Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning

• URL: http://arxiv.org/abs/2103.08125v1
• Date: Mon, 15 Mar 2021 04:01:20 GMT
• Title: Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning
• Authors: Visak Kumar
• Abstract summary: We develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. We show that the method generalizes well across human-subjects with different kinematic structure and gait-characteristics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. At the core, our work leverages the ability of deep reinforcement learning methods to learn high-dimensional motor skills while being robust to variations in the environment dynamics. Our approach iterates between policy learning and parameter identification to match the real-world bio-mechanical human data. We present a thorough evaluation of the kinematics, kinetics and ground reaction forces generated by our learned virtual human agent. We also show that the method generalizes well across human-subjects with different kinematic structure and gait-characteristics.

Related papers

• Learning Multimodal Latent Dynamics for Human-Robot Interaction [19.803547418450236]
  This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI) We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines.
  arXiv  Detail & Related papers  (2023-11-27T23:56:59Z)
• CG-HOI: Contact-Guided 3D Human-Object Interaction Generation [29.3564427724612]
  We propose CG-HOI, the first method to generate dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion, as semantically rich human motion rarely happens in isolation. We show that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences.
  arXiv  Detail & Related papers  (2023-11-27T18:59:10Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• Deep state-space modeling for explainable representation, analysis, and generation of professional human poses [0.0]
  This paper introduces three novel methods for creating explainable representations of human movement. The trained models are used for the full-body dexterity analysis of expert professionals.
  arXiv  Detail & Related papers  (2023-04-13T08:13:10Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• 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)
• 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)
• Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models [29.34375999491465]
  A core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills to long-horizon tasks, robots should be able to learn and later refine their skills in a structured manner. We proposeSAC-GMM, a novel hybrid approach that learns robot skills through a dynamical system and adapts the learned skills in their own trajectory distribution space.
  arXiv  Detail & Related papers  (2021-11-25T15:36:11Z)
• Scene-aware Generative Network for Human Motion Synthesis [125.21079898942347]
  We propose a new framework, with the interaction between the scene and the human motion taken into account. Considering the uncertainty of human motion, we formulate this task as a generative task. We derive a GAN based learning approach, with discriminators to enforce the compatibility between the human motion and the contextual scene.
  arXiv  Detail & Related papers  (2021-05-31T09:05:50Z)
• Learning Bipedal Robot Locomotion from Human Movement [0.791553652441325]
  We present a reinforcement learning based method for teaching a real world bipedal robot to perform movements directly from motion capture data. Our method seamlessly transitions from training in a simulation environment to executing on a physical robot. We demonstrate our method on an internally developed humanoid robot with movements ranging from a dynamic walk cycle to complex balancing and waving.
  arXiv  Detail & Related papers  (2021-05-26T00:49:37Z)
• S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling [103.65625425020129]
  We represent the pedestrian's shape, pose and skinning weights as neural implicit functions that are directly learned from data. We demonstrate the effectiveness of our approach on various datasets and show that our reconstructions outperform existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-01-17T02:16:56Z)

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.