Roboschool

Roboschool
Name	Roboschool
Developer	OpenAI; originally by Ruslan Salakhutdinov et al.
Released	2016
Latest release	2017
Programming language	C++, Python
Operating system	Linux, macOS
License	MIT License

Roboschool is an open-source software framework for physics simulation and reinforcement learning environments that integrates with RL toolkits and continuous control algorithms. It provided alternative simulated robotics environments for researchers using popular libraries and interfaces to accelerate experimentation with model-free and model-based agents. The project bridged components from academic labs and industry projects to support benchmarking and reproducibility.

Overview

Roboschool aimed to provide lightweight, GPU-accelerated simulators compatible with interfaces used by OpenAI Gym, DeepMind research tools, Stanford University projects, and contributors from institutions such as Carnegie Mellon University, Massachusetts Institute of Technology, University of California, Berkeley, University of Oxford, ETH Zurich, University of Toronto, Princeton University, Harvard University, Google Research, Facebook AI Research, Microsoft Research, NVIDIA, Intel and IBM Research. It offered environments resembling tasks from Mujoco, Bullet (software), Gazebo (software), V-REP, and frameworks used in competitions like the DARPA Robotics Challenge, RoboCup, DARPA Grand Challenge, Amazon Robotics Challenge, Kaggle reinforcement learning contests, NeurIPS benchmarks, and ICML workshop datasets. The project interfaced with algorithms from papers associated with authors affiliated with DeepMind Technologies, OpenAI Scholars Program, Berkeley Artificial Intelligence Research, and labs behind the DQN and TRPO publications.

History and Development

Roboschool emerged in the context of growing interest at venues such as NeurIPS, ICLR, ICML, and AAAI where research groups from DeepMind, OpenAI, Google Brain, Facebook AI Research, Deep Reinforcement Learning (DRL) community, Stanford AI Lab, Berkeley AI Research (BAIR), and Mila sought open, fast simulators. Early contributors included engineers connected to OpenAI, academics who collaborated with University of Toronto and Carnegie Mellon University, and maintainers who previously worked on Bullet (software) and Mujoco. The repository attracted pull requests from developers at NVIDIA Research, Intel Labs, Microsoft Research Cambridge, Amazon Web Services, Uber AI Labs, Waymo, and teams preparing submissions to NeurIPS and ICML competitions. After initial releases, maintenance slowed as alternative simulators and licensing options evolved in projects associated with DeepMind Control Suite, PyBullet, Isaac Gym, and commercial platforms produced by NVIDIA and Unity Technologies.

Software Architecture and Components

Roboschool combined native code with bindings used in research stacks like OpenAI Gym and reinforcement learning libraries developed at Berkeley AI Research, DeepMind, OpenAI Baselines, Stable Baselines, Ray (software), and RLlib. Its core used collision and dynamics code influenced by projects at Bullet (software), and it produced Python wrappers for use with TensorFlow, PyTorch, JAX, Keras, and toolchains from Anaconda. Build systems leveraged standards from CMake and integration patterns familiar to contributors from GitHub, GitLab, Bitbucket, Travis CI, CircleCI, and continuous integration used by academic groups at MIT CSAIL and Oxford Robotics Institute. Components included environment definitions, rendering hooks compatible with SDL, OpenGL, and data logging adapters used by TensorBoard, Weights & Biases, and experiment trackers developed at Papers With Code contributors.

Supported Environments and Tasks

Roboschool provided simulated control tasks resembling traditional benchmarks like those used in OpenAI Gym and environments seen in publications from DeepMind Control Suite authors. Typical tasks included locomotion inspired by datasets and studies from CMU Robotics Institute, manipulation scenarios common in Stanford Robotics Lab work, and multi-joint control tasks explored by researchers at ETH Zurich, EPFL, Max Planck Institute for Intelligent Systems, and MPI-SWS. Environments targeted continuous control algorithms such as those evaluated in papers by John Schulman, Sergey Levine, Pieter Abbeel, David Silver, Shane Legg, Richard Sutton, and teams from DeepMind and OpenAI. Benchmarked tasks paralleled problems featured at RoboCup and simulated challenges prepared by DARPA programs.

Performance and Benchmarks

Benchmarks for Roboschool were reported in community repositories and compared against engines like Mujoco and PyBullet in studies authored by groups from UC Berkeley, DeepMind, OpenAI, CMU, Stanford, Princeton University, Harvard University, ETH Zurich, Max Planck Institute, and NVIDIA Research. Performance metrics often appeared in workshops at NeurIPS, ICLR, and ICML, and in blog posts by researchers at OpenAI, DeepMind, Facebook AI Research, Google Research, and industrial labs including Amazon Science and Microsoft Research. Evaluations measured sample efficiency, wall-clock training time, GPU utilization, and reproducibility across environments used in experiments by teams from Berkeley AI Research, DeepMind, OpenAI Scholars Program, and university labs referenced in conference proceedings.

Community, Adoption, and Licensing

The Roboschool source attracted contributions from developers on GitHub and discussions in forums frequented by researchers affiliated with OpenAI, DeepMind, Berkeley AI Research, Stanford AI Lab, CMU Robotics, NVIDIA Research, Microsoft Research, Facebook AI Research, Amazon Web Services, Intel Labs, Waymo, Uber ATG, and groups participating in NeurIPS and ICML competitions. Its MIT-style licensing facilitated forks and reuse by academic groups at MIT, Harvard, Caltech, Yale University, Columbia University, Cornell University, University of Illinois Urbana-Champaign, Purdue University, University of Washington, University of Michigan, and research-oriented startups attending ICLR workshops. Over time, maintainers and adopters shifted focus to alternative simulators developed by teams at NVIDIA, Unity Technologies, DeepMind, and community projects like PyBullet and Isaac Gym.

Category:Simulation software