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25 papers in the last four weeks, up 127% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 167

Mar 6, 2026cs.RO

Safe Consensus of Cooperative Manipulation with Hierarchical Event-Triggered Control Barrier Functions

Cooperative transport and manipulation of heavy or bulky payloads by multiple manipulators requires coordinated formation tracking, while simultaneously enforcing strict safety constraints in varying environments with limited communication and real-time computation budgets. This paper presents a distributed control framework that achieves consensus coordination with safety guarantees via hierarchical event-triggered control barrier functions (CBFs). We first develop a consensus-based protocol that relies solely on local neighbor information to enforce both translational and rotational consistency in task space. Building on this coordination layer, we propose a three-level hierarchical event-triggered safety architecture with CBFs, which is integrated with a risk-aware leader selection and smooth switching strategy to reduce online computation. The proposed approach is validated through real-world hardware experiments using two Franka manipulators operating with static obstacles, as well as comprehensive simulations demonstrating scalable multi-arm cooperation with dynamic obstacles. Results demonstrate higher precision cooperation under strict safety constraints, achieving substantially reduced computational cost and communication frequency compared to baseline methods.

Simiao Zhuang, Bingkun Huang, Zewen Yang

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), 80992 Munich, Germany

Control Barrier Functions Multi-Agent Control Multi-Robot Coordination Robotic Manipulation Feedback Control Event-Triggered Control

Feb 1, 2026cs.RO

TriphiBot: A Triphibious Robot Combining FOC-based Propulsion with Eccentric Design

Triphibious robots capable of multi-domain motion and cross-domain transitions are promising to handle complex tasks across diverse environments. However, existing designs primarily focus on dual-mode platforms, and some designs suffer from high mechanical complexity or low propulsion efficiency, which limits their application. In this paper, we propose a novel triphibious robot capable of aerial, terrestrial, and aquatic motion, by a minimalist design combining a quadcopter structure with two passive wheels, without extra actuators. To address inefficiency of ground-support motion (moving on land/seabed) for quadcopter based designs, we introduce an eccentric Center of Gravity (CoG) design that inherently aligns thrust with motion, enhancing efficiency without specialized mechanical transformation designs. Furthermore, to address the drastic differences in motion control caused by different fluids (air and water), we develop a unified propulsion system based on Field-Oriented Control (FOC). This method resolves torque matching issues and enables precise, rapid bidirectional thrust across different mediums. Grounded in the perspective of living condition and ground support, we analyse the robot's dynamics and propose a Hybrid Nonlinear Model Predictive Control (HNMPC)-PID control system to ensure stable multi-domain motion and seamless transitions. Experimental results validate the robot's multi-domain motion and cross-mode transition capability, along with the efficiency and adaptability of the proposed propulsion system.

Xiangyu Li, Tiancheng Lai, Mingwei Lai +6

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China · Huzhou Institute of Zhejiang University, Huzhou 313000, China · Hainan Red Tower Cigarette Co., Ltd., Hainan 571100, China

Aerial Robotics Nonlinear MPC Robot Locomotion Feedback Control

Jan 27, 2026eess.SY

Model-Free Output Feedback Stabilization via Policy Gradient Methods

Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. The problem becomes challenging when the system model is unknown. Among the Reinforcement Learning (RL) algorithms that have been successfully applied to solve problems pertaining to unknown linear dynamical systems, the policy gradient (PG) method stands out due to its ease of implementation and can solve the problem in a model-free manner. However, most of the existing works on PG methods for unknown linear dynamical systems assume full-state feedback. In this paper, we take a step towards model-free learning for partially observed linear dynamical systems with output feedback and focus on the fundamental stabilization problem of the system. We propose an algorithmic framework that stretches the boundary of PG methods to the problem without global convergence guarantees. We show that by leveraging zeroth-order PG update based on system trajectories and its convergence to stationary points, the proposed algorithms return a stabilizing output feedback policy for discrete-time linear dynamical systems. We also explicitly characterize the sample complexity of our algorithm and verify the effectiveness of the algorithm using numerical examples.

