Robotic Control

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78 papers in the last four weeks, up 388% on the four weeks before. 0.8% of all new papers.

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Latest papers 367

Sep 20, 2026cs.RO

Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations

We consider robotic motion planning and control under unknown dynamics with hybrid state observations, where state measurements are available only in parts of the state space. Existing work combines system identification, predicted reachability, graph search and controller synthesis in a hierarchical framework using local affine approximated models over polytopic state space partitioning, but requires state observations for identification and feedback control. Based on this framework, we address blind regions by selecting nominal dynamics and precomputing open-loop control sequences before observation is lost. Since the true dynamics may differ from the selected nominal model, the robot may exit a blind polytope through an unintended facet. We quantify this transition risk and incorporate the possible outcomes into a stochastic transition system. The high-level planning problem is formulated as a stochastic shortest path problem, whose policy guides controller synthesis. A case study demonstrates that the method guides the robot from an initial state to a target while balancing route efficiency and the risks associated with traversing blind regions.

Zhiquan Zhang, Melkior Ornik

University of Illinois Urbana-Champaign, IL, 61801 USA

Robotic Control Safe Motion Planning Robot Motion Planning

Sep 20, 2026cs.RO

AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation

As AI agents become increasingly capable, agent-driven robotic control is emerging as a compelling paradigm. However, prevailing vision-language-action (VLA) models and world action models (WAMs) still rely on natural-language instructions to specify manipulation tasks, an ill-suited interface for agent-driven control: referentially ambiguous, spatially imprecise, redundant with the agent's inherent language understanding, and entangling intent with execution. We present AR-WAM, a visual-conditioned, agent-ready world action model that replaces language with two complementary conditions: a visual grounding prompt (a bounding box of the target) denoting the interaction object and location, and a learnable operation token dictating the atomic skill to execute. Our compact 0.5B-parameter model, with a frozen pretrained visual encoder and no language encoder, predicts scene evolution within compact latent states while decoding actions, exposing the policy's intent through explicit, supervisable reasoning signals. A model-agnostic compatibility layer provides three primitives (detect, execute, and query) so that local VLMs or online agent APIs can drive the policy directly, with long-horizon memory and closed-loop error recovery delegated to the agent side. On RoboTwin 2.0, RMBench, and a real Astribot S1 dual-arm platform, AR-WAM matches the strongest baselines on standard manipulation (87.2% average success) and outperforms them on memory-dependent and real-robot long-horizon tasks, improving success rates by 5.9% and 36.7%, respectively, while maintaining the lowest inference latency (14.1 ms).

Yicheng Jiang, Zesen Gan, Xiaobo Wang +8

The Hong Kong University of Science and Technology · Shenzhen University of Advanced Technology · Sangfor Technologies Inc. +4

Robotic Control Long-Horizon Robotic Manipulation Robotic Manipulation World Action Models

Sep 20, 2026cs.RO

Design and Control of a Cable-Driven Switchable Actuator with Torque/Tension Dual Modes for Exoskeletons

Existing wearable exoskeleton architectures are typically constrained by a single mechanical output modality, providing either joint torque around an anatomical joint or linear traction along a limb-training-oriented direction, which limits adaptability to diverse training scenarios. This letter presents a cable-driven switchable actuator (CDSA) that can rapidly switch between torque and tension modes while centralizing all sensing and actuation components at the proximal drive unit. A Coupled Movable Pulley Mechanism (CMPM) provides tension amplification at the distal end-effector, while a bidirectional Cable-Driven Ratchet Mechanism (CDRM) enables mode switching and preload regulation. To eliminate the need for distal instrumentation, multi-source proximal sensors are integrated with a data-driven fusion model to estimate distal output forces. An adaptive dual-mode force control strategy based on iterative learning control (ILC) is further developed. Platform experiments demonstrate transmission efficiencies of

(92.4 \pm 2.0)\%

and

(96.5 \pm 3.3)\%

in the torque and tension modes, respectively, along with a tension amplification ratio of

2.77 \pm 0.10

under tension mode. Tracking tests on simulated knee-joint gait trajectories and short-stroke tension profiles yield stable control, with RMSEs of

(4.52 \pm 0.51)\%

and

(3.15 \pm 0.19)\%

of the uncontrolled peak value, respectively. Finally, seated human-coupled experiments validate the system's controllable force generation in both joint-torque and linear-traction application modes.

YuanLong Ji, Xu Liu, Xinyuan Cai +8

School of Biological Science and Medical Engineering, Beihang University; the Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education; Key Laboratory of Innovation and Transformation of Advanced Medical Devices, Ministry of Industry and Information Technology; National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Interdiscipline of Medicine and Engineering), Beihang University, Beijing, 100191, China · School of Engineering Medicine, Beihang University, Beijing, 100191, China · School of Physics and Mechanical and Electrical Engineering, Longyan University, Longyan, 364012, China

Robotic Control Robotics Assistive Robotics Tendon-Driven Actuation Adaptive Control

Sep 20, 2026cs.RO

Towards Reliable Underwater Diver-Robot Interaction: Gesture Design, Interaction Logic, and Real-World Evaluation

Underwater human--robot interaction requires gesture commands that are both easy for divers to use and reliable for robots to recognize. We investigate these aspects through a closed-loop diver--robot interaction framework integrating a compact seven-gesture vocabulary, lightweight landmark-based recognition, and command-level interaction logic. We evaluate the framework through a user study and underwater robot experiments in a laboratory tank and a swimming pool. The user study supported the reproducibility of the gestures after brief learning. Recognition analysis further showed that visual similarity was associated with gesture confusion, while intermediate poses during gesture formation introduced temporal ambiguity. Command-level processing mitigated the effects of transient recognition errors on robot execution, reducing unintended triggers and premature task interruptions. These findings show that reliable underwater gesture interaction depends on human usability, gesture recognizability, and execution reliability in underwater interaction.

