Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks
Hao Jiang, Yue Wu, Yue Wang, Gaurav S. Sukhatme, Daniel Seita

TL;DR
This paper introduces DexMulti, a sample-efficient method for multi-stage dexterous manipulation that decomposes demonstrations into skills, enabling robust real-world performance with minimal data across various objects and tasks.
Contribution
DexMulti presents a novel approach that decomposes demonstrations into object-centric skills and retrieves them based on current object states, improving data efficiency and generalization in dexterous manipulation.
Findings
Achieves 66% success rate with only 3-4 demonstrations per object.
Outperforms diffusion policy baselines by 2-3x in data efficiency.
Demonstrates robust generalization to new objects and spatial variations.
Abstract
Dexterous hands enable concurrent prehensile and nonprehensile manipulation, such as holding one object while interacting with another, a capability essential for everyday tasks yet underexplored in robotics. Learning such long-horizon, contact-rich multi-stage behaviors is challenging because demonstrations are expensive to collect and end-to-end policies require substantial data to generalize across varied object geometries and placements. We present DexMulti, a sample-efficient approach for real-world dexterous multi-task manipulation that decomposes demonstrations into object-centric skills with well-defined temporal boundaries. Rather than learning monolithic policies, our method retrieves demonstrated skills based on current object geometry, aligns them to the observed object state using an uncertainty-aware estimator that tracks centroid and yaw, and executes them via a…
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Taxonomy
TopicsRobot Manipulation and Learning · Reinforcement Learning in Robotics · Motor Control and Adaptation
