mlsanityData to 13 Oct 2026

Lambda-Hold Control: Human-Like Movement Emerges from a Minimal Task Reward in Predictive Musculoskeletal Simulation

arXiv 2608.17030 ↗First posted 17 Aug 2026cs.RO cs.GR cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

The massive overactuation in the human musculoskeletal system makes it challenging to train musculoskeletal models to generate human-like motion via reinforcement learning, primarily because exploration in the resulting high-dimensional and redundant action space is extremely inefficient. To address this problem, we propose the λ-hold controller, inspired by the equilibrium-point (EP) hypothesis, which has been widely supported by extensive evidence from human motor control studies. The policy's control variable is the per-muscle EP threshold length λ, from which a stretch-reflex recruitment law computes the muscle excitations automatically. Holding each λ over an interval of the gait phase also sharply reduces the frequency at which the policy must be queried. Consequently, the controller, to our knowledge for the first time, enables a muscle-actuated skeletal model to learn human-like sprinting using only a minimal reward within an hour of training. The efficient exploration through the proposed λ-hold controller is not merely an engineering trick but an approach grounded in physiology, bringing together the EP hypothesis, intermittent control, and optimal feedback control. Beyond encapsulating human-like behavior in predictive simulation, this achievement contributes to developing a learnable model of the human motor controller.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 71 references that are in the map. First posted on arXiv 17 Aug 2026.

Builds on

17 of 71 references.

References · 17

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,656
  2. 2015Continuous control with deep reinforcement learningcited by 3,290
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,310
  4. 2016The Option-Critic Architecturecited by 439
  5. 2018Maximum a Posteriori Policy Optimisationcited by 320
  6. 2020First return, then explorecited by 257
  7. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 159
  8. 2022MyoSuite -- A contact-rich simulation suite for musculoskeletal motor controlcited by 80
  9. 2017When Waiting is not an Option : Learning Options with a Deliberation Costcited by 58
  10. 2018Learning Symmetric and Low-energy Locomotioncited by 53
  11. 2020Smooth Exploration for Robotic Reinforcement Learningcited by 45
  12. 2017Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learningcited by 38
5 more
  1. 2022DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systemscited by 27
  2. 2023Latent Exploration for Reinforcement Learningcited by 21
  3. 2023SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representationcited by 17
  4. 2015A novel plasticity rule can explain the development of sensorimotor intelligencecited by 3
  5. 2026Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulationcited by 1

Built on by

Not cited yet.