Learning Variable Impedance Skills from Demonstrations with Passivity
Guarantee
- URL: http://arxiv.org/abs/2306.11308v1
- Date: Tue, 20 Jun 2023 06:05:04 GMT
- Title: Learning Variable Impedance Skills from Demonstrations with Passivity
Guarantee
- Authors: Yu Zhang, Long Cheng, Xiuze Xia, and Haoyu Zhang
- Abstract summary: This paper presents a learning-from-demonstration framework that integrates force sensing and motion information to facilitate variable impedance control.
A novel tank based variable impedance control approach is proposed to ensure passivity by using the learned stiffness.
- Score: 13.446072103907971
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Robots are increasingly being deployed not only in workplaces but also in
households. Effectively execute of manipulation tasks by robots relies on
variable impedance control with contact forces. Furthermore, robots should
possess adaptive capabilities to handle the considerable variations exhibited
by different robotic tasks in dynamic environments, which can be obtained
through human demonstrations. This paper presents a learning-from-demonstration
framework that integrates force sensing and motion information to facilitate
variable impedance control. The proposed approach involves the estimation of
full stiffness matrices from human demonstrations, which are then combined with
sensed forces and motion information to create a model using the non-parametric
method. This model allows the robot to replicate the demonstrated task while
also responding appropriately to new task conditions through the use of the
state-dependent stiffness profile. Additionally, a novel tank based variable
impedance control approach is proposed to ensure passivity by using the learned
stiffness. The proposed approach was evaluated using two virtual variable
stiffness systems. The first evaluation demonstrates that the stiffness
estimated approach exhibits superior robustness compared to traditional methods
when tested on manual datasets, and the second evaluation illustrates that the
novel tank based approach is more easily implementable compared to traditional
variable impedance control approaches.
Related papers
- Distributed Robust Learning based Formation Control of Mobile Robots based on Bioinspired Neural Dynamics [14.149584412213269]
We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance.
Then, a kinematic tracking control method is developed utilizing a bioinspired neural dynamic-based approach aimed at providing smooth control inputs and effectively resolving the speed jump issue.
To address the challenges for robots operating with completely unknown dynamics and disturbances, a learning-based robust dynamic controller is developed.
arXiv Detail & Related papers (2024-03-23T04:36:12Z) - SWBT: Similarity Weighted Behavior Transformer with the Imperfect
Demonstration for Robotic Manipulation [32.78083518963342]
We propose a novel framework named Similarity Weighted Behavior Transformer (SWBT)
SWBT effectively learn from both expert and imperfect demonstrations without interaction with environments.
We are the first to attempt to integrate imperfect demonstrations into the offline imitation learning setting for robot manipulation tasks.
arXiv Detail & Related papers (2024-01-17T04:15:56Z) - DiAReL: Reinforcement Learning with Disturbance Awareness for Robust
Sim2Real Policy Transfer in Robot Control [0.0]
Delayed Markov decision processes fulfill the Markov property by augmenting the state space of agents with a finite time window of recently committed actions.
We introduce a disturbance-augmented Markov decision process in delayed settings as a novel representation to incorporate disturbance estimation in training on-policy reinforcement learning algorithms.
arXiv Detail & Related papers (2023-06-15T10:11:38Z) - Value function estimation using conditional diffusion models for control [62.27184818047923]
We propose a simple algorithm called Diffused Value Function (DVF)
It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model.
We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
arXiv Detail & Related papers (2023-06-09T18:40:55Z) - Exploiting Symmetry and Heuristic Demonstrations in Off-policy
Reinforcement Learning for Robotic Manipulation [1.7901837062462316]
This paper aims to define and incorporate the natural symmetry present in physical robotic environments.
The proposed method is validated via two point-to-point reaching tasks of an industrial arm, with and without an obstacle.
A comparison study between the proposed method and a traditional off-policy reinforcement learning algorithm indicates its advantage in learning performance and potential value for applications.
arXiv Detail & Related papers (2023-04-12T11:38:01Z) - On Robust Numerical Solver for ODE via Self-Attention Mechanism [82.95493796476767]
We explore training efficient and robust AI-enhanced numerical solvers with a small data size by mitigating intrinsic noise disturbances.
We first analyze the ability of the self-attention mechanism to regulate noise in supervised learning and then propose a simple-yet-effective numerical solver, Attr, which introduces an additive self-attention mechanism to the numerical solution of differential equations.
arXiv Detail & Related papers (2023-02-05T01:39:21Z) - Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model
Predictive Control [49.60520501097199]
We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems.
Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
arXiv Detail & Related papers (2022-10-23T00:45:05Z) - End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and
Compliant Impedance Control [16.88250694156719]
We present a novel hybrid model formulation that enables us to identify fully physically consistent inertial parameters of a rigid body dynamics model.
We compare our approach against state-of-the-art inverse dynamics models on a 7 degree of freedom manipulator.
arXiv Detail & Related papers (2022-05-27T07:39:28Z) - Visual Adversarial Imitation Learning using Variational Models [60.69745540036375]
Reward function specification remains a major impediment for learning behaviors through deep reinforcement learning.
Visual demonstrations of desired behaviors often presents an easier and more natural way to teach agents.
We develop a variational model-based adversarial imitation learning algorithm.
arXiv Detail & Related papers (2021-07-16T00:15:18Z) - Learning to Shift Attention for Motion Generation [55.61994201686024]
One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query.
Previous approaches fail to capture all modes or tend to average modes of the demonstrations and thus generate invalid trajectories.
We propose a motion generation model with extrapolation ability to overcome this problem.
arXiv Detail & Related papers (2021-02-24T09:07:52Z) - Efficient Empowerment Estimation for Unsupervised Stabilization [75.32013242448151]
empowerment principle enables unsupervised stabilization of dynamical systems at upright positions.
We propose an alternative solution based on a trainable representation of a dynamical system as a Gaussian channel.
We show that our method has a lower sample complexity, is more stable in training, possesses the essential properties of the empowerment function, and allows estimation of empowerment from images.
arXiv Detail & Related papers (2020-07-14T21:10:16Z)
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.