RLBench: The Robot Learning Benchmark & Learning Environment (1909.12271v1)

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. 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.

• The paper introduces RLBench as a comprehensive benchmark offering 100 unique robotic manipulation tasks to standardize performance evaluation.
• It employs diverse observations—including RGB, depth, and segmentation data—and an infinite supply of motion-planner demonstrations for robust learning.
• The benchmark promotes scalability and few-shot learning by enabling seamless integration of new tasks, advancing generalization in robotic systems.

An Overview of RLBench: A Comprehensive Robot Learning Benchmark

The paper by James et al. introduces RLBench, a novel and expansive benchmark and learning environment tailored for research in robotic manipulation. This paper presents RLBench as a means to bridge the gap between traditional robotic manipulation methodologies and contemporary deep-learning-based approaches, thereby providing a standard platform for evaluating and comparing various techniques within the community.

Key Contributions and Framework

RLBench offers a suite of 100 unique, hand-designed tasks that span a range of difficulties. The tasks are designed to encompass both simple tasks such as target reaching and complex, multi-stage activities like opening an oven and inserting a tray. RLBench incorporates proprioceptive and visual observations, including RGB, depth, and segmentation data from multiple camera viewpoints. An innovative aspect of RLBench is its provision of an infinite supply of demonstrations via motion planners using waypoints set at task creation, which facilitates robust demonstration-based learning.

A crucial element of RLBench is its scalability. This benchmark allows users to create and integrate new tasks seamlessly into the RLBench task repository. Through a set of open-source tools, the benchmark ensures that task generation remains both accessible and verifiable, thus encouraging community involvement and growth.

Research Implications

The paper delineates several domains within robotic research that RLBench aims to accelerate, including reinforcement learning, imitation learning, multi-task learning, and geometric computer vision. Of particular note is the benchmark’s emphasis on few-shot learning, introducing a large-scale few-shot challenge to the robotics field. This emphasis reflects a growing interest in algorithms that can generalize from minimal examples, akin to human learning.

The benchmark's integration of diverse tasks and demonstrations offers opportunities for developing generalizable agents capable of performing a wide array of tasks requiring vision-guided manipulation. The authors propose RLBench as a platform to unify traditional robotics and learning methods, thereby enabling cross-pollination of ideas and approaches.

Prospects and Considerations

Looking forward, RLBench could serve as a pivotal resource in pushing the boundaries of robotic capabilities, especially in learning from demonstrations and task generalization. The scalability and extensibility intrinsic to RLBench allow it to evolve alongside advancements in robotics and machine learning, ensuring its long-term relevance in academic and practical settings.

For future developments, further exploration into seamlessly transitioning learned policies from the simulated environments of RLBench to real-world applications could be a significant avenue of research. Improvements in sim-to-real transfer methods could leverage the high-quality rendering and modelling in RLBench to produce robust, adaptable real-world systems.

Conclusion

RLBench represents a significant contribution to robotic learning, promising to standardize evaluation across various domains and assist in the development of versatile robotic agents. By providing a comprehensive and scalable benchmark, this paper lays the groundwork for a rich ecosystem of research and applications in robotics, propelling the community towards more sophisticated, adaptable, and efficient systems.