Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration

01/17/2023
by   Ilyes Gharbi, et al.
0

Automatic design is a promising approach to generating control software for robot swarms. So far, automatic design has relied on mission-specific objective functions to specify the desired collective behavior. In this paper, we explore the possibility to specify the desired collective behavior via demonstrations. We develop Demo-Cho, an automatic design method that combines inverse reinforcement learning with automatic modular design of control software for robot swarms. We show that, only on the basis of demonstrations and without the need to be provided with an explicit objective function, Demo-Cho successfully generated control software to perform four missions. We present results obtained in simulation and with physical robots.

READ FULL TEXT

page 3

page 5

research
08/05/2020

Learning from Sparse Demonstrations

This paper proposes an approach which enables a robot to learn an object...
research
05/29/2018

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

The design of a reward function often poses a major practical challenge ...
research
05/17/2023

Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning

The discovery of individual objectives in collective behavior of complex...
research
12/06/2022

Safe Inverse Reinforcement Learning via Control Barrier Function

Learning from Demonstration (LfD) is a powerful method for enabling robo...
research
10/31/2022

Learning Modular Robot Locomotion from Demonstrations

Modular robots can be reconfigured to create a variety of designs from a...
research
11/15/2017

IKBT: solving closed-form Inverse Kinematics with Behavior Tree

Serial robot arms have complicated kinematic equations which must be sol...

Please sign up or login with your details

Forgot password? Click here to reset