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Contact-rich Manipulation

Papers

Showing 26–50 of 61 papers

TitleStatusHype
Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks—0
TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction—0
Hearing Touch: Audio-Visual Pretraining for Contact-Rich Manipulation—0
Generalize by Touching: Tactile Ensemble Skill Transfer for Robotic Furniture Assembly—0
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks—0
Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot LearningCode2
Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing—0
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation—0
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation—0
MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation—0
A Virtual Reality Teleoperation Interface for Industrial Robot Manipulators—0
Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment—0
Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation—0
ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot InteractionCode0
Augmentation for Learning From Demonstration with Environmental Constraints—0
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation—0
Learning Dense Reward with Temporal Variant Self-Supervision—0
Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning StudyCode1
A Differentiable Recipe for Learning Visual Non-Prehensile Planar ManipulationCode0
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space—0
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty—0
Reinforcement Learning for Contact-Rich Tasks: Robotic Peg Insertion StrategiesCode1
Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors—0
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing—0
Recovery RL: Safe Reinforcement Learning with Learned Recovery ZonesCode1
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