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MyoDex: A Generalizable Prior for Dexterous Manipulation
Vittorio Caggiano · Sudeep Dasari · Vikash Kumar

Thu Jul 27 01:30 PM -- 03:00 PM (PDT) @ Exhibit Hall 1 #200

Human dexterity is a hallmark of motor control behaviors. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled by more than 40 muscles) of mosculoskeletal control. In this work, we take inspiration from how human dexterity builds on a diversity of prior experiences, instead of being acquired through a single task. Motivated by this observation, we set out to develop agents that can build upon previous experience to quickly acquire new (previously unattainable) behaviors. Specifically, our approach leverages multi-task learning to implicitly capture a task-agnostic behavioral priors (MyoDex) for human-like dexterity, using a physiologically realistic human hand model -- MyoHand. We demonstrate MyoDex's effectiveness in few-shot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. MyoDex can solve approximately 3x more tasks and it can accelerate the achievement of solutions by about 4x in comparison to a distillation baseline. While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors.

Author Information

Vittorio Caggiano (Meta)
Sudeep Dasari (Carnegie Mellon University)
Sudeep Dasari

I'm a PhD student at the Robotics Institute in Carnegie Mellon's School of Computer Science. I aspire to build scalable robotic learning algorithms, which can parse the visual world and enable autonomous agents to perform complex tasks in diverse environments. I am advised by Professor Abhinav Gupta. My research is supported by the NDSEG fellowship.

Vikash Kumar (Univ. Of Washington)

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