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Contact-rich Manipulation

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Showing 26–50 of 61 papers

TitleStatusHype
Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space—0
LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks—0
Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation—0
ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data—0
MILES: Making Imitation Learning Easy with Self-Supervision—0
MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation—0
Modality Selection and Skill Segmentation via Cross-Modality Attention—0
MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation—0
MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation—0
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments—0
Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization—0
PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies—0
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks—0
A Survey on Imitation Learning for Contact-Rich Tasks in Robotics—0
AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks—0
Augmentation for Learning From Demonstration with Environmental Constraints—0
A Virtual Reality Teleoperation Interface for Industrial Robot Manipulators—0
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing—0
CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity—0
Compliant Residual DAgger: Improving Real-World Contact-Rich Manipulation with Human Corrections—0
TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction—0
Uncertainty-aware Contact-safe Model-based Reinforcement Learning—0
Use the Force, Bot! -- Force-Aware ProDMP with Event-Based Replanning—0
Residual Feedback Learning for Contact-Rich Manipulation Tasks with Uncertainty—0
Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation—0
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