Yingqi Liu, Kanzhong Yao, Zimeng Peng +4

University of Science and Technology of China, Hefei, China. · Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai 200232, China.

Robotic Control Gesture Recognition Underwater Robotics Human-Robot Interaction

Sep 17, 2026cs.RO

Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control

World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the policy to learn physical dynamics that support effective control. However, recent tactile WAMs often rely on large-scale pretrained generative backbones to capture contact-rich physical dynamics, which limit their inference efficiency and flexible deployment. In this paper, we present \ABBR{}, an agile tactile World Action Model for contact-rich robot control. \ABBR{} encodes visual and tactile observations into a shared latent that serves as the source of a direct vision-tactile-to-action flow-matching process, which can jointly generate latent representations of action chunks and future visual/tactile latents. A key observation is that vision and tactile signals evolve at inherently different timescales: adjacent visual frames are often highly similar, whereas tactile signals can change abruptly upon contact. We therefore introduce multi-horizon multimodal prediction in \ABBR{}, which provides supervision for visual latent at a larger temporal offset while predicting the tactile latent in the next frame to capture fine-grained contact dynamics. Across nine simulated and five real-world contact-rich manipulation tasks, \ABBR{} demonstrates strong and robust performance, outperforming the strongest baseline in success rate while maintaining low inference latency. In particular, in five real-world experiments, \ABBR{} yields a relative gain of

\textbf{29.4\%}

in overall success rates while achieving inference latency of

\textbf{11.9 ms}

. These results demonstrate that multimodal WAM can be achieved with an agile architecture suitable for precise and high-frequency robot control. More details are available on our project page: https://hanchuzhou.github.io/TARO_project_page/.

Hanchu Zhou, Brendan Lynch, Raman Goyal +4

University of California, Davis, One Shields Avenue, Davis, CA, USA · Analog Devices · Analog Devices, San Jose, CA, USA

Robotic Control World Models for Robotics Contact-Rich Robotic Manipulation Visuo-Tactile Representation Learning World Action Models Tactile World Models

Sep 17, 2026cs.RO

Towards AI-enhanced control: a numerical technique for trajectory smoothing of a parallel robot for pancreatic surgery

The paper presents a numerical approach for the end-effector trajectory smoothing of a parallel robot designed for minimally invasive pancreatic surgery. The approach is tailored for real-time master-slave control architecture and uses a 3D space mouse for command input for velocity control. The trajectory smoothing is achieved by generating S-curves in the end-effector velocity fields, thus controlling the accelerations, which in turn reduces tissue trauma in the minimally invasive procedures. Real-time control is enabled by segmenting the S-curves based on the command inputs from the 3D space mouse. A special case is considered where the acceleration time is constant for all command inputs. Numeric results demonstrate stable transitions (without abrupt changes) in both the end-effector parameter space and in the active joints parameters, thereby validating the proposed approach. Further work aims to test the approach on an experimental model and integrate it into AI-based training modules.

Iosif Birlescu, Alexandru Pusca, Bogdan Gherman +4

LS2N - équipe RoMas, LS2N · CESTER, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania · École Centrale Nantes, Nantes Université, CNRS, LS2N, UMR 6004, F-44000 Nantes, France +2

Robotic Control Trajectory Optimization Robotics Surgical Robotics

Sep 17, 2026cs.RO

Compliance for Free: Learning Identifiable Impedance via Bilateral Teleoperation

Vision-language-action models tell a robot where to move, but not how hard to push. Contact-rich tasks depend on that second quantity, compliance, yet no widely used demonstration interface records it. The obstacle is identifiability as realized pose and measured force cannot separate the operator's intended equilibrium from their stiffness, so VR controllers, SpaceMouse and handheld grippers cannot supply compliance supervision even in principle. Prior compliance-output policies work around this with hand-specified task structure, privileged simulation contact state, or dedicated force and tactile hardware. Four-channel bilateral teleoperation removes the ambiguity directly by using the leader arm as a separate measurement of the intended equilibrium, making per-axis stiffness identifiable by regression using only the joint-torque sensing already on the manipulator. This yields per-timestep, direction-dependent compliance labels at zero annotation cost, which we use to fine-tune a VLA to emit stiffness alongside pose. On a Franka Research 3 wiping task, ours is the only policy of five whose contact force changes when the instruction asks for a firm wipe rather than a normal one (6.4N (normal) to 9.1N (firm) RMS, Cohen's d = 0.89, p = 0.023

Harsha Guda, Adrià Colomé, Carme Torras

Institut de Rob`otica i Inform`atica Industrial, CSIC-UPC Barcelona, Spain

Robotic Control Contact-Rich Robotic Manipulation Robot Teleoperation Force-Controlled Robotic Manipulation VLMs for Robotics

