CPG-ACTOR: Reinforcement Learning for Central Pattern Generators

02/25/2021
by   Luigi Campanaro, et al.
0

Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. Although conceptually promising, we argue that the full potential of CPGs has so far been limited by insufficient sensory-feedback information. This paper proposes a new methodology that allows tuning CPG controllers through gradient-based optimization in a Reinforcement Learning (RL) setting. To the best of our knowledge, this is the first time CPGs have been trained in conjunction with a MultilayerPerceptron (MLP) network in a Deep-RL context. In particular, we show how CPGs can directly be integrated as the Actor in an Actor-Critic formulation. Additionally, we demonstrate how this change permits us to integrate highly non-linear feedback directly from sensory perception to reshape the oscillators' dynamics. Our results on a locomotion task using a single-leg hopper demonstrate that explicitly using the CPG as the Actor rather than as part of the environment results in a significant increase in the reward gained over time (6x more) compared with previous approaches. Furthermore, we show that our method without feedback reproduces results similar to prior work with feedback. Finally, we demonstrate how our closed-loop CPG progressively improves the hopping behaviour for longer training epochs relying only on basic reward functions.

READ FULL TEXT

page 1

page 6

10/03/2018

Comparison of Reinforcement Learning algorithms applied to the Cart Pole problem

Designing optimal controllers continues to be challenging as systems are...
04/07/2022

Hybrid LMC: Hybrid Learning and Model-based Control for Wheeled Humanoid Robot via Ensemble Deep Reinforcement Learning

Control of wheeled humanoid locomotion is a challenging problem due to t...
09/18/2019

A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming

Recent successes of Reinforcement Learning (RL) allow an agent to learn ...
06/22/2016

Simultaneous Control and Human Feedback in the Training of a Robotic Agent with Actor-Critic Reinforcement Learning

This paper contributes a preliminary report on the advantages and disadv...
06/12/2021

Recomposing the Reinforcement Learning Building Blocks with Hypernetworks

The Reinforcement Learning (RL) building blocks, i.e. Q-functions and po...
06/14/2022

Open-Ended Learning Strategies for Learning Complex Locomotion Skills

Teaching robots to learn diverse locomotion skills under complex three-d...
11/25/2020

Symmetry-Aware Actor-Critic for 3D Molecular Design

Automating molecular design using deep reinforcement learning (RL) has t...