# MyoDex: A Generalizable Prior for Dexterous Manipulation

arXiv: https://arxiv.org/abs/2309.03130 · HTML: https://mlsanity.com/paper/2309.03130/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 67 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Deep Dynamics Models for Learning Dexterous Manipulation](https://mlsanity.com/paper/1909.11652/) (2019)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning](https://mlsanity.com/paper/1911.12423/) (2019)
- [ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging](https://mlsanity.com/paper/1904.06830/) (2019)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [Neural probabilistic motor primitives for humanoid control](https://mlsanity.com/paper/1811.11711/) (2018)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [InfoBot: Transfer and Exploration via the Information Bottleneck](https://mlsanity.com/paper/1901.10902/) (2019)
- [Learning human behaviors from motion capture by adversarial imitation](https://mlsanity.com/paper/1707.02201/) (2017)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps](https://mlsanity.com/paper/2209.11221/) (2022)
- [Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation](https://mlsanity.com/paper/1904.13041/) (2019)
- [Discovering Diverse Athletic Jumping Strategies](https://mlsanity.com/paper/2105.00371/) (2021)
- [Reinforcement Learning of Musculoskeletal Control from Functional Simulations](https://mlsanity.com/paper/2007.06669/) (2020)
- [Learning Relevant Features for Manipulation Skills using Meta-Level Priors](https://mlsanity.com/paper/1605.04439/) (2016)

## Built on by (most cited first, 11 of 11)

- [HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation](https://mlsanity.com/paper/2403.10506/) (2024)
- [DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2505.24853/) (2025)
- [Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation](https://mlsanity.com/paper/2312.05473/) (2023)
- [DynSyn: Dynamical Synergistic Representation for Efficient Learning and Control in Overactuated Embodied Systems](https://mlsanity.com/paper/2407.11472/) (2024)
- [Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow](https://mlsanity.com/paper/2601.19707/) (2026)
- [emg2tendon: From sEMG Signals to Tendon Control in Musculoskeletal Hands](https://mlsanity.com/paper/2508.08269/) (2025)
- [Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity](https://mlsanity.com/paper/2603.29332/) (2026)
- [Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows](https://mlsanity.com/paper/2402.02904/) (2024)
- [Imitation Learning for Adaptive Control of a Virtual Soft Exoglove](https://mlsanity.com/paper/2505.09099/) (2025)
- [Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models](https://mlsanity.com/paper/2512.23077/) (2025)
- [MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning](https://mlsanity.com/paper/2602.15245/) (2026)

## Abstract (arXiv)

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