Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots

• URL: http://arxiv.org/abs/2606.22397v1
• Date: Sun, 21 Jun 2026 09:02:53 GMT
• Title: Do Rigid-Body Simulators Dream of Soft Robots? Learning Contact-Rich Manipulation for Tendon-Driven Continuum Robots
• Abstract summary: We bridge the gap for tendon-driven continuum robots by deriving a continuum-mechanics-informed discretization.<n>This is the first demonstration of sim-to-real transfer for contact-rich manipulation with continuum robots.
• Score: 4.338766069286991
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning contact-rich, whole-body manipulation for soft continuum robots is held back by the lack of simulation infrastructure that has accelerated rigid-robot manipulation. Existing soft robot simulators are physically grounded but lack the contact handling, actuation support, or learning integration needed for contact-rich manipulation; rigid-body approximations offer these capabilities but sacrifice physical grounding. We bridge this gap for tendon-driven continuum robots (TDCRs) by deriving a continuum-mechanics-informed discretization that places the soft robot natively inside MuJoCo, unifying tendon forces, body contact, and dynamics in a single physics pipeline. We validate the simulator against a Cosserat rod reference (static and dynamic) and real TDCR hardware. We then train state-based imitation learning policies via teleoperation in simulation and deploy them zero-shot to a physical 3-segment TDCR on a 7-DoF Franka arm across two contact-rich manipulation tasks. To our knowledge, this is the first demonstration of sim-to-real transfer for contact-rich manipulation with continuum robots.

Related papers

• RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation [48.828787848144806]
  We introduce digital teleoperation, a paradigm that decouples data collection from physical constraints by replacing real robot with a generative world model.<n>We instantiate this paradigm in RynnWorld-Teleop, a system that integrates depth-aware skeletal conditioning, progressive human-to-robot training on a video Diffusion Transformer, and streaming autoregressive distillation.<n>This pipeline compresses the generative process into a single-pass inference, enabling 40+ FPS, real-time interactive generation on a single H100 GPU.
  arXiv  Detail & Related papers  (2026-07-07T17:58:11Z)
• DataLadder: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid [63.45560198934907]
  Generalist robot policies require trustworthy evaluation and robot-usable training data.<n>We present DataLadder, a simulation-enabled interconversion toolchain for human-robot aligned model evaluation and data generation.<n>We package the core reconstruction, simulation, rendering, and realism-augmentation modules as cloud services on JD Cloud.
  arXiv  Detail & Related papers  (2026-06-15T14:21:35Z)
• Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks [53.12548325396181]
  Tendon drives paired with soft muscle actuation enable faster and safer robots.<n>We propose a sim-to-real pipeline that learns a neural network model of this complex actuation.<n>We successfully deploy precise goal-reaching and dynamic ball-in-a-cup policies trained entirely in simulation.
  arXiv  Detail & Related papers  (2026-04-10T16:52:54Z)
• Functional Force-Aware Retargeting from Virtual Human Demos to Soft Robot Policies [22.000828900701237]
  We introduce SoftAct, a framework for teaching soft robot hands to perform human-like manipulation skills by explicitly reasoning about contact forces.<n>Unlike conventional approaches that retarget human joint trajectories, SoftAct employs two-stage, force-aware algorithm.
  arXiv  Detail & Related papers  (2026-04-01T17:59:35Z)
• Tendon Force Modeling for Sim2Real Transfer of Reinforcement Learning Policies for Tendon-Driven Robots [4.374417345150659]
  We propose a method to model the tendon forces produced by typical servo motors.<n>We then utilize our force estimation model in a GPU-accelerated tendon force-driven rigid body simulation to train RL-based controllers.<n>Our model is capable of predicting tendon forces within 3% of the maximum motor force and is robot-agnostic.
  arXiv  Detail & Related papers  (2026-03-04T18:16:58Z)
• SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation [33.51083722346151]
  Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control.<n>This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation.
  arXiv  Detail & Related papers  (2026-02-02T17:59:31Z)
• Feel the Force: Contact-Driven Learning from Humans [52.36160086934298]
  Controlling fine-grained forces during manipulation remains a core challenge in robotics.<n>We present FeelTheForce, a robot learning system that models human tactile behavior to learn force-sensitive manipulation.<n>Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks.
  arXiv  Detail & Related papers  (2025-06-02T17:57:52Z)
• Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [34.47272224723296]
  We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed.<n>Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.
  arXiv  Detail & Related papers  (2025-04-17T12:57:11Z)
• Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System [5.497832119577795]
  dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors. Learning from Demonstrations offers an intuitive alternative, allowing robots to learn manipulations through observed actions.
  arXiv  Detail & Related papers  (2024-06-21T09:03:37Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine [9.268539775233346]
  Tensegrity robots exhibit high strength-to-weight ratios and significant deformations. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. This paper describes a Real2Sim2Real (R2S2R) strategy for modeling tensegrity robots.
  arXiv  Detail & Related papers  (2022-09-13T18:51:26Z)
• COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing [87.7257446869134]
  General contact-rich manipulation problems are long-standing challenges in robotics. Deep reinforcement learning has shown great potential in solving robot manipulation tasks. We propose COCOI, a deep RL method that encodes a context embedding of dynamics properties online.
  arXiv  Detail & Related papers  (2020-11-23T08:20:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.