title: MyoDex: A Generalizable Prior for Dexterous Manipulation
summary: Human dexterity is a hallmark of motor control. 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
  musculoskeletal sensory-motor circuits. 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 their previous experience to quickly
  acquire new (previously unattainable) behaviors. Specifically, our approach
  leverages multi-task learning to implicitly capture 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. Agents leveraging MyoDex can solve approximately
  3x more tasks, and 4x faster 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. We also demonstrate the effectiveness of our paradigms beyond
  musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit
  Hand. Website: https://sites.google.com/view/myodex
paper_authors._id: 64f9465682673b2a07f9dbd8
paper_authors.name: Vittorio Caggiano
paper_authors._id: 64f9465682673b2a07f9dbd9
paper_authors.name: Sudeep Dasari
paper_authors._id: 64f9465682673b2a07f9dbda
paper_authors.name: Vikash Kumar
paper_title: MyoDex: A Generalizable Prior for Dexterous Manipulation
paper_summary: Human dexterity is a hallmark of motor control. 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
  musculoskeletal sensory-motor circuits. 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 their previous experience to quickly
  acquire new (previously unattainable) behaviors. Specifically, our approach
  leverages multi-task learning to implicitly capture 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. Agents leveraging MyoDex can solve approximately
  3x more tasks, and 4x faster 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. We also demonstrate the effectiveness of our paradigms beyond
  musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit
  Hand. Website: https://sites.google.com/view/myodex
paper_ai_summary: Multi-task learning is used to develop a manipulative prior for human-like dexterity, enabling agents to quickly learn a wide range of complex manipulation tasks using a realistic human hand model.
paper_ai_keywords: multi-task learning task-agnostic behavioral priors physiologically realistic human hand model few-shot generalization positive transfer musculoskeletal control behaviors contact-rich behaviors Adroit Hand