Sep 16, 2026cs.RO

Self-excited actuation enables adaptive and resilient flapping-wing flight

The muscles that power insect flight fall into one of two categories: 1) synchronous muscles that contract under direct control from the nervous system, and 2) asynchronous muscles which have an intrinsic stretch activation response that spontaneously generates wingbeats without the need for signaling from the brain. It is thought that the emergent nature of asynchronous wingbeats provides both adaptive and responsive capabilities for flight control. To date, most flying robots use synchronous actuation. In this paper we develop the first flight-capable flapping wing robot that uses asynchronous actuation. We demonstrate that asynchronous actuation allows wings to respond to changes in the resonant mechanics of the body without control input, and wings can react instantaneously to collisions with obstacles with no extrinsic sensing needed. Flight tests within cluttered environments demonstrate that asynchronous actuation significantly improves stability and performance when compared to synchronous actuation. In total this work demonstrates that a flapping wing robot actuation strategy that emulates the asynchronous muscles of flying insects can provide fast, reactive actuation responses before a control system would need to intervene. This partitioning of embodied control to both the low-level actuation dynamics and and high-level sensorimotor system provides a compelling blueprint for new flying robots.

Rundong Yang, Ethan S. Wold, Ellen Liu +5

Mechanical and Aerospace Engineering, University of California San Diego, San Diego, CA, 92161, USA · School of Biological Sciences and School of Physics, Georgia Institute of Technology, Atlanta, GA, 30332, USA · Mechanical Industrial Engineering, Montana State University, MT, 59717, USA

Robotic Control Aerial Robotics Robotics

Sep 16, 2026cs.RO

Winning a Won Game: Strict Reach-Avoid-Stay Control Barrier Functions for High-Dimensional Black-Box Systems

Robots must complete their tasks and maintain the achieved outcomes while avoiding safety failures at all times. Strict reach-avoid-stay (sRAS) formalizes this requirement: safely reaching a target and remaining there indefinitely after first entry. We propose an sRAS Q-control barrier function (CBF) safety filter for high-dimensional black-box systems under bounded uncertainty. Our construction combines a stay value encoding safe permanent residence in a target subset with a reach-avoid value encoding safe reachability of this subset while avoiding target states from which safe permanent residence cannot be guaranteed. We prove that these values jointly yield a valid robust discrete-time CBF and lift them to state-action Q-functions for runtime intervention. For exact values and under a measure-zero condition, our filter preserves sRAS feasibility from almost every winnable initial state and keeps the system safely within the target after first entry, against all admissible uncertainty realizations. We adopt reachability-based adversarial reinforcement learning for scalable value approximation using only black-box interactions. Notably, neither synthesis nor deployment of our filter requires known dynamics, affine structure, value derivatives, or hand-designed barriers. We validate our framework in quadruped gap jumping in simulation and hardware, where the robot crosses the gap, lands safely, and remains safe afterward. Simulated F1TENTH races further demonstrate safe overtaking and lead retention.

Donggeon David Oh, Duy P. Nguyen, Gongkai Yuan +3

Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA

Robotic Control Control Barrier Functions Robust Control Robot Safety Safety Filtering

Sep 16, 2026cs.RO

Design and Experimental Validation of a 3D Printed Torsional Series Elastic Actuator for Safe Human Robot Interaction

Ensuring intrinsic safety in physical human robot interaction (pHRI) is a critical requirement for social and service robots. While Series Elastic Actuators (SEAs) offer hardware based compliance, traditional metallic designs often require complex, multi part assemblies. This paper presents the design, finite element analysis (FEA), and experimental validation of a low stiffness, torsional spring for SEAs, manufactured via 3D printed thermoplastic polyurethane (TPU). The compliant element exhibits a highly linear torque deformation response (Ks = 0.066 Nm/degree), matching numerical predictions with under 3% deviation, a variance attributed to FDM structural anisotropy. To accommodate external interactions using standard position limited servomotors, a hybrid position controller with torque threshold switching was implemented. Experimental evaluations demonstrate the system ability to accurately track non stationary trajectories and safely yield to external disturbances. Furthermore, the inherent material damping of the TPU acts as a passive low pass filter, preventing high frequency oscillations during control mode transitions. The proposed architecture offers a cost effective, reliable, and easily manufacturable solution for safe pHRI.

Joel Hidalgo Pisco, Melissa Cobos Condo, Luigi Miranda +1

ESPOL Polytechnic University Guayaquil, Ecuador

Robotic Control Robotics Human-Robot Interaction

Sep 16, 2026cs.RO

ElastiQP: An Always-Feasible QP Solver for Constrained Robot Control

As robot capabilities increase, quadratic programming (QP)-based controllers must account for a similarly increasing number of constraints to ensure safe, reliable operation. Yet, with each added constraint, this introduces more chances of momentary conflict: in which case, a QP solver that returns an "infeasible" status leaves the controller with nothing to execute. To address this, we introduce ElastiQP, a modified dual active-set QP solver that relaxes every inequality constraint with an exact, per-constraint l1 penalty while keeping equality constraints (dynamics) hard. Notably, ElastiQP does so by folding the slack variables into the solver analytically, maintaining a constant size of the condensed linear system. On a suite of robot control benchmarks, ElastiQP achieves microsecond-level performance, matching or outperforming leading modern solvers on feasible problems. On infeasible problems, ElastiQP handles these gracefully, confining violations to strictly the conflicting inequality terms, returning a usable solution up to 40x faster than the best alternative solvers. ElastiQP is available as an open-source C++ header-only library, with Python and JAX interfaces, at https://github.com/StanfordASL/elastiqp.