Ankang Zhang, Ming Chi, Xiaoling Wang +1

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China · College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Dynamical Systems Partially Observable RL Feedback Control Policy Gradient Methods Model-Free RL

Dec 24, 2025cs.RO

Tracing Energy Flow: Learning Tactile-based Grasping Force Control to Reduce Slippage in Dynamic Object Interaction

Regulating grasping force to reduce slippage during dynamic object interaction remains a fundamental challenge in robotic manipulation, especially when objects are manipulated by multiple rolling contacts, have unknown properties (such as mass or surface conditions), and when external sensing is unreliable. In contrast, humans can quickly regulate grasping force by touch, even without visual cues. Inspired by this ability, we aim to enable robotic hands to rapidly explore objects and learn tactile-driven grasping force control under motion and limited sensing. We propose a physics-informed energy abstraction that models the object as a virtual energy container. The inconsistency between the fingers' applied power and the object's retained energy provides a physically grounded signal for inferring slip-aware stability. Building on this abstraction, we employ model-based learning and planning to efficiently model energy dynamics from tactile sensing and perform real-time grasping force optimization. Experiments in both simulation and hardware demonstrate that our method can learn grasping force control from scratch within minutes, effectively reduce slippage, and extend grasp duration across diverse motion-object pairs, all without relying on external sensing or prior object knowledge. (Video: https://youtu.be/l3TJV29Mo6w)

Cheng-Yu Kuo, Hirofumi Shin, Takamitsu Matsubara

Graduated School of Science and Technology, Nara Institute of Science and Technology, Nara 630-0192, Japan · Honda R&D, Ltd., Saitama 351-0114, Japan

Robotic Grasping Contact-Rich Robotic Manipulation Force-Controlled Robotic Manipulation Feedback Control Tactile Sensing for Robotic Manipulation

Nov 23, 2025cs.RO

Expanding the Workspace of Electromagnetic Navigation Systems Using Dynamic Feedback for Single- and Multi-agent Control

Electromagnetic navigation systems (eMNS) enable a number of magnetically guided surgical procedures. A challenge in magnetically manipulating surgical tools is that the effective workspace of an eMNS is often severely constrained by power and thermal limits. We show that system-level control design significantly expands this workspace by reducing the currents needed to achieve a desired motion. We identified five key system approaches that enable this expansion: (i) motion-centric torque/force objectives, (ii) energy-optimal current allocation, (iii) real-time pose estimation, (iv) dynamic feedback, and (v) high-bandwidth eMNS components. As a result, we stabilize a 3D inverted pendulum on an eight-coil OctoMag eMNS with significantly lower currents (0.1-0.2 A vs. 8-14 A), by replacing a field-centric field-alignment strategy with a motion-centric torque/force-based approach. We generalize to multi-agent control by simultaneously stabilizing two inverted pendulums within a shared workspace, exploiting magnetic-field nonlinearity and coil redundancy for independent actuation. A structured analysis compares the electromagnetic workspaces of both paradigms and examines current-allocation strategies that map motion objectives to coil currents. Cross-platform evaluation of the clinically oriented Navion eMNS further demonstrates substantial workspace expansion by maintaining stable balancing at distances up to 50 cm from the coils. The results demonstrate that feedback is a practical path to scalable, efficient, and clinically relevant magnetic manipulation.

Jasan Zughaibi, Denis von Arx, Maurus Derungs +5

Multi-Scale Robotics Lab, ETH Zurich, 8092 Zurich, Switzerland · Learning and Dynamical Systems Group, Max Planck Institute for Intelligent Systems, 72076 Tübingen, Germany

Multi-Agent Control Feedback Control Surgical Robotics

Oct 16, 2025cs.RO

Stability Boundaries and Motor Performance in Delayed Robot-Mediated Dyadic Interactions

This paper establishes analytical stability boundaries for robot-mediated human-human (dyadic) interaction systems, subject to haptic communication under network-induced time delays. Bypassing conservative approximations, we employ a frequency-domain zero-crossing methodology to extract explicit stability limits based on the robotic hardware dynamics and coupling stiffness. To demonstrate the scalability of this mathematical framework, we extend the analysis from an elastic coupling to a highly complex, asymmetric virtual proxy topology. The theoretical analysis reveals how interaction stiffness non-linearly constrains the system's stability margin, heightening its vulnerability to delay. Furthermore, we validate these theoretical boundaries through experimental trials, highlighting the correlation between analytical stability margins and empirical motor performance. The proposed framework provides rigorous design guidelines for stable remote dyadic systems and suggests the prerequisites for effective delay-compensation strategies.

