Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks Using Large Language Models
Large Language Models (LLMs) can successfully generate robot policy code for a variety of high-precision, contact-rich manipulation tasks by parameterizing the action space to include compliance with constraints on interaction forces and stiffnesses.
Multi-Fingered Dynamic Grasping of Unknown Objects in Real-Time
A practical framework for real-time multi-fingered grasping of unknown dynamic objects, leveraging a hybrid target model and adaptive grasp generation to handle challenging scenarios like conveyor belt and human-robot handover.
Learning Dexterous Prehensile Manipulation Skills from State-only Observations through Imitation and Emulation
A two-stage framework called CIMER that learns dexterous prehensile manipulation skills from state-only observations by first imitating the interdependent motions of the robot hand and object, and then refining the hand motion to emulate the desired object motion.
SAGE: A Generalizable Framework for Articulated Object Manipulation via Bridging Semantic and Actionable Parts
SAGE is a novel framework that bridges the understanding of semantic and actionable parts of articulated objects to achieve generalizable manipulation under natural language instructions.
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