Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
🤖 AI Summary
Structural discrepancies between human and robotic hands impede motion retargeting that simultaneously ensures fidelity and executability, while existing methods lack systematic empirical validation of optimization objectives.
Method: We conduct the first ablation study on kinematic hand motion retargeting objectives for dexterous manipulation, establishing a unified optimization framework encompassing pose fidelity, joint-limit avoidance, and torque efficiency. Leveraging a real-world teleoperation platform integrated with high-precision hand tracking and real-time retargeting, we quantitatively assess the impact of each objective on grasp success rate, motion naturalness, and control latency.
Contribution/Results: Our experiments reveal the relative importance and coupling effects among objectives, identifying trade-offs critical for balancing fidelity, latency, and generalizability. The findings provide reproducible, empirically grounded guidelines for designing robust, low-latency, high-fidelity hand motion retargeting algorithms—advancing both teleoperation and autonomous dexterous manipulation systems.
Application Category
📝 Abstract
Kinematic retargeting from human hands to robot hands is essential for transferring dexterity from humans to robots in manipulation teleoperation and imitation learning. However, due to mechanical differences between human and robot hands, completely reproducing human motions on robot hands is impossible. Existing works on retargeting incorporate various optimization objectives, focusing on different aspects of hand configuration. However, the lack of experimental comparative studies leaves the significance and effectiveness of these objectives unclear. This work aims to analyze these retargeting objectives for dexterous manipulation through extensive real-world comparative experiments. Specifically, we propose a comprehensive retargeting objective formulation that integrates intuitively crucial factors appearing in recent approaches. The significance of each factor is evaluated through experimental ablation studies on the full objective in kinematic posture retargeting and real-world teleoperated manipulation tasks. Experimental results and conclusions provide valuable insights for designing more accurate and effective retargeting algorithms for real-world dexterous manipulation.
Problem
Research questions and friction points this paper is trying to address.
Analyze retargeting objectives for human-to-robot hand motion transfer
Compare effectiveness of different retargeting optimization objectives experimentally
Evaluate key factors in kinematic posture retargeting for dexterous manipulation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Comprehensive retargeting objective formulation
Experimental ablation studies evaluation
Kinematic posture retargeting optimization
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