Mingtian Du, Suhas Raghavendra Kulkarni, Simone Kager +2

Robotics Research Centre, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 · Department of Computer Science and Artificial Intelligence, University of Technology Nuremberg, Germany 90461

Robot Teleoperation Feedback Control

Sep 15, 2025cs.RO

Distributed Event-Triggered Distance-Based Formation Control for Multi-Agent Systems

This paper addresses collaborative formation control for multi-agent systems with limited resources. We consider a team of robots tasked with achieving a desired formation from an arbitrary initial configuration. To reduce unnecessary control updates and conserve resources, we propose a distributed event-triggered formation controller. Unlike the well-studied linear formation controllers, the proposed controller is nonlinear and relies on inter-agent distance measurements. Control updates are triggered only when the measurement error exceeds a state-dependent threshold, ensuring system stability while reducing actuation effort. We also employ a distributed control barrier function to guarantee inter-agent collision avoidance. The proposed controller is validated through simulations and real-world experiments involving different communication topologies, scalability tests, and variations in design parameters, and is also compared to periodic triggering strategies. Results demonstrate that the event-triggered approach significantly reduces control effort while preserving formation performance.

Evangelos Psomiadis, Panagiotis Tsiotras

D. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0150, USA

Multi-Robot Formation Control Multi-Agent Control Multi-Robot Coordination Feedback Control Event-Triggered Control

Aug 28, 2025cs.RO

First Experimental Demonstration of Natural Hovering Extremum Seeking: A New Paradigm in Flapping Flight Physics

In this article, we report the first attempt of experimental validation of the recently emerged new paradigm in hovering and flapping flight physics called "Natural Hovering Extremum Seeking (NH-ES)" [doi.org/10.1103/4dm4-kc4g], which theorized that stable hovering flight physics observed in nature by flapping insects and hummingbirds can be characterized and emulated via a model-free, real-time, computationally-basic, sensory-based feedback mechanism that only needs the built-in natural oscillations of the flapping wing motion as both the control and the propulsion input. We run experiments of moth-like, light bulb source-seeking, on a flapping-wing body in a total model-free setting that is agnostic to morphological parameters and body/aerodynamic models. That is, the flapping performer (e.g., insects or hummingbirds) do not perceive internal or external governing physical equations, system states, models or parameters. Moreover, in addition to experimentally demonstrating the NH-ES paradigm in vertical motion as predicted by simulations in our earlier foundational work, we provide experiments that suggest the expansion of the NH-ES theory to longitudinal mode by yielding altitude and pitch stabilization in hovering flight. In the presented experiments, the flapper seeks, then hovers, around the light bulb source like a moth, needing only feedback of sensory measurements of light intensity taken by a simple sensor mounted on the head of the flapper body. Our experiment confirms that NH-ES needs only instantaneous, local measurement of the physical signal (e.g., light) as a feedback to perform source-seeking and does not need measurements, estimations or reference information of states, angles, altitude, or coordinates of the flapper system or source, nor does it need any global information such as access to GPS. Our results were also achieved under delay and noise effects.

