Using Tactile Sensing to Improve the Sample Efficiency and Performance of Deep Deterministic Policy Gradients for Simulated In-Hand Manipulation Tasks
Open Access
- 29 June 2021
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Robotics and AI
- Vol. 8, 538773
- https://doi.org/10.3389/frobt.2021.538773
Abstract
Deep Reinforcement Learning techniques demonstrate advances in the domain of robotics. One of the limiting factors is a large number of interaction samples usually required for training in simulated and real-world environments. In this work, we demonstrate for a set of simulated dexterous in-hand object manipulation tasks that tactile information can substantially increase sample efficiency for training (by up to more than threefold). We also observe an improvement in performance (up to 46%) after adding tactile information. To examine the role of tactile-sensor parameters in these improvements, we included experiments with varied sensor-measurement accuracy (ground truth continuous values, noisy continuous values, Boolean values), and varied spatial resolution of the tactile sensors (927 sensors, 92 sensors, and 16 pooled sensor areas in the hand). To facilitate further studies and comparisons, we make these touch-sensor extensions available as a part of the OpenAI Gym Shadow-Dexterous-Hand robotics environments.This publication has 12 references indexed in Scilit:
- Deep Reinforcement Learning for Tactile Robotics: Learning to Type on a Braille KeyboardIEEE Robotics and Automation Letters, 2020
- Learning dexterous in-hand manipulationThe International Journal of Robotics Research, 2019
- Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal EnvironmentsPublished by Springer Science and Business Media LLC ,2018
- An Approach to Hierarchical Deep Reinforcement Learning for a Decentralized Walking Control ArchitecturePublished by Springer Science and Business Media LLC ,2018
- More Than a Feeling: Learning to Grasp and Regrasp Using Vision and TouchIEEE Robotics and Automation Letters, 2018
- EEG correlates of sensorimotor processing: independent components involved in sensory and motor processingScientific Reports, 2017
- Flexible and stretchable fabric-based tactile sensorRobotics and Autonomous Systems, 2015
- Approaching Manual IntelligenceKI - Künstliche Intelligenz, 2010
- Hand movements: A window into haptic object recognitionCognitive Psychology, 1987
- Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objectsExperimental Brain Research, 1984