-based feasible set F_param and the realizable set F_real, and characterize the regime of mismatch via the dimensionless parameter α = ωsT (actuator bandwidth times task timescale). For the 1D hopping monoped, we prove that below an analytical threshold α_crit derived in closed form from task physics, no admissible stiffness command realizes the parameter-based prediction. Numerical validation in 1D shows monotonic deviation growth as α decreases, with the predicted scaling holding across ten parameter combinations (log-log R2 = 0.986). Mechanism transfer to planar spring-loaded inverted pendulum dynamics confirms center-of-mass and stance-timing deviation as the primary consequence, with regime-dependent friction effects as a tertiary observable. A second threshold α_infeas < α_crit establishes a floor below which restricting the admissible stiffness range cannot repair realizability, closing the conservative-tuning objection. Augmenting the prediction state with stiffness closes the mismatch by construction.

Vishal Ramesh

Impedance Control Model Predictive Control Actuator Inverse Dynamics Feasibility Conservative

Apr 17, 2026cs.RO

DTEA: A Dual-Topology Elastic Actuator Enabling Real-Time Switching Between Series and Parallel Compliance

Series and parallel elastic actuators offer complementary but mutually exclusive advantages, yet no existing actuator enables real-time transition between these topologies during operation. This paper presents a novel actuator design called the Dual-Topology Elastic Actuator (DTEA), which enables dynamic switching between SEA and PEA topologies during operation. A proof-of-concept prototype of the DTEA is developed to demonstrate the feasibility of the topology-switching mechanism. Experiments are conducted to evaluate the robustness and timing of the switching mechanism under operational conditions. The actuator successfully performed 324 topology-switching cycles under load without damage, demonstrating the robustness of the mechanism. The measured switching time between SEA and PEA modes is under 33.33 ms. Additional experiments are conducted to characterize the static stiffness and disturbance rejection performance in both SEA and PEA modes. Static stiffness tests show that the PEA mode is 1.53x stiffer than the SEA mode, with KSEA = 5.57 +/- 0.02 Nm/rad and KPEA = 8.54 +/- 0.02 Nm/rad. Disturbance rejection experiments show that the mean peak deflection in SEA mode is 2.26x larger than in PEA mode (5.2 deg vs. 2.3 deg), while the mean settling time is 3.45x longer (1380 ms vs. 400 ms). The observed behaviors are consistent with the known characteristics of conventional SEA and PEA actuators, validating the functionality of both modes in the DTEA actuator.

Vishal Ramesh, Aman Singh, Shishir Kolathaya

Department of Cyber-Physical Systems (CPS), Indian Institute of Science (IISc), Bengaluru, India

Soft Pneumatic Actuators Actuator

Apr 7, 2026cs.RO

A Co-Design Framework for High-Performance Jumping of a Five-Bar Monoped with Actuator Optimization

The performance of legged robots depends strongly on both mechanical design and control, motivating co-design approaches that jointly optimize these parameters. However, most existing co-design studies focus on link dimensions and transmission ratios while neglecting detailed actuator design, particularly motor and gearbox parameter optimization, and are largely limited to serial open-chain mechanisms. In this work, we present a co-design framework for a planar closed-chain five-bar monoped that jointly optimizes mechanical design, motor and gearbox parameters, and control parameters for dynamic jumping. The objective is to maximize jump distance while minimizing mechanical energy consumption. The framework employs a two-stage optimization approach, where actuator optimization generates a mapping from gear ratio to actuator mass, efficiency, and peak torque, which is then incorporated into CMA-ES-based co-design optimization of the robot design and control parameters. Simulation results demonstrate an improvement of approximately 30.4% in jump distance and an 11.5% reduction in mechanical energy consumption compared to a nominal design, highlighting the effectiveness of the proposed framework for high-performance and energy-efficient planar jumping.

Aastha Mishra, Aman Singh, Shishir Kolathaya

Department of Cyber-Physical Systems (CPS), Indian Institute of Science (IISc), Bengaluru, India.

Kinematic Chain Co-Design Actuator

Date pendingcs.RO

Design, modelling and experimental validation of single and multisloped bipenniform architecture-based shape memory alloy-driven linear actuator

The increasing industrial demand for alternative actuators over conventional electromagnetism-based systems having limited efficiency, bulky size, complex design due to in-built gear-train mechanisms, and high production and amortization costs necessitates the innovation in new actuator development. Integrating bio-inspired design principles into linear actuators could bring forth the next generation of adaptive and energy efficient smart material-based actuation systems. The present study amalgamates the advantages of bipenniform architecture, which generates high force in the given physiological region and a high power-to-weight ratio of shape memory alloy (SMA), into a novel bio-inspired SMA-based linear actuator. A mathematical model of a multi-layered bipenniform configuration-based SMA actuator was developed and validated experimentally. The current research also caters to the incorporation of failure mitigation strategies using design failure mode and effects analysis along with the experimental assessment of the performance of the developed actuator. The system has been benchmarked against an industry-developed stepper motor-driven actuator. It has shown promising results generating an actuation force of 257 N with 15 V input voltage, meeting the acceptable range for actuation operation. It further exhibits about 67% reduction in the weight of the drive mechanism, with 80% lesser component, 32% cost reduction, and 8% energy savings and similar envelope dimensions for assembly compatibility with dampers and louvers for easy onsite deployment. The study introduces SMA coil-based actuator as an advanced design that can be deployed for high force-high stroke applications. The bio-inspired SMA-based linear actuator has applications ranging from building automation controls to lightweight actuation systems for space robotics and medical prosthesis.

Kanhaiya Lal Chaurasiya, Ruchira Kumar Pradhan, Yashaswi Sinha +5

Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, 208016, India · Johnson Controls India Private Limited, Pune, Maharashtra, 411006, India

Soft Pneumatic Actuators Actuator Bio-Inspired Framework Shape-Memory Alloy