Daniel Morton, Jon Arrizabalaga, Zachary Manchester +1

Departments of Mechanical Engineering and Aeronautics & Astronautics, Stanford University, USA · Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, USA

Robotic Control Constrained Optimization Quadratic Programming

Sep 16, 2026cs.RO

KINO: A Keyframe Interface for VLM Planning and Whole-Body Control in Humanoid Loco-Manipulation

Humanoid loco-manipulation requires robots to interpret task instructions and scene semantics while executing coordinated whole-body motions. We propose a hierarchical framework that uses motion keyframes as an intermediate representation between Vision-Language Model (VLM) planning and Reinforcement Learning (RL) control. Each keyframe specifies a target whole-body robot pose and, when applicable, an object pose. Given a language instruction, scene observations, and execution feedback, the VLM selects successive task-relevant keyframes from a predefined library. The selected keyframes are retargeted to the current scene to account for object poses and dimensions. A keyframe-conditioned whole-body policy then generates joint-level actions to reach these goals. We introduce a saliency-based keyframe sampling strategy for low-level policy training that improves end-to-end task success rate from 44% to 92% when using sparse VLM keyframes. We evaluate our framework on object pickup, transport, and placement tasks in simulation and on a Unitree G1 humanoid. The system successfully performs both one- and two-handed manipulation and generalises to placement locations beyond the training reference data.

Sitong Chen, Fatemeh Zargarbashi, Jin Cheng +2

ETH Zürich, Switzerland

Robotic Control RL for Robotics Humanoid Loco-Manipulation Language-Conditioned Robot Manipulation VLMs for Robotics

Sep 16, 2026cs.RO

Feedback-Modulated Harmonic Policies for Quadruped Locomotion

Learned quadruped locomotion policies commonly map observations directly to joint-level actions, leaving the periodic structure of locomotion implicit in the policy. We investigate an alternative representation in which each joint trajectory is expressed as a command-conditioned Fourier series and modified online using feedback from the robot state. A context network generates the Fourier coefficients and the weights of a per-step feedback network, whose outputs adjust joint offsets, harmonic gains, frequency, and phase during execution. In simulation, we examine this explicit frequency structure alongside the hidden activations of an MLP policy that directly outputs joint targets. The harmonic waveforms change frequency and shape with commanded speed. Dynamic mode decomposition of selected MLP rollouts reveals dominant activation modes near the foot-height oscillation frequency and its second harmonic, showing periodic structure without an explicit Fourier generator. On a Unitree Go2, the simulation-trained harmonic controller records a provisional onboard-estimated peak speed of 3.67 meter per second and carries added loads up to 5.883 kilogram in separate trials.

Yixuan Jia, Steven Roche, Jonathan P. How

Massachusetts Institute of Technology, Cambridge, MA 02319 USA

Robotic Control Robot Policy Learning Robot Motion Generation Quadruped Locomotion

Sep 15, 2026cs.RO

Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control

We study cooperative multi-humanoid pickup and transport of objects with varying size, weight, and geometry, requiring robot teams of different sizes. Our approach uses decentralized object-centric control, where each humanoid is assigned a local attachment region on the shared object and learns to realize pickup and transport through gripperless bimanual pinching. This attachment-based interface provides a common control abstraction spanning single-robot pickup, cooperative multi-robot transport, and robot-to-robot handover, without per-task redesign. We find that policies trained only on single-robot pickup already transfer nontrivially to cooperative settings, suggesting that this abstraction captures much of the structure needed for coordination. At the same time, explicit multi-robot training further improves performance, showing that shared-object coupling introduces coordination dynamics that are beneficial to learn directly. We validate the approach in simulation across varying team sizes and object geometries, and demonstrate sim-to-real transfer on hardware, where the learned controllers enable real humanoids to perform cooperative manipulation tasks.

Bikram Pandit, Mohitvishnu S. Gadde, Aayam Kumar Shrestha +1

Dynamics Robotics and AI Lab (DRAIL), Oregon State University, Corvallis, OR, USA

Robotic Control Multi-Robot Systems Bimanual Robotic Manipulation Humanoid Robot Control Multi-Robot Coordination

Sep 15, 2026cs.RO

Hamilton-Jacobi Reachability for Hybrid Systems: Unified Goal-Driven Control with Safety Guarantees

Hybrid dynamical systems provide a powerful modeling framework for robotic systems, particularly in contact-rich environments. However, ensuring safety and performance in such systems remains challenging due to the intricate coupling between continuous dynamics and discrete mode transitions. In this work, we extend classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous-time nonlinear systems, to hybrid dynamical systems. Our framework characterizes safe sets for hybrid systems through a generalized value function defined over both discrete and continuous states while accounting for control constraints and model uncertainty. We additionally provide a numerical algorithm to compute this value function. Building on these safe sets, we propose two different mechanisms to integrate performance objectives. First, we introduce a hybrid least-restrictive safety filter that intervenes on both the discrete and continuous components of a nominal controller only when necessary to avoid unsafe states, thereby preserving nominal behavior whenever possible. Second, we formulate and compute hybrid backward reach-avoid tubes, enabling the simultaneous enforcement of safety and goal-reaching behavior, an extension not previously addressed within hybrid HJ reachability. This enables the synthesis of continuous and discrete control policies that guarantee both safety and task completion. We validate our framework through simulation studies and real-world experiments on a quadrupedal robot, demonstrating its effectiveness in hybrid mode planning and safety-critical applications.

