PyRep: Bringing V-REP to Deep Robot Learning

By Stephen James, Marc Freese, Andrew J. Davison. 2019

130 citations recorded by the source corpus.

PyRep is a toolkit for robot learning research, built on top of the virtual robotics experimentation platform (V-REP). Through a series of modifications and additions, we have created a tailored version of V-REP built with robot learning in mind. The new PyRep toolkit offers three improvements: (1) a simple and flexible API for robot control and scene manipulation, (2) a new rendering engine, and (3) speed boosts upwards of 10,000x in comparison to the previous Python Remote API. With these improvements, we believe PyRep is the ideal toolkit to facilitate rapid prototyping of learning algorithms in the areas of reinforcement learning, imitation learning, state estimation, mapping, and computer vision.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

RLBench: The Robot Learning Benchmark & Learning Environment (2019, methodology): “new tasks (through the use of PyRep [12]) in order to improve”
Language-Conditioned Imitation Learning for Robot Manipulation Tasks (2020, methodology): “The evaluation was performed using CoppeliaSim [30, 17], which allowed for accurate dynamics simulations at an update rate of 20Hz.”
Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation (2022, methodology): “The simulation is set in CoppelaSim [71] and interfaced through PyRep [72].”