Title
Rlbench: The Robot Learning Benchmark & Learning Environment
Abstract
We present a challenging new benchmark and learning-environment for robot learning: RLBench. The benchmark features 100 completely unique, hand-designed tasks, ranging in difficulty from simple target reaching and door opening to longer multi-stage tasks, such as opening an oven and placing a tray in it. We provide an array of both proprioceptive observations and visual observations, which include rgb, depth, and segmentation masks from an over-the-shoulder stereo camera and an eye-in-hand monocular camera. Uniquely, each task comes with an infinite supply of demos through the use of motion planners operating on a series of waypoints given during task creation time; enabling an exciting flurry of demonstration-based learning possibilities. RLBench has been designed with scalability in mind; new tasks, along with their motion-planned demos, can be easily created and then verified by a series of tools, allowing users to submit their own tasks to the RLBench task repository. This large-scale benchmark aims to accelerate progress in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning. With the benchmark's breadth of tasks and demonstrations, we propose the first large-scale few-shot challenge in robotics. We hope that the scale and diversity of RLBench offers unparalleled research opportunities in the robot learning community and beyond. Benchmarking code and videos can be found at https://sites.google.com/view/rlbench.
Year
DOI
Venue
2020
10.1109/LRA.2020.2974707
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Task analysis, Benchmark testing, Cameras, Learning (artificial intelligence), Tools, Robot vision systems, Learning from demonstration, deep learning in robotics and automation, performance evaluation and benchmarking, perception for grasping and manipulation
Journal
5
Issue
ISSN
Citations 
2
2377-3766
4
PageRank 
References 
Authors
0.42
0
4
Name
Order
Citations
PageRank
Stephen James140.42
Zicong Ma240.42
David Rovick Arrojo340.42
Andrew J. Davison46707350.85