Javier Borquez, Shuang Peng, Somil Bansal

Universidad de Santiago de Chile · University of Southern California · Stanford University

Robotic Control Reachability Analysis Safety-Critical Control

Sep 15, 2026cs.RO

Optimized Wrench Polytope Analysis for Real-Time Stability Control of Legged Robots in Complex Multi-Contact Configurations

Legged robots offer a variety of automation applications in real-world scenarios. But areas that are difficult to traverse, like slopes, caves, or scaffolding, still pose a great challenge for traversal. To tackle this problem, we propose an optimized algorithm for evaluating the full actuatable wrench polytope for arbitrary contact scenarios. With our improved analysis algorithm, the torques for each joint of the robot can be calculated within a control frequency of 49 Hz. The achieved speedup allows for deployment within a regular control loop for actuating robot poses for different contact scenarios. We evaluated our stability controller extensively in simulation scenarios and validated its applicability by deploying it on actual walking robot hardware. The proposed controller achieved stability in very complex scenarios that are currently not achievable by any other controller.

Friedrich Graaf, Elias Birkefeld, Christian Eichmann +5

FZI Research Center for Information Technology, Karlsruhe, Germany · Machine Intelligence and Robotics Lab (MaiRo), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

Robotic Control Legged Robotics Robot Locomotion

Sep 15, 2026cs.RO

Swim-and-Breach at Palm Scale: A Rudder-Steered Two-Propeller Underwater Robot Platform with Differential-Thrust Pitch Control

We present a palm-scale (65 mm, 34 g) swim-and-breach robot platform. Two vertically stacked propellers provide both propulsion and differential-thrust pitch control under a proportional-integral-derivative (PID) loop, and a tail rudder adds yaw control. The hull, evaluated by flow simulation, reduces the drag five-fold relative to an equivalent cuboid, and the propellers are optimized using B-series modeling validated by dynamometer measurements. The current robot swims at 13.9 body lengths per second and turns at 209 deg per second, corresponding to the upper limits reported for underwater robots. In free swimming, the pitch loop turns the body to any commanded nose-up pitch angle, and, with the rudder stabilizing the exit, the current robot leaps 1.6 body lengths high and 3.7 long in a seamless cruise-leap-cruise sequence. The platform can be used to build small-scale robots that cross barriers and dry gaps between pools for inspection in streams, flooded structures, and industrial systems.

Daehyun Choi, Ian Bergerson, Hengjia Zhu +2

BioFrontiers Institute, University of Colorado Boulder, Boulder, CO 80309, USA

Robotic Control Underwater Robotics

Sep 15, 2026cs.CV

sensVLA: Spatially-Grounded Vision-Language-Action Model for Autonomous Wheel Loader

Autonomous wheel-loader control requires joint reasoning over task semantics, egocentric vision, proprioception, and 3D scene geometry. We present sensVLA, a Vision-Language-Action (VLA) architecture that combines a Qwen3-2B Vision-Language Model (VLM) with a fully trainable transformer action expert trained by flow-matching velocity regression. sensVLA routes Bird's-Eye-View (BEV) features, extracted from fused front and rear lidar, directly to the action expert through a dedicated cross-attention pathway, while the VLM consumes front and rear RGB views to provide task-conditioned semantic context. This design decouples spatial grounding from linguistic reasoning while preserving interaction between both streams at decision time. The expert predicts six action dimensions: longitudinal velocity, steering, body-frame displacement, arm rate, and bucket rate. On a real-world dataset from a wheel loader, sensVLA reaches aggregate per-step parity with a strong camera-only baseline and reduces longitudinal velocity RMSE by 28% and displacement error by 9% on loading centric scenarios. It also degrades 29% less when the camera stream is corrupted or removed, evidencing that explicit spatial grounding improves accuracy and fault-tolerance for heavy equipment autonomy.

Gopi Krishna Erabati, Bjarne Johannsen, Angus Stewart +1

sensmore GmbH Berlin, Germany

Robotic Control Vision-Language Grounding Vision-Language-Action Models

Sep 15, 2026cs.RO

IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator

Autonomous excavator control is challenged by coupled kinematics, actuation lag, and uncertainty. We propose imitation learning and adaptive Cartesian tracking (IL-ACT), a novel motion control framework for a 30-ton-class excavator. An anchored, 14-input imitation policy pretrained on operator demonstrations generates nominal joint rates; adaptive Cartesian feedback and gated gain/bias estimation correct these commands before a stopping-distance governor constrains joint-reference generation. Simscape evaluation covers 100 sequential goals and spiral, figure-eight, and rounded-raster tracking, including 88 additional runs across three training seeds, two initializations, and speeds, under hydraulic response and sensing conditions. Compared with Teacher+ACT, IL-ACT completes all goals with shorter duration and lower terminal errors under both response conditions. Telemetry-initialized IL-ACT lowers RMSE in all 24 figure-eight and rounded-raster seed comparisons and lowers additional-load spiral mean RMSE by approximately 29%. Original spiral RMSE also improves over IL-only and PID. Under a shared sensor-noise realization, telemetry-initialized IL-ACT achieves 27.67% lower mean RMSE than Teacher+ACT; enabling estimation reduces mean RMSE by

22.44\%

relative to the frozen estimator. Pretrained-weight effects remain mixed, and the original teacher comparison exhibits a spiral RMSE--maximum-error tradeoff. Analysis establishes bounded adaptive states and Cartesian feedback, with reference admissibility conditional on governor feasibility.

