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[02/17/2021] v1.2.0: Added observable sensor models 👀 and dynamics randomization 🎲
[12/17/2020] v1.1.0: Refactored infrastructure and standardized model classes for much easier environment prototyping 🔧
robosuite is a simulation framework powered by the MuJoCo physics engine for robot learning. It also offers a suite of benchmark environments for reproducible research. The current release (v1.2) features manipulation tasks with feature supports of procedural generation, advanced controllers, teleoperation, etc. This project is part of the broader Advancing Robot Intelligence through Simulated Environments (ARISE) Initiative, with the aim of lowering the barriers of entry for cutting-edge research at the intersection of AI and Robotics.
Data-driven algorithms, such as reinforcement learning and imitation learning, provide a powerful and generic tool in robotics. These learning paradigms, fueled by new advances in deep learning, have achieved some exciting successes in a variety of robot control problems. However, the challenges of reproducibility and the limited accessibility of robot hardware (especially during a pandemic) have impaired research progress. The overarching goal of robosuite is to provide researchers with:
- a standardized set of benchmarking tasks for rigorus evaluation and algorithm development;
- a modular design that offers great flexibility to design new robot simulation environments;
- a high-quality implementation of robot controllers and off-the-shelf learning algorithms to lower the barriers to entry.
This framework was originally developed since late 2017 by researchers in Stanford Vision and Learning Lab (SVL) as an internal tool for robot learning research. Now it is actively maintained and used for robotics research projects in SVL and the UT-Austin Robot Perception and Learning Lab (RPL). We welcome community contributions to this project. For details please check out our contributing guidelines.
This release of robosuite contains seven robot models, eight gripper models, six controller modes, and nine standardized tasks. It also offers a modular design of APIs for building new environments with procedural generation. We highlight these primary features below:
- standardized tasks: a set of standardized manipulation tasks of large diversity and varying complexity and RL benchmarking results for reproducible research;
- procedural generation: modular APIs for programmatically creating new environments and new tasks as a combinations of robot models, arenas, and parameterized 3D objects;
- controller supports: a selection of controller types to command the robots, such as joint-space velocity control, inverse kinematics control, operational space control, and 3D motion devices for teleoperation;
- multi-modal sensors: heterogeneous types of sensory signals, including low-level physical states, RGB cameras, depth maps, and proprioception;
- human demonstrations: utilities for collecting human demonstrations, replaying demonstration datasets, and leveraging demonstration data for learning.
Please cite robosuite if you use this framework in your publications:
@inproceedings{robosuite2020,
title={robosuite: A Modular Simulation Framework and Benchmark for Robot Learning},
author={Yuke Zhu and Josiah Wong and Ajay Mandlekar and Roberto Mart\'{i}n-Mart\'{i}n},
booktitle={arXiv preprint arXiv:2009.12293},
year={2020}
}