Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation

• URL: http://arxiv.org/abs/2312.05473v4
• Date: Sat, 25 May 2024 16:26:22 GMT
• Title: Self Model for Embodied Intelligence: Modeling Full-Body Human Musculoskeletal System and Locomotion Control with Hierarchical Low-Dimensional Representation
• Authors: Chenhui Zuo, Kaibo He, Jing Shao, Yanan Sui,
• Abstract summary: We build a musculoskeletal model (MS-Human-700) with 90 body segments, 206 joints, and 700 muscle-tendon units. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control.
• Score: 22.925312305575183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling and control of the human musculoskeletal system is important for understanding human motor functions, developing embodied intelligence, and optimizing human-robot interaction systems. However, current human musculoskeletal models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model (MS-Human-700) with 90 body segments, 206 joints, and 700 muscle-tendon units, allowing simulation of full-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art full-body control. We validate the effectiveness of our model and algorithm in simulations with real human locomotion data. The musculoskeletal model, along with its control algorithm, will be made available to the research community to promote a deeper understanding of human motion control and better design of interactive robots. Project page: https://lnsgroup.cc/research/MS-Human-700

Related papers

• Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
  We introduce a novel system for joint learning between human operators and robots. It enables human operators to share control of a robot end-effector with a learned assistive agent. It also allows the human operator to adjust the control ratio to achieve a trade-off between manual and automated control.
  arXiv  Detail & Related papers  (2024-06-29T03:37:29Z)
• HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
  We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands. Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
  arXiv  Detail & Related papers  (2024-03-15T17:45:44Z)
• Expressive Whole-Body Control for Humanoid Robots [20.132927075816742]
  We learn a whole-body control policy on a human-sized robot to mimic human motions as realistic as possible. With training in simulation and Sim2Real transfer, our policy can control a humanoid robot to walk in different styles, shake hands with humans, and even dance with a human in the real world.
  arXiv  Detail & Related papers  (2024-02-26T18:09:24Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Hierarchical generative modelling for autonomous robots [8.023920215148486]
  We show how a humanoid robot can autonomously complete a complex task that requires a holistic use of locomotion, manipulation, and grasping. Specifically, we demonstrate the ability of a humanoid robot that can retrieve and transport a box, open and walk through a door to reach the destination, approach and kick a football, while showing robust performance in presence of body damage and ground irregularities.
  arXiv  Detail & Related papers  (2023-08-15T13:51:03Z)
• HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
  We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
  arXiv  Detail & Related papers  (2022-12-08T15:56:13Z)
• Learning body models: from humans to humanoids [2.855485723554975]
  Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. Key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. mechanisms of operation of body models in the brain are largely unknown and even less is known about how they are constructed from experience after birth.
  arXiv  Detail & Related papers  (2022-11-06T07:30:01Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• Motron: Multimodal Probabilistic Human Motion Forecasting [30.154996245556532]
  Motron is a graph-structured model that captures human's multimodality. It outputs deterministic motions and corresponding confidence values for each mode. We demonstrate the performance of our model on several challenging real-world motion forecasting datasets.
  arXiv  Detail & Related papers  (2022-03-08T14:58:41Z)
• Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity [0.0]
  We learn a control policy using a motor babbling approach as implemented in reinforcement learning. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal with the curse of dimensionality, we use a simplified second-order muscle model, acting at each degree of freedom instead of individual muscles.
  arXiv  Detail & Related papers  (2020-11-13T19:49:29Z)

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.