mlsanityData to 14 Oct 2026

Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks

arXiv 2207.03952 ↗First posted 8 Jul 2022cs.RO cs.LGcited by 6

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Abstract (arXiv)

Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements. We hypothesize that highly nonlinear muscle dynamics play a large role in providing inherent stability, which is favorable to learning. While recent advances have been made in applying modern learning techniques to muscle-actuated systems both in simulation as well as in robotics, so far, no detailed analysis has been performed to show the benefits of muscles when learning from scratch. Our study closes this gap and showcases the potential of muscle actuators for core robotics challenges in terms of data-efficiency, hyperparameter sensitivity, and robustness.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 14 of its 61 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

14 of 61 references.

References · 14

  1. 2018Maximum a Posteriori Policy Optimisationcited by 328
  2. 2022MyoSuite -- A contact-rich simulation suite for musculoskeletal motor controlcited by 80
  3. 2016Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?cited by 48
  4. 2022DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systemscited by 29
  5. 2020Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learningcited by 27
  6. 2018Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environmentscited by 19
  7. 2020Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarkingcited by 19
  8. 2017Bayesian Optimization Using Domain Knowledge on the ATRIAS Bipedcited by 17
  9. 2020Variable impedance control and learning -- A reviewcited by 17
  10. 2019Artificial Intelligence for Prosthetics - challenge solutionscited by 12
  11. 2020Reinforcement Learning Control of a Biomechanical Model of the Upper Extremitycited by 11
  12. 2021OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotioncited by 10
2 more
  1. 2020Reinforcement Learning of Musculoskeletal Control from Functional Simulationscited by 8
  2. 2019Learning to Control Highly Accelerated Ballistic Movements on Muscular Robotscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 0; 2025: 1; 2026: 2.

6 papers.

  1. 2023Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Modelscited by 5
  2. 2023Investigating the Impact of Action Representations in Policy Gradient Algorithmscited by 2
  3. 2023MIMo: A Multi-Modal Infant Model for Studying Cognitive Developmentcited by 2
  4. 2025Arnold: A multi-task, multi-embodiment muscle transformer policycited by 2
  5. 2026Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networkscited by 0
  6. 2026Model-Based Reinforcement Learning Exploits Passive Body Dynamics for High-Performance Biped Robot Locomotioncited by 0