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This work re-implements the OpenAI Gym multi-goal robotic manipulationenvironment, originally based on the commercial Mujoco engine, onto theopen-source Pybullet engine. By comparing the performances of the HindsightExperience Replay-aided Deep Deterministic Policy Gradient agent on bothenvironments, we demonstrate our successful re-implementation of the originalenvironment. Besides, we provide users with new APIs to access a joint controlmode, image observations and goals with customisable camera and a built-inon-hand camera. We further design a set of multi-step, multi-goal, long-horizonand sparse reward robotic manipulation tasks, aiming to inspire newgoal-conditioned reinforcement learning algorithms for such challenges. We usea simple, human-prior-based curriculum learning method to benchmark themulti-step manipulation tasks. Discussions about future research opportunitiesregarding this kind of tasks are also provided.

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