Mehdi Heydari Shahna, Seihun Kim, Soyi Jung +3

Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland · Department of Defense Convergence Technology, Korea University, Seoul, Republic of Korea · Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea +2

Robotic Control Robot Trajectory Tracking Robot Imitation Learning Adaptive Control

Sep 14, 2026cs.RO

Auto-HSI: Personalized human control of a robot swarm on demand by using LLMs for online automatic code generation

This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untrained operators to use natural language descriptions and gesture demonstrations to explain how they want the robots to collectively behave in response to their gestures. Based on these inputs, the code should automatically be generated for personalized state machines that will control the robots as desired, in response to the desired gesture inputs. In the developed Auto-HSI prototype, the generated code produces a personalized interface for centralized control using one- and two-handed gestures, enabling a user to teleoperate the robots' motion, formation shape, and shape deformation. We test the gesture tracking and code generation components of Auto-HSI against performance benchmarks. We then test the full Auto-HSI prototype in ``live'' operation experiments, in which real human operators centrally control 50 simulated robots in a physics-based simulator, under nominal and noisy conditions. In these experiments, robots are teleoperated to: score a goal, traverse a maze that requires shape deformation, and score two simultaneous goals by splitting into two groups. We also demonstrate a real human operator making live updates to their personalized Auto-HSI interface during operation (in simulation). Finally, we demonstrate live operation of real robots.

Alessandro Nazzari, Nathan Cerisara, Dorian Tonnis +5

IRIDIA, Universit´e Libre de Bruxelles, Brussels, Belgium · Department of Aerospace Science and Technology, Politecnico di Milano, Milan, Italy · T´el´ecom Physique Strasbourg, Universit´e de Strasbourg, France +2

Robotic Control Swarm Robotics Robot Teleoperation

Sep 14, 2026cs.RO

A Physics-Based Closed-Loop Robotic Bioprinting Framework Towards Volumetric Muscle Loss Treatment

Robotic bioprinting and Direct Ink Writing (DIW) are being explored towards the treatment of Volumetric Muscle Loss (VML). While previous studies have shown the importance of proper parameter selection on the print outcome, existing approaches often rely on time- and material-intensive design of experiments methods, or require large, well-curated datasets for training machine learning models. In this paper, we propose a physics-based closed-loop robotic bioprinting system capable of near real-time parameter adaptation. The system integrates a 3D point cloud camera and fully autonomous vision-based algorithms to provide quantitative evaluation of printed constructs. This evaluation is fed into a controller that adjusts printing parameters to achieve a desired bead thickness. To assess the framework's performance, four experimental configurations were tested, each repeated three times. In these tests, printing began from an arbitrary initial parameter value, and the controller was tasked with adjusting the parameters to reach the desired thickness. The system converged in all trials, achieving a tracking error below 0.5 mm within an average of 5.2 seconds from the start of printing. The low standard deviation of the converged pressure over different tests (0.04 bar on average) demonstrates robustness and repeatability. Additional experiments were conducted with the controller turned off, enabling direct comparison with open-loop DIW bioprinting, further confirming the effectiveness of the proposed closed-loop framework in achieving the desired bead geometry.

Omid Rezayof, Jerin T. Andrews, Ehsan Zobeidi +5

Walker Department of Mechanical Engineering, University of Texas at Austin, TX, USA · Chandra Department of Electrical and Computer Engineering, University of Texas at Austin, TX, USA · Terasaki Institute for Biomedical Innovation, Los Angeles, CA, USA

Robotic Control Feedback Control Vision-Based Robot Control

Sep 14, 2026cs.RO

Dynin-Robotics: Omnimodal Unified Diffusion Vision-Language-Action Model

Visual goal and dynamics prediction can provide language-conditioned robot policies with both a target outcome and a representation of action-dependent scene changes. We bring these predictions into action generation and selection through a shared trajectory model. Dynin-Robotics implements this formulation on Dynin-Omni, an omnimodal masked-diffusion backbone, representing language, visual observations, goals, and actions as discrete tokens. By varying conditioning and target spans, the same model learns action prediction, action-conditioned next-observation prediction, terminal goal-state prediction, and trajectory-to-instruction reconstruction. These interfaces support test-time scaling through goal prediction, action-candidate evaluation, and joint refinement of action and future-state predictions. We continually pretrain the model on approximately 1.33 million trajectories from 48 Open X-Embodiment datasets and adapt it separately to downstream domains. On two VLABench tasks, robot pretraining improves adaptation within a fixed Stage-2 step budget, and the full objective mixture improves shifted-instruction success over Policy-only post-training under the same coupled decoder. Combining goal guidance with joint action-next-state denoising further improves shifted-instruction success over action-only decoding; the benefit depends on how the predictions are composed. Dynin-Robotics achieves competitive performance on LIBERO and zero-shot LIBERO-Plus, together with a 78.4% average success rate across four manipulation conditions on a Franka Research 3 robot. An optimized block-parallel implementation accelerates model-side action decoding by up to 29.2x relative to the base implementation under the reported profiling setup. These results support shared trajectory modeling as a common interface for learning complementary robot objectives and composing their predictions during control.

