Soft Robotics

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21 papers in the last four weeks, up 133% on the four weeks before. 0.2% of all new papers.

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Rapidly Learning Soft Robot Control via Implicit Time-Stepping
Nov 10, 2025Andrew Choi, Dezhong Tong, Xiaonan HuangRobot Policy Learning Robotics Simulation
Horizon Robotics, Cupertino, CA, USA · Robotics Department at the University of Michigan, Ann Arbor, MI, USA
External Photoreflective Tactile Sensing Based on Surface Deformation Measurement
Nov 9, 2025Seiichi Yamamoto, Hiroki Ishizuka, Takumi Kawasetsu +6Tactile Sensing Tactile Sensing for Robotic Manipulation
Graduate School of Engineering Science, The University of Osaka, Toyonaka 560-0043, Japan
A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms
Nov 4, 2025Linxin Hou, Qirui Wu, Zhihang Qin +3RL Control Robotic Policy Evaluation
Department of Electrical and Computer Engineering, National University of Singapore · Department of Computer Science, National University of Singapore · Department of Mechanical Engineering, National University of Singapore
Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation
Oct 13, 2025Junxiang Wang, Han Zhang, Zehao Wang +3Robotic Control Model Predictive Control
School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China · Seventh Affiliated Hospital, SunYat-sen University, Shenzhen 510275, China
Soft yet Effective Robots via Holistic Co-Design
Apr 20, 2025Maximilian Stölzle, Niccolò Pagliarani, Francesco Stella +6Hardware-Software Co-Design Soft Robotics
Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. · Cognitive Robotics, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands. · Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA. +4

Nov 10, 2025, cs.ROJ/K move · Enter open · S save

Rapidly Learning Soft Robot Control via Implicit Time-Stepping

Andrew Choi, Dezhong Tong, Xiaonan Huang

Horizon Robotics, Cupertino, CA, USA · Robotics Department at the University of Michigan, Ann Arbor, MI, USA

Robot Policy Learning Robotics Simulation Continuum Robot Control Soft Robotics Deformable Object Simulation

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With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies. In contrast, soft robotic simulation frameworks remain scarce and are seldom adopted by the soft robotics community. This gap stems partly from the lack of easy-to-use, general-purpose frameworks and partly from the high computational cost of accurately simulating continuum mechanics, which often renders policy learning infeasible. In this work, we demonstrate that rapid soft robot policy learning is indeed achievable via implicit time-stepping. Our simulator of choice, DisMech, is a general-purpose, fully implicit soft-body simulator capable of handling both soft dynamics and frictional contact. We further introduce delta natural curvature control, a method analogous to delta joint position control in rigid manipulators, providing an intuitive and effective means of enacting control for soft robot learning. To highlight the benefits of implicit time-stepping and delta curvature control, we conduct extensive comparisons across four diverse soft manipulator tasks against one of the most widely used soft-body frameworks, Elastica. With implicit time-stepping, parallel stepping of 500 environments achieves up to 6x faster speeds for non-contact cases and up to 40x faster for contact-rich scenarios. Finally, a comprehensive sim-to-sim gap evaluation--training policies in one simulator and evaluating them in another--demonstrates that implicit time-stepping provides a rare free lunch: dramatic speedups achieved without sacrificing accuracy.

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Latest papers 125

Rapidly Learning Soft Robot Control via Implicit Time-Stepping

External Photoreflective Tactile Sensing Based on Surface Deformation Measurement

A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms

Koopman Model Predictive Control of An Origami-Inspired Soft Exoskeleton for Knee Rehabilitation

Soft yet Effective Robots via Holistic Co-Design