Optimizing Execution of Dynamic Goal-Directed Robot Movements with Learning Control

07/05/2018
by   Okan Koc, et al.
0

Highly dynamic tasks that require large accelerations and precise tracking usually rely on accurate models and/or high gain feedback. While kinematic optimization allows for efficient representation and online generation of hitting trajectories, learning to track such dynamic movements with inaccurate models remains an open problem. In particular, stability issues surrounding the learning performance, in the iteration domain, can prevent the successful implementation of model based learning approaches. To achieve accurate tracking for such tasks in a stable and efficient way, we propose a new adaptive Iterative Learning Control (ILC) algorithm that is implemented efficiently using a recursive approach. Moreover, covariance estimates of model matrices are used to exercise caution during learning. We evaluate the performance of the proposed approach in extensive simulations and in our robotic table tennis platform, where we show how the striking performance of two seven degree of freedom anthropomorphic robot arms can be optimized. Our implementation on the table tennis platform compares favorably with high-gain PD-control, model-free ILC (simple PD feedback type) and model-based ILC without cautious adaptation.

READ FULL TEXT

page 1

page 11

research
03/15/2020

Robot Playing Kendama with Model-Based and Model-Free Reinforcement Learning

Several model-based and model-free methods have been proposed for the ro...
research
06/10/2020

Learning to Play Table Tennis From Scratch using Muscular Robots

Dynamic tasks like table tennis are relatively easy to learn for humans ...
research
07/25/2021

Adaptive Identification of Legged Robotic Kinematic Structure

Model-based control usually relies on an accurate model, which is often ...
research
06/24/2022

Bioinspired composite learning control under discontinuous friction for industrial robots

Adaptive control can be applied to robotic systems with parameter uncert...
research
02/28/2023

Model-Free and Learning-Free Proprioceptive Humanoid Movement Control

This paper presents a novel model-free method for humanoid-robot quasi-s...
research
12/07/2021

Bridging the Model-Reality Gap with Lipschitz Network Adaptation

As robots venture into the real world, they are subject to unmodeled dyn...
research
03/13/2019

Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach

Nowadays, autonomous service robots are becoming an important topic in r...

Please sign up or login with your details

Forgot password? Click here to reset