Hoeun Lee, Jaeik Kim, Jusang Oh +4

AIDAS Lab Seoul National University

Robotic Control Action-Conditioned Video Generation Language-Conditioned Robot Manipulation Vision-Language-Action Models Trajectory Representation Learning

Sep 11, 2026cs.CL

Agent as Policy for Robotic Manipulation

We demonstrate that a general-purpose agent can directly drive a physical robot throughout task execution without any task-specific or environment-specific training. We introduce Agent as Policy (AGP), which places task planning and execution under the agent's control. Given a task and a robot interface, the agent interprets visual evidence, writes executable programs, issues motion commands, and revises its actions in response to physical outcomes. This brings the agent's reasoning and programming capabilities into continuous interaction with the physical world. We study AGP across multiple real-world manipulation tasks spanning precision manipulation, dynamic motions, and deformable objects. These include assembly from human videos, block construction from goal images, dice flipping, targeted throwing, and bimanual towel folding. Across assembly, block construction, and dice flipping, AGP succeeds in at least eight of ten trials for each evaluated task configuration. We further study efficiency through task experience accumulation and find that reusing saved procedures and programs shortens execution time across repeated trials. These findings support a path for general-purpose agents to act as robot policies, extending their autonomy to physical manipulation through runtime reasoning, programming, and interaction.

Mengzhao Jia, Yang Lin, Xixin Zhang +3

University of Notre Dame · University of California San Diego · San Diego State University

Robotic Control Robot Policy Learning Generalization in Robotic Manipulation Robotic Manipulation Robot Task Planning

Sep 11, 2026cs.RO

Expressive Robotic Pianist: Mastering Complex Piano Repertoire with Graph-Mimic and Musical Dynamics

Enabling robots to perform musical instruments with human-level expressivity represents a frontier in bridging the gap between mechanical precision and artistic interpretation. Despite advances in robotic dexterity, replicating the fluid finger transitions and nuanced dynamic control characteristic of human pianists remains a significant challenge. Through a reinforcement learning-based control framework, we demonstrate that a dexterous robotic hand can achieve high-fidelity performance across a diverse piano repertoire. Central to our approach is a graph-based optimization strategy that guides the robot to generate natural pre-press and key-press fingering strategies that closely resemble human movement patterns. To achieve expressive sound production, the control system is coupled with a physics-inspired acoustic model that modulates keypress velocity to accurately reproduce the dynamic variations specified in musical scores. Quantitative evaluations demonstrate that our expressive control model significantly outperforms baseline methods in both finger morphology similarity and dynamic velocity accuracy. In a perceptual test involving participants from diverse listener groups, performances generated by our system are significantly preferred over baseline robotic performances and are indistinguishable from human performances for non-professional audiences. Furthermore, extensive experiments across multiple musical styles confirm that our method maintains high note-level accuracy while achieving expressive performance. Our approach provides a robust pathway for robotic systems to move beyond mere mechanical accuracy, elevating robotic musicianship to a level of expressive performance comparable to human pianists.

Yanhong Liang, Xianwei Liu, Chaojie Fu +7

the Center for X-Mechanics, Zhejiang University, Hangzhou & 310027, China. · ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou & 311200, China. · Department of Public Physical and Art Education, Zhejiang University, Hangzhou & 310058, China.

Robotic Control Robotic RL Robotic Manipulation

Sep 11, 2026cs.RO

Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

Muscle-driven locomotion provides a physically grounded approach to generating realistic human movement. However, achieving both physiological plausibility and adaptability to changes in musculoskeletal capacity and external disturbances remains a fundamental challenge. To address this limitation, we propose a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. Within this framework, a fixed phase-dependent reflex controller serves as the underlying neuromuscular control mechanism, while the reinforcement learning policy produces four biomechanically meaningful residual parameters to modulate key reflex gains and thresholds associated with hip swing, knee support, and ankle propulsion according to the current state. Experimental results demonstrate that the proposed framework generates physiologically plausible locomotion with improved kinematic accuracy and dynamic consistency, as well as better bilateral symmetry and stride-to-stride consistency under nominal walking conditions. The learned policy remains robust under muscle weakness and external perturbations without retraining.

Jian Zhou, Xingyu Zhang, Rui Ma +3

University of Leeds, Leeds, United Kingdom

Robotic Control RL Control Robust RL

Sep 11, 2026cs.RO

Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation

Dexterous in-hand manipulation of a grasped object with an anthropomorphic hand is an unsolved frontier for robot dexterity. The contact-richness and highly dynamic nature of object-hand interactions tend to require extensive modeling or data-collection efforts for learning-based approaches. Modern simulators used for reinforcement learning (RL) cannot fully replicate the required contact complexity, while collecting dexterous demonstrations for imitation learning (IL) remains an open problem. In this research, we present an embodied control approach based on real-time task Jacobian estimation of the combined hand and object system on the physical robot. Using only the CPU on a laptop, the proposed controller begins in-hand pen writing after approximately 18 s of initialization and continues to adapt online, without an analytic hand--object kinematic/contact model, simulation training, or precollected task demonstrations. We demonstrate that the same estimator/controller formulation works on three anthropomorphic robotic hand systems (one physical, two simulated) to show human-like, in-hand articulation of a grasped pen by an embodiment-independent formulation. Sub-millimeter in-plane precision (mean 0.6 mm across runs) is achieved across letters and shapes written in the air and on paper on a physical robot. To our knowledge, this is the first demonstration of an anthropomorphic hand writing arbitrary single-stroke trajectories with a grasped pen through purely in-hand motion, and it showcases an alternative to compute- and data-heavy approaches such as RL and IL for achieving dexterous manipulation through computationally simple and data-efficient algorithms.