Simone Martini, Rohan Palanikumar, Ahmed A. Elgohary +1

Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, Ohio 45221, USA

Aerial Robotics Feedback Control

Jul 26, 2025math.OC

Douglas-Rachford Splitting for Group-Sparse Feedback Linear-Quadratic Control

In this paper, we study the distributed linear quadratic problem with fixed communication topology (DFT-LQ) and the sparse feedback linear quadratic (SF-LQ) problem through a unified optimization framework. Specifically, both problems are formulated as a nonconvex, nonsmooth optimization problem equipped with an

\ell_0

-penalty under affine constraints. To solve this problem, we first investigate the application of the Douglas-Rachford (DR) splitting algorithm. Under the local condition that the generated iterates remain on a fixed smooth manifold, we establish the convergence of the DR splitting to a stationary point. Furthermore, we characterize this stationary point as the global minimizer of a corresponding DFT-LQ problem. To bypass the restriction of the smooth manifold assumption, we introduce a projected subgradient descent algorithm that achieves global convergence without relying on smooth-manifold structures. This algorithm may serve as a warm-start mechanism that effectively drives the iterates toward the desired smooth manifolds, thereby establishing a favorable initialization where the convergence theory of the DR splitting algorithm becomes fully applicable. Numerical experiments shed light on the effectiveness of the proposed methods in distributed group-sparse controller design.

Lechen Feng, Xun Li, Yuan-Hua Ni

Department of Applied Mathematics, The Hong Kong Polytechnic University, China · College of Artificial Intelligence, Nankai University, Tianjin, China

Distributed Optimization Nonconvex Optimization Feedback Control Continuous Control Linear Quadratic Regulator

Jun 10, 2025eess.SY

Differentiable Physics-Informed Adaptive Koopman Control for Stable Flight under Unknown Disturbances

Uncertainties and disturbances in robotic systems, such as aerodynamic forces, are fundamentally outcomes of physical interactions with the environment, manifesting as learnable spatiotemporal sequences rather than random noise. However, achieving high-precision control for robotic systems operating in unstructured environments is often hindered by complex unmodeled dynamics and external disturbances. While learning-based methods offer powerful approximation capabilities, they typically suffer from heavy reliance on offline training and lack theoretical guarantees. Conversely, traditional robust control strategies are predominantly reactive, limited to instantaneous estimation without the foresight to anticipate future disturbance trends. To bridge this gap, this paper proposes a differentiable data-enabled Koopman control framework termed DEKC. Unlike black-box approaches, DEKC adopts a hybrid modeling strategy that retains the nominal physics model while employing a deep neural network to parameterize the lifting function of Koopman operator for unknown residual dynamics. Crucially, the framework formulates disturbances as a dynamical system, learning their temporal evolution in a global linear space. This enables the prediction of future disturbance trajectories, which are explicitly integrated into controller for preemptive compensation. Furthermore, an online backward gradient update mechanism is introduced to ensure real-time adaptation to time-varying uncertainties. Numerical simulations on a tethered space robot demonstrate the efficacy of the proposed DEKC in mitigating highly coupled uncertainties. Complementing these results, real-world experiments on a quadrotor substantiate its superiority in tracking agile trajectories under uncertainties induced by aerodynamics and suspended payload.

Ao Jin, Qiujin Liang, Tao Zhang +2

Research Center for Intelligent Robotics, School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China · Department of Automation, Tsinghua University, Beijing 100080, China

Robot Trajectory Tracking Robust Control Feedback Control Quadrotor Control Adaptive Control

Jun 5, 2025cs.RO

A Three-Stage Offline SDRE-Based Control Framework for Human Motion Reproduction on a Suspended Bipedal Robot

This paper presents a three-stage offline command generation framework for reproducing human lower-limb motion on a suspended bipedal robot while matching torque trajectories computed from the robot dynamic model. First, State-Dependent Riccati Equation (SDRE) control derives the reference torque trajectory for the measured motion. Second, parameterized optimization converts this trajectory into trapezoidal joint velocity commands under motor speed and acceleration limits. Third, a proportional-integral-derivative linear quadratic regulator (PID-LQR) compensation scheme refines these commands using experimental tracking data. The platform executes the resulting profiles to reproduce human walking and squatting motions recorded by a Vicon system, allowing evaluation of tracking accuracy and repeatability. Results show that the average root mean square error (RMSE) and standard deviation (STD) of joint angles across repeated trials remain below 7° and 0.33°, respectively. Joint angle and torque trajectory comparisons show lower maximum RMSE and STD values than those for MPC and IPSO-PID in every reported case. The framework enables accurate and repeatable motion reproduction within actuator limits, providing controlled and measurable conditions that can reduce reliance on human participation and associated risks during preliminary evaluation of devices for assistive walking, gait training, and rehabilitation.

