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RLBench: The Robot Learning Benchmark & Learning Environment
Stephen James, Zicong Ma, David Rovick Arrojo, Andrew J. Davison
TL;DR
Robot-learning research lacks a broad benchmark for diverse tasks and few-shot generalization. RLBench addresses this with 100 unique tasks, multimodal observations, generated demonstrations, and extensible tools, establishing a large-scale platform and few-shot challenge for manipulation research.
Problem
Robot manipulation research lacks a standard benchmark spanning diverse methods and a way to evaluate few-shot learning across tasks.
Method
RLBench constructs a 100-task learning environment with multimodal observations, textual task variations, infinite planner-generated episodes, and tools for creating and validating new tasks.
Results
RLBench provides a broad benchmark and learning environment for robot manipulation research, including a large-scale few-shot challenge.
Takeaways & Limitations
The benchmark is intended to support reinforcement, imitation, few-shot, multi-task, and geometric robot-learning research through diverse tasks and demonstrations.
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
RLBench addresses the need for a broad, reusable benchmark spanning diverse robot-learning and traditional manipulation methods. It combines 100 unique tasks, rich observations, scalable demonstrations, and tools for few-shot evaluation and task creation.
- RLBench provides a one-size-fits-all benchmark for comparing robot-learning and traditional manipulation methods.
- 100 unique, hand-designed tasks span different difficulties and share a common robot arm with visual and proprioceptive observations.
- Each task includes textual descriptions and an infinite set of demonstrations generated through waypoint-based motion planning.
- Open-source tools support rapid creation of new tasks, enabling the benchmark to expand over time.
- The benchmark proposes a large-scale few-shot challenge using M training tasks, N unseen tasks, and K demonstrations per unseen task.
II. RELATED WORK
Existing benchmarks often target narrow manipulation sub-problems, single task families, or limited data settings. RLBench instead supports whole-system evaluation across 100 diverse tasks while providing scalable simulated observations and demonstrations.
- Many existing manipulation benchmarks focus on isolated perception, grasping, or planning sub-problems.
- RLBench evaluates complete manipulation systems across 100 unique tasks rather than concentrating on a single task family such as picking and stowing.
- Unlike competitions with little large-scale data, RLBench supports comparisons between reinforcement-learning, end-to-end, and classical approaches.
- RoboTurk provides only three tasks, whereas RLBench generates an infinite supply of demonstrations through motion planners.
- RLBench uses visual and proprioceptive observations, including RGB, depth, segmentation masks, joint states, torques, and end-effector pose.
III. BENCHMARK PROPERTIES
RLBench is designed as a diverse, reproducible, scalable, extensible, realistic, and tiered-difficulty benchmark for robot learning. Its visual observations and task-generation tools support both data-intensive learning and classical robotic methods.
- Diversity: Diversity is intended to support general algorithms and reduce over-fitting by exposing models to inter-task relationships.
- Reproducibility: Simulation improves reproducibility across laboratories, while leaving open the question of how well learned solutions transfer to the real world.
- Scale: RLBench targets scale through a large task collection and the ability to produce many demonstrations from those tasks.
- Extensibility: PyRep enables users to build a broad range of new tasks quickly, supporting continued growth of the task repository.
- Tiered Difficulty: Tasks span easy reaching problems through challenging long-time-horizon tasks that can stress-test established algorithms.
- Realism: The simulator prioritizes realistic robot models, lighting and shadows, and domain-randomized rendering, but does not claim full photorealism or generally realistic physics.
IV. RLBENCH
RLBench is built on V-REP and PyRep as a long-term, extensible benchmark and learning environment. It organizes benchmark instances around tasks, variations, and episodes, with variation-specific textual descriptions and episode-level positional changes.
- RLBENCH: RLBench uses V-REP and PyRep to build a benchmark and learning environment intended to grow over many years.
- RLBENCH: A task contains V variations, and each variation contains E episodes in the illustrated ‘stack blocks’ example.
- RLBENCH: Variation descriptions summarize objectives, while variations typically change target objects or colours and episodes change positions.
A. Scene
The RLBench scene uses a fixed simulated Panda-arm setup and provides visual and proprioceptive observations. Tasks are organized into variations and episodes, with variation abstractions grouping closely related objectives.
- Scene: The fixed scene contains a 7 DoF Franka Emika Panda arm on a wooden table surrounded by 3 directional lights.
- Scene: Visual observations come from stereo and wrist cameras, while proprioception includes joint angles, velocities, torques, and end-effector pose.
- Tasks, Variations & Episodes: Each task has one or more variations, each variation yields infinitely many episodes, and episodes are observation-action trajectories sampled from a variation.
- Tasks, Variations & Episodes: Variations group closely related cases such as picking up different objects, making task definitions more reusable when generating many variants.
C. Environment
RLBench exposes a reinforcement-learning-style environment interface with sparse task-completion rewards and multiple action-space parameterizations.
- Environment: Users access tasks through an Environment class that can spawn TaskEnvironment instances for selected tasks.
