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PyRep: Bringing V-REP to Deep Robot Learning
Stephen James, Marc Freese, Andrew J. Davison
TL;DR
Robot learning needs rich data from complex environments, but collecting it can be time-consuming, and deep learning requires large training datasets. PyRep adapts V-REP with a Python API, faster simulation control, and a new renderer; it reports speed boosts upwards of 10,000× over the original V-REP Python API. The authors present it as a toolkit for the robot learning community.
Problem
Robot learning needs varied data from complex, dynamic environments, but collecting it is time-consuming and deep learning requires large training datasets.
Source: §1 · p. 1Method
PyRep modifies V-REP for robot learning with a Python API, direct Python control of the simulation loop, an object-oriented interface, and a new OpenGL 3.0+ renderer.
Source: §1 · p. 2, §3 · pp. 2–3Results
Upwards of 10,000× speed boosts over the original V-REP Python API are reported for PyRep.
Source: §1 · p. 2Takeaways & Limitations
The authors present PyRep as bringing V-REP’s capabilities to the robot learning community through a flexible API, faster runtime, and a new renderer.
Source: §4 · p. 4
Abstract
from arXiv · showhide
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.
1 Introduction
Robot learning needs varied data, but collecting it in dynamic environments is time-consuming; simulation can support prototyping or training and transfer. PyRep adapts V-REP for robot learning with a Python API, a new renderer, and speed boosts of up to 10,000× over its original Python API.
1 Introduction
- Collecting varied robot data in complex, dynamic environments is time-consuming, while deep learning requires large training datasets.The passage identifies simulation as a way to address this data-collection challenge.Source: §1 · p. 1
- Simulation can support rapid prototyping of data-efficient algorithms for small real-world datasets or training on simulation data with some real-world data and transfer.The transfer approach is described with references.Source: §1 · p. 1
- Physics engines such as Bullet and MuJoCo can make it cumbersome to build rich environments and integrate standard robotics tools.The listed tools include kinematics, user interfaces, motion libraries, and path planners.Source: §1 · p. 1
- V-REP makes robotics applications easy to design, but its Python remote API was not developed for large-scale data collection.The following passage identifies its speed as inadequate for rapid interaction in methods such as reinforcement learning.Source: §1 · pp. 1–2
- PyRep adds an intuitive Python API and renderer to a V-REP modified for Python communication, achieving speed boosts upwards of 10,000× over the original V-REP Python API.The toolkit is intended to bring V-REP to the robot learning community.Source: §1 · p. 2
2 Background
V-REP is a general-purpose robotics framework with broad simulation and control features, but its external APIs can be slow for data-hungry applications. The delay is especially relevant when reinforcement learning requires synchronous queries each timestep.
2 Background
- V-REP is a general-purpose robot simulation framework maintained by Coppelia Robotics.Its listed capabilities include cross-platform content, multiple communication options, four switchable physics engines, kinematics, and distributed control.Source: §2 · p. 2
- V-REP’s external APIs can be slow for data-hungry applications because calls rely on inter-thread communication, a delay relevant to synchronous timestep queries in reinforcement learning.Python and C++ are identified as primary languages for deep learning and robotics research.Source: §2 · p. 2
3 Modifications
PyRep modifies V-REP to reduce communication delays, introduces an OpenGL 3.0+ renderer with shadows, and provides an object-oriented Python API for simulation control and robot motion planning.
3 Modifications
- Direct Python control of the simulation loop removes inter-thread communication latency and made trajectory and episode collection over four orders of magnitude faster than with the original remote Python API.The change targets synchronous environment queries at each timestep, common in reinforcement learning.Source: §3 · p. 2
- The new OpenGL 3.0+ renderer supports shadows from all V-REP-supported lights, including directional, spotlight, and point lights.Figure 1 shows example environment renderings using the new renderer.Source: §3 · p. 3, Figure 1 · p. 3
- The object-oriented PyRep API manages simulation handles and lets users add robots with motion-planning capabilities using only a few lines of Python.Figure 2 provides an API example.Source: §3 · p. 3, Figure 2 · p. 3
4 Conclusion
PyRep brings V-REP to robot learning through a flexible API, faster runtime, and an OpenGL 3.0+ renderer.
4 Conclusion
- PyRep combines a simple, flexible API, significant runtime speedup, and an OpenGL 3.0+ renderer to bring V-REP to the robot learning community.The authors express interest in the tasks that robot learning methods may solve with the toolkit.Source: §4 · p. 4
- V-REP had been used extensively in traditional robotics research and development but was overlooked by the growing robot learning community.This is the audience gap PyRep is intended to address.Source: §4 · p. 4
- The authors point to future tasks for new robot learning methods as a reason to bring V-REP capabilities to this community.The conclusion does not identify specific tasks or report their results.Source: §4 · p. 4