Learning Control Policies for Imitating Human Gaits
- URL: http://arxiv.org/abs/2106.15273v1
- Date: Sat, 15 May 2021 16:33:24 GMT
- Title: Learning Control Policies for Imitating Human Gaits
- Authors: Utkarsh A. Mishra
- Abstract summary: Humans exhibit movements like walking, running, and jumping in the most efficient manner, which served as the source of motivation for this project.
Skeletal and Musculoskeletal human models were considered for motions in the sagittal plane.
Model-free reinforcement learning algorithms were used to optimize inverse dynamics control actions.
- Score: 2.28438857884398
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The work presented in this report introduces a framework aimed towards
learning to imitate human gaits. Humans exhibit movements like walking,
running, and jumping in the most efficient manner, which served as the source
of motivation for this project. Skeletal and Musculoskeletal human models were
considered for motions in the sagittal plane, and results from both were
compared exhaustively. While skeletal models are driven with motor actuation,
musculoskeletal models perform through muscle-tendon actuation. Model-free
reinforcement learning algorithms were used to optimize inverse dynamics
control actions to satisfy the objective of imitating a reference motion along
with secondary objectives of minimizing effort in terms of power spent by
motors and metabolic energy consumed by the muscles. On the one hand, the
control actions for the motor actuated model is the target joint angles
converted into joint torques through a Proportional-Differential controller.
While on the other hand, the control actions for the muscle-tendon actuated
model is the muscle excitations converted implicitly to muscle activations and
then to muscle forces which apply moments on joints. Muscle-tendon actuated
models were found to have superiority over motor actuation as they are
inherently smooth due to muscle activation dynamics and don't need any external
regularizers. Finally, a strategy that was used to obtain an optimal
configuration of the significant decision variables in the framework was
discussed. All the results and analysis are presented in an illustrative,
qualitative, and quantitative manner. Supporting video links are provided in
the Appendix.
Related papers
- 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) - Persistent-Transient Duality: A Multi-mechanism Approach for Modeling
Human-Object Interaction [58.67761673662716]
Humans are highly adaptable, swiftly switching between different modes to handle different tasks, situations and contexts.
In Human-object interaction (HOI) activities, these modes can be attributed to two mechanisms: (1) the large-scale consistent plan for the whole activity and (2) the small-scale children interactive actions that start and end along the timeline.
This work proposes to model two concurrent mechanisms that jointly control human motion.
arXiv Detail & Related papers (2023-07-24T12:21:33Z) - Imposing Temporal Consistency on Deep Monocular Body Shape and Pose
Estimation [67.23327074124855]
This paper presents an elegant solution for the integration of temporal constraints in the fitting process.
We derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses.
Our approach enables the derivation of realistic 3D body models from image sequences, including facial expression and articulated hands.
arXiv Detail & Related papers (2022-02-07T11:11:55Z) - From Motion to Muscle [0.0]
We show that muscle activity can be artificially generated based on motion features such as position, velocity, and acceleration.
The model achieves remarkable precision for previously trained movements and maintains significantly high precision for new movements that have not been previously trained.
arXiv Detail & Related papers (2022-01-27T13:30:17Z) - OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical
Locomotion [8.849771760994273]
We release a 3D musculoskeletal simulation of an ostrich based on the MuJoCo simulator.
The model is based on CT scans and dissections used to gather actual muscle data.
We also provide a set of reinforcement learning tasks, including reference motion tracking and a reaching task with the neck.
arXiv Detail & Related papers (2021-12-11T19:58:11Z) - Targeted Muscle Effort Distribution with Exercise Robots: Trajectory and
Resistance Effects [1.2891210250935146]
The objective of this work is to relate muscle effort distributions to the trajectory and resistance settings of a robotic exercise and rehabilitation machine.
A four degrees-of-freedom robot and its impedance control system are used to create advanced exercise protocols.
arXiv Detail & Related papers (2021-07-02T21:07:35Z) - HuMoR: 3D Human Motion Model for Robust Pose Estimation [100.55369985297797]
HuMoR is a 3D Human Motion Model for Robust Estimation of temporal pose and shape.
We introduce a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence.
We demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset.
arXiv Detail & Related papers (2021-05-10T21:04:55Z) - Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle
Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics.
Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset.
Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
arXiv Detail & Related papers (2021-04-29T00:11:32Z) - 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) - 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.