- Environment: Each task provides a completely sparse reward of +1 only when the task is completed.
- Environment: Available actions include absolute or delta commands for joint velocities, joint positions, torques, end-effector velocities, and end-effector poses.
D. Demonstrations
RLBench supplies expert motion-planning policies for each task variation, enabling demonstration episodes to be generated for learning.
- RLBench provides an expert algorithm π∗ for each task and its corresponding variations.The demonstrations are generated from episodes produced via the Open Motion Planning Library.
- Motion-planned episodes serve as demonstrations for robot-learning methods.The framework uses task-specific expert policies to produce demonstration data.
- Demonstrations can be generated across different task variations rather than for only one fixed task instance.The expert algorithm is associated with each task and its corresponding variations.
E. Task Builder
RLBench provides an environment API and task-building workflow for configuring, creating, randomizing, validating, and contributing robot-manipulation tasks.
- Environment interface: The environment API lets users instantiate an environment, select tasks, and configure action modes such as joint-velocity control.The example imports Environment, ActionMode, and ReachTarget before selecting an absolute joint-velocity action mode.
- Task creation: The task-building tool combines a V-REP scene file with Python code that defines variations, success criteria, and task behavior.Python task files connect scene objects to the RLBench backend and can add more complex behaviors.
- Validation and contribution: A validation tool collects demonstrations to check whether the designed task’s path planning fails only a small number of times.After validation passes, users may contribute the task to the growing repository through a GitHub pull request.
- Task creation: Task authors can edit scenes and behaviors together, including conditions that require both object detection and grasping for success.The saucepan-lid example also randomizes the task position at the beginning of each episode.
V. THE RLBENCH FEW-SHOT CHALLENGE (v 1.0)
RLBench defines a large-scale few-shot challenge over diverse tasks, with demonstrations for unseen tasks and evaluation on new configurations. Its versioned design is intended to keep results reproducible as the benchmark expands.
- Motivation: RLBench addresses the lack of a way to evaluate and compare few-shot learning methods for robotics.The paper emphasizes learning new tasks quickly from knowledge acquired on previously learned tasks.
- Motivation: The challenge uses diverse tasks because prior few-shot robotics work often treated small task variations as separate tasks.The paper argues that truly general algorithms require a broader range of tasks for training and testing.
- Protocol: 100 unique tasks are split into a 10% meta-test set spanning difficulties and a remaining meta-train set.The train-test splits are made available on the benchmark webpage.
- Data and scope: RLBench supplies pre-generated demonstrations while also allowing demonstrations to be generated on the fly or created by users.The benchmark’s tasks range from 100 to 1000 timesteps, with longer tasks composing multiple actions.
- Protocol: At test time, systems receive K demonstrations of an unseen task and report success on new episodes using 1-shot, 5-shot, and 20-shot settings.No prior knowledge of the unseen task may be provided beyond information included in training tasks.
- Versioning: Version 1.0 is intended to evolve as the number and breadth of tasks grow, preserving meaningful and reproducible comparisons.The paper notes that several state-of-the-art few-shot learning families had not been tested at this scale.
VI. OTHER APPLICATIONS & CHALLENGES
RLBench is positioned as a shared platform for visually guided manipulation research beyond few-shot learning, including reinforcement learning, imitation learning, sim-to-real transfer, multi-task learning, and SLAM.
- Reinforcement learning: RLBench supports visually guided reinforcement learning with demonstrations, eye-in-hand observations, and real-world-based manipulation tasks.These features support bootstrapping policies with demonstrations and research on partial observability or incremental estimation.
- Imitation learning: RLBench improves imitation-learning reproducibility by shipping demonstrations and allowing an effectively unlimited supply to be generated on the fly.Many imitation-learning studies otherwise design their own evaluation tasks.
- Sim-to-real transfer: The simulated Franka Panda can be replaced with another laboratory arm in one line of code, enabling sim-to-real methods to be compared on standard tasks.The task-building and demonstration tools also support designing new tasks for particular sim-to-real features.
- Multi-task learning: Multi-task learning on RLBench can provide insights relevant to improving performance in the few-shot domain.The multi-task setup trains on meta-training and meta-testing tasks and evaluates generalization to unseen examples of those tasks.
- SLAM: RLBench can facilitate tighter integration of SLAM and manipulation by providing task-based manipulation settings for studying map representation and required accuracy.The paper identifies sparse, dense, and semidense maps as alternative representations whose utility remains unclear.
VII. SUMMARY AND FUTURE WORK
RLBench is presented as a broad robot-manipulation benchmark intended to accelerate research and support a few-shot learning challenge. The authors also anticipate early platform issues and plan ongoing maintenance and task expansion.
- RLBench targets broad robotic-manipulation research and introduces a few-shot learning challenge.The authors frame it as a large-scale benchmark and learning environment for multiple research areas.
- The platform may experience teething problems during launch, requiring continuous maintenance and improvement.
- The authors plan to expand the available training and evaluation tasks with community support.