Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations

• URL: http://arxiv.org/abs/2506.09383v1
• Date: Wed, 11 Jun 2025 04:23:49 GMT
• Title: Bipedal Balance Control with Whole-body Musculoskeletal Standing and Falling Simulations
• Authors: Chengtian Ma, Yunyue Wei, Chenhui Zuo, Chen Zhang, Yanan Sui,
• Abstract summary: Balance control is important for human and bipedal robotic systems.<n>This work offers unique muscle-level insights into human balance dynamics.<n>It could provide a foundation for developing targeted interventions for individuals with balance impairments.
• Score: 11.689074741652163
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of static balance and falling remains limited. This work presents a hierarchical control pipeline for simulating human balance via a comprehensive whole-body musculoskeletal system. We identified spatiotemporal dynamics of balancing during stable standing, revealed the impact of muscle injury on balancing behavior, and generated fall contact patterns that aligned with clinical data. Furthermore, our simulated hip exoskeleton assistance demonstrated improvement in balance maintenance and reduced muscle effort under perturbation. This work offers unique muscle-level insights into human balance dynamics that are challenging to capture experimentally. It could provide a foundation for developing targeted interventions for individuals with balance impairments and support the advancement of humanoid robotic systems.

Related papers

• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• HuB: Learning Extreme Humanoid Balance [21.986188490182993]
  We propose HuB (Humanoid Balance), a unified framework that integrates reference motion refinement, balance-aware policy learning, and sim-to-real training.<n>We validate our approach on the Unitree G1 humanoid robot across challenging quasi-static balance tasks.
  arXiv  Detail & Related papers  (2025-05-12T07:31:42Z)
• Allostatic Control of Persistent States in Spiking Neural Networks for perception and computation [79.16635054977068]
  We introduce a novel model for updating perceptual beliefs about the environment by extending the concept of Allostasis to the control of internal representations.<n>In this paper, we focus on an application in numerical cognition, where a bump of activity in an attractor network is used as a spatial numerical representation.
  arXiv  Detail & Related papers  (2025-03-20T12:28:08Z)
• Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control [47.423243831156285]
  We present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control.<n>We demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data.<n>KINESIS generates muscle activity patterns that correlate well with human EMG activity.
  arXiv  Detail & Related papers  (2025-03-18T18:37:49Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• Muscles in Time: Learning to Understand Human Motion by Simulating Muscle Activations [64.98299559470503]
  Muscles in Time (MinT) is a large-scale synthetic muscle activation dataset. It contains over nine hours of simulation data covering 227 subjects and 402 simulated muscle strands. We show results on neural network-based muscle activation estimation from human pose sequences.
  arXiv  Detail & Related papers  (2024-10-31T18:28:53Z)
• MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
  We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
  arXiv  Detail & Related papers  (2024-04-16T02:18:18Z)
• Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models [29.592874007260342]
  Humans excel at robust bipedal walking in complex natural environments. It is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multi-objective control problem.
  arXiv  Detail & Related papers  (2023-09-06T13:20:31Z)
• Characterization of Human Balance through a Reinforcement Learning-based Muscle Controller [1.5469452301122177]
  Balance assessment during physical rehabilitation often relies on rubric-oriented battery tests to score a patient's physical capabilities, leading to subjectivity. This study explores the use of the center of mass (COM) state space and presents a promising avenue for monitoring the balance capabilities in humans.
  arXiv  Detail & Related papers  (2023-08-08T01:53:26Z)
• Dynamics with autoregressive neural quantum states: application to critical quench dynamics [41.94295877935867]
  We present an alternative general scheme that enables one to capture long-time dynamics of quantum systems in a stable fashion. We apply the scheme to time-dependent quench dynamics by investigating the Kibble-Zurek mechanism in the two-dimensional quantum Ising model.
  arXiv  Detail & Related papers  (2022-09-07T15:50:00Z)
• Reinforcement Learning of Musculoskeletal Control from Functional Simulations [3.94716580540538]
  In this work, a deep reinforcement learning (DRL) based inverse dynamics controller is trained to control muscle activations of a biomechanical model of the human shoulder. Results are presented for a single-axis motion control of shoulder abduction for the task of following randomly generated angular trajectories.
  arXiv  Detail & Related papers  (2020-07-13T20:20:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.