An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

05/12/2021 ∙ by Xintong Yang, et al. ∙ 0

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementation of the original environment. Besides, we provide users with new APIs to access a joint control mode, image observations and goals with customisable camera and a built-in on-hand camera. We further design a set of multi-step, multi-goal, long-horizon and sparse reward robotic manipulation tasks, aiming to inspire new goal-conditioned reinforcement learning algorithms for such challenges. We use a simple, human-prior-based curriculum learning method to benchmark the multi-step manipulation tasks. Discussions about future research opportunities regarding this kind of tasks are also provided.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 2

page 12

Code Repositories

pybullet_multigoal_gym

Pybullet version of the multigoal robotics environment from OpenAI Gym


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.