Kai Stewart, Yasunori Toshimitsu, Robert K. Katzschmann

Soft Robotics Lab, D-MAVT, ETH Zurich, 8092 Zurich, Switzerland

Robotic Control In-Hand Manipulation Contact-Rich Robotic Manipulation

Sep 10, 2026cs.RO

ActSafeGuard: Differentiable and Training-Aligned Constraint Enforcement for Flow-Matching Policies

Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their generated actions may violate hard physical constraints and therefore be unsafe or infeasible for deployment. Existing safety approaches either optimize statistical safety objectives without deterministic per-step guarantees or correct unsafe actions only during inference, creating a mismatch between policy training and execution. We introduce ActSafeGuard, a differentiable and training-aligned safeguard layer for flow-matching based policies. ActSafeGuard integrates hard action feasibility into policy learning, not merely treating safety as an inference-time external component. Through an analytical ray-scaling operator design, ActSafeGuard enables boundary-aware gradients to guide the model to naturally learn constrained manifolds. Extensive experiments on multiple standard foundation backbones (

π_{0.5}

and Fast-WAM) across various tasks demonstrate that ActSafeGuard consistently achieves a

100\%

step safety rate while fully preserving or even boosting task success rates, providing a scalable and minimally invasive solution for safe embodied AI deployment.

Jianming Ma, Rongjun Jin, Xiaxi Si +3

1Shanghai Jiao Tong University · 2Shanghai Innovation Institute

Robotic Control Robot Foundation Models Robot Safety Vision-Language-Action Models

Sep 10, 2026cs.RO

Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model

We study how physical-state inputs affect a 0.8B hybrid language model adapted for manipulation with 6.2M trainable parameters. Six conditions are trained on three LIBERO-Spatial tasks and evaluated over three seeds and 540 held-out rollouts. Conditioning recurrent decay gates on geometric increments yields 28.9% success, compared with 36.7% when those increments are shuffled during training and 24.4% without explicit object/goal geometry. Both geometry policies receive correct inputs at evaluation. A token adapter using the same increments scores 27.8%; differences vary across seeds and remain inconclusive. Token-clock conditioning scores 11.1%, including one seed that fails to converge. In separate robustness tests, a state-only relative-coordinate policy retains 7/10 success under frame relabeling, whereas all four tested visual policies fall to at most 3/20 after a 5 cm object displacement. These results show no reliable advantage from training-time geometric alignment under this recipe and illustrate the gap between coordinate invariance and physical-layout generalization. Episode records, seed-level analyses, and figure-generation code accompany the paper.

Hao Li, Haofei Sun, Lin He

University of Tennessee, Knoxville

Robotic Control Robotic Manipulation Robustness of VLA Models

Sep 8, 2026cs.RO

A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects

In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. This article offers a comprehensive overview of the state-of-the-art in BO, designed to support both researchers and practitioners in understanding recent advancements, practical applications, and future research directions. We begin by adopting a practitioner's perspective, illustrating how to effectively set up BO through a representative controller tuning example. We position BO within the broader context of learning paradigms, ranging from deep reinforcement learning to data-driven control, and highlight scenarios where BO is most advantageous. Next, we discuss the diverse range of BO methods that have been developed to tackle complex problems and specific applications. This article provides a unified perspective on the current landscape of BO, emphasizing its relevance to control systems and robotics, and it highlights future prospects by identifying key research challenges and promising avenues for advancing BO in the field. This includes addressing a significant gap in the BO landscape: the lack of standardized benchmark problems specifically for control-related applications. To foster future research and ensure rigorous evaluation, we start an effort towards a lightweight benchmark suite for control engineering and robotics. We also present metrics and best practices to facilitate direct comparisons between new BO algorithms and established state-of-the-art methods.

David Stenger, Paul Brunzema, Johanna Menn +3

RWTH Aachen University, Aachen, Germany · University of Technology Nuremberg, Nuremberg, Germany · Technical University of Munich, Munich, Germany

Robotic Control Bayesian Optimization Robot Learning

Sep 8, 2026cs.RO

Online, Reachability-Aware, Sampling-Based Motion Planning

Sampling-Based Model-Predictive Control (MPC) algorithms are a flexible class of controllers used for navigation on a wide range of robotic systems. Historically, such approaches have lacked hard safety guarantees, a shortcoming which we remedy in this work by computing guaranteed reachable-set overapproximations online with a fast, interval-based pipeline. We show that our method achieves similar performance to a state-of-the-art reachability-based planner without the need for the expensive pre-computation step, and can be scaled to systems that are infeasible using existing approaches. Finally, we demonstrate that our technique reduces safety violations by over 99% in a racing simulation and successfully controls a model racecar on real hardware experiments without crashes.

Brendan Gould, Zhiyuan Zhang, Panagiotis Tsiotras +1

Robotic Control Reachability Analysis Robot Safety Model Predictive Control Safe Motion Planning Sampling-Based Motion Planning