# Embodied sensorimotor control: computational modeling of the neural control of movement

arXiv: https://arxiv.org/abs/2509.14360 · HTML: https://mlsanity.com/paper/2509.14360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 149 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation](https://mlsanity.com/paper/2106.13281/) (2021)
- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [Learning human behaviors from motion capture by adversarial imitation](https://mlsanity.com/paper/1707.02201/) (2017)
- [Universality and individuality in neural dynamics across large populations of recurrent networks](https://mlsanity.com/paper/1907.08549/) (2019)
- [Learning and Transfer of Modulated Locomotor Controllers](https://mlsanity.com/paper/1610.05182/) (2016)
- [Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors](https://mlsanity.com/paper/2203.17138/) (2022)
- [DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems](https://mlsanity.com/paper/2206.00484/) (2022)
- [Deep neuroethology of a virtual rodent](https://mlsanity.com/paper/1911.09451/) (2019)
- [Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters](https://mlsanity.com/paper/1410.6031/) (2014)
- [OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion](https://mlsanity.com/paper/2112.06061/) (2021)
- [Predictive auxiliary objectives in deep RL mimic learning in the brain](https://mlsanity.com/paper/2310.06089/) (2023)
- [KINESIS: Motion Imitation for Human Musculoskeletal Locomotion](https://mlsanity.com/paper/2503.14637/) (2025)

## Abstract (arXiv)

We review how sensorimotor control is dictated by interacting neural populations, optimal feedback mechanisms, and the biomechanics of bodies. First, we outline the distributed anatomical loops that shuttle sensorimotor signals between cortex, subcortical regions, and spinal cord. We then summarize evidence that neural population activity occupies low-dimensional, dynamically evolving manifolds during planning and execution of movements. Next, we summarize literature explaining motor behavior through the lens of optimal control theory, which clarifies the role of internal models and feedback during motor control. Finally, recent studies on embodied sensorimotor control address gaps within each framework by aiming to elucidate neural population activity through the explicit control of musculoskeletal dynamics. We close by discussing open problems and opportunities: multi-tasking and cognitively rich behavior, multi-regional circuit models, and the level of anatomical detail needed in body and network models. Together, this review and recent advances point towards reaching an integrative account of the neural control of movement.