Ping-Kong Huang, Chien-Wu Lan, Chin-Tien Wu +1

Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan · Department of Electrical Engineering, National Central University, Taoyuan City 320317, Taiwan

Robotic Control Robot Trajectory Tracking Robot Motion Generation Feedback Control

Mar 18, 2025cs.RO

Learning-Based Progressive Barrier Control for Robot Manipulators with Initial Errors Outside Prescribed Tracking Bounds

Robot manipulators may start a new task with a tracking error larger than the prescribed tolerance. Conventional barrier controllers generally require the initial error to lie within this tolerance, which prevents their direct use under such conditions. This paper develops a progressive barrier controller that gradually contracts an initial error bound to the required value within a prescribed time. The robot can therefore start outside the final bound, while the direct joint-position error satisfies it after the transition. The closed-form control law combines progressive barrier feedback with an online adaptive torque term based on an actor--critic structure. A Lyapunov analysis establishes bounded closed-loop signals and gives sufficient conditions for satisfaction of the final tracking bound. Two-link simulations consider large initial errors, actuator saturation, dynamic variations, disturbances, and measurement errors. The adaptive term reduces the median root-mean-square tracking error by 45.1% compared with the zero-weight progressive barrier controller. The simulations also map the initial errors that can be handled at different transition times under fixed torque limits. Finally, an experiment on a Niryo Ned3 Pro illustrates tracking performance using measured position and motor-current data.

Hamed Rahimi Nohooji, Danial Zafaranchizadeh Moghaddam, Abolfazl Zaraki +1

Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 1855 Luxembourg, Luxembourg · School of Physics, Engineering and Computer Science (SPECS), and Robotics Research Group of the University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom

Robotic Control Robot Trajectory Tracking Feedback Control Adaptive Control

Mar 1, 2025cs.RO

Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks

Reinforcement learning (RL) has achieved remarkable success in various robotic tasks; however, its deployment in real-world scenarios, particularly in contact-rich environments, often overlooks critical safety and stability aspects. Policies without passivity guarantees can result in system instability, posing risks to robots, their environments, and human operators. In this work, we investigate the limitations of traditional RL policies when deployed in contact-rich tasks and explore the combination of energy-based passive control with safe RL in both training and deployment to answer these challenges. Firstly, we reveal the discovery that standard RL policy does not satisfy stability in contact-rich scenarios. Secondly, we introduce a \textit{passivity-aware} RL policy training with energy-based constraints in our safe RL formulation. Lastly, a passivity filter is exerted on the policy output for \textit{passivity-ensured} control during deployment. We conduct comparative studies on a contact-rich robotic maze exploration task, evaluating the effects of learning passivity-aware policies and the importance of passivity-ensured control. The experiments demonstrate that a passivity-agnostic RL policy easily violates energy constraints in deployment, even though it achieves high task completion in training. The results show that our proposed approach guarantees control stability through passivity filtering and improves the energy efficiency through passivity-aware training. A video of real-world experiments is available as supplementary material. We also release the checkpoint model and offline data for pre-training at Hugging Face.

Heng Zhang, Gokhan Solak, Sebastian Hjorth +1

HRI2 Lab, Istituto Italiano di Tecnologia, Genoa, Italy · Ph.D. program of national interest in Robotics and Intelligent Machines (DRIM) and University of Genova, Genoa, Italy

Robotic Control Contact-Rich Robotic Manipulation Robotic RL Robot Safety Feedback Control Passivity-Based Control+1

Feb 20, 2025math.OC

Sample Complexity of Linear Quadratic Regulator Without Initial Stability

Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics. Unlike prior methods, our algorithm avoids reliance on two-point gradient estimates while maintaining the same order of sample complexity. Furthermore, it eliminates the restrictive requirement of starting with a stable initial policy, broadening its applicability. Beyond these improvements, we introduce a refined analysis of error propagation through the contraction of the Riccati operator under the Riemannian distance. This refinement leads to a better sample complexity and ensures improved convergence guarantees.

Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard

RL Control Sample Complexity Feedback Control Linear Quadratic Regulator Sample-Efficient RL

Nov 6, 2024cs.RO

Observability-Aware Control for Quadrotor Formation Flight with Range-only Measurement

Cooperative Localization is a promising approach to achieving safe quadrotor formation flight through precise positioning via low-cost inter-drone sensors. This paper develops an observability-aware control principle tailored to quadrotor formation flight with range-only inter-drone measurements. The control principle is based on a novel approximation of the local observability Gramian (LOG), which we name the Short-Term Local Observability Gramian (STLOG). The validity of STLOG is established by proving its link to directional estimation precision in nonlinear systems. We propose the Observability Predictive Controller (OPC), a receding-horizon controller that generates optimal inputs to enhance information gain in weakly observable state directions by maximizing the minimum eigenvalue of the STLOG. This reduces the risk of estimator divergence due to the unbounded growth of uncertainty in weakly observed state components. Monte Carlo simulations and flight experiments are conducted with quadrotors in a GNSS-denied ferrying mission, showing that the OPC improves positioning confidence and estimator robustness.

H S Helson Go, Ching Lok Chong, Longhao Qian +1

Institute for Aerospace Studies, University of Toronto, 4925 Dufferin St., North York, Ontario, Canada, M3H 5T6 · Oxford University, OCIAM

Multi-Robot Formation Control Cooperative Localization Model Predictive Control Feedback Control Quadrotor Control Robot State Estimation+1

Mar 17, 2024cs.RO

N-dimensional Convex Obstacle Avoidance using Hybrid Feedback Control (Extended version)

This paper addresses the autonomous robot navigation problem in a priori unknown n-dimensional environments containing disjoint convex obstacles of arbitrary shapes and sizes, with pairwise distances strictly greater than the robot's diameter. We propose a hybrid feedback control scheme that guarantees safe and global asymptotic convergence of the robot to a predefined target location. The proposed control strategy relies on a switching mechanism allowing the robot to operate either in the move-to-target mode or the obstacle-avoidance mode, based on its proximity to the obstacles and the availability of a clear straight path between the robot and the target. In the obstacle-avoidance mode, the robot is constrained to move within a two-dimensional plane that intersects the obstacle being avoided and the target, preventing it from retracing its path. The effectiveness of the proposed hybrid feedback controller is demonstrated through simulations in two-dimensional and three-dimensional environments.

Mayur Sawant, Ilia Polushin, Abdelhamid Tayebi

Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada · Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada

Collision Avoidance Safe Robot Navigation Feedback Control Robot Collision Avoidance

Date pendingcs.RO

Periodic robust robotic rock chop via virtual model control

Robotic cutting is a challenging, contact-rich manipulation task where the robot must simultaneously negotiate unknown object mechanics, large contact forces, and precise motion requirements. Our hypothesis is that this complexity can be alleviated through the design of a physically structured virtual-model controller that uses switched virtual mechanisms to generate a robust, rhythmic rock-chop motion for robotic cutting, without requiring pre-planned trajectories or precise environmental information. Motion is generated by the interaction between the environment, the robot's dynamics, and the virtual forces of the switching virtual mechanism, ultimately realized through the available actuation. Through theoretical and numerical analysis, together with experimental validation, we demonstrate that the controlled robot behavior settles into a stable periodic motion. Experiments with a Franka manipulator demonstrate robust cuts across five different vegetables, achieving sub-millimeter slice accuracy for thicknesses from 1 mm to 7 mm at a rate of nearly one cut per second. The controller maintains high performance despite changes in knife shape or cutting board height, and successfully adapts to a different humanoid manipulator, demonstrating robustness and platform independence.

Yi Zhang, Fumiya Iida, Fulvio Forni

University of Cambridge, United Kingdom · University of Tokyo, Japan

Contact-Rich Robotic Manipulation Robust Control Robotic Tool Use Feedback Control