arXiv AI
Bridging the Sim-to-Real Gap with multipanda_ros2: A Real-Time ROS2 Framework for Multimanual Systems
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TAM: Torque Adaptation Module for Robust Motion Transfer in Manipulation
arXiv:2606. 06218v1 Announce Type: cross Abstract: A policy tuned for one robot often behaves differently on another, whether due to the sim-to-real gap, unknown payloads, or the differing dynamics of two instances of the same robot.
arXiv Machine Learning
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NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception
arXiv:2607. 11734v1 Announce Type: cross Abstract: Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error.
arXiv AI
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Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback
arXiv:2606. 14218v1 Announce Type: cross Abstract: For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact.
arXiv Machine Learning
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PhysVLA: Towards Physically-Grounded VLA for Embodied Robotic Manipulation
arXiv:2606. 13886v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models excel at mapping visual inputs and natural language instructions directly to robotic control policies.
arXiv AI
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FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation
FWBC‑VLA is a force‑aware framework that links vision‑language‑action (VLA) models with whole‑body compensation control for wheeled‑legged robots. It introduces HSR‑Force, a sensorless residual‑torque estimator that infers contact strength and encodes this information as tokens for the VLA action decoder, allowing the policy to detect contact onset, loading, and release. The system fine‑tunes a pretrained VLA backbone on a large WL&Arm dataset, combines proprioceptive, Jacobian‑derived force, and contact estimates to generate corrective actions, and demonstrates effectiveness in real‑world tasks such as whiteboard wiping and door opening.
arXiv AI
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Rapidly Learning Soft Robot Control via Implicit Time-Stepping
arXiv:2511. 06667v2 Announce Type: replace-cross Abstract: With the explosive growth of rigid-body simulators, policy learning in simulation has become the de facto standard for most rigid morphologies.