robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

We introduce robosuite, a modular simulation framework and benchmark for robot learning. This framework is powered by the MuJoCo physics engine [15], which performs fast physical simulation of contact dynamics. The overarching goal of this framework is to facilitate research and development of data-driven robotic algorithms and techniques. The development of this framework was initiated from the SURREAL project [3] on distributed reinforcement learning for robot manipulation, and is now 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 [9], such as reinforcement learning [13, 7] and imitation learning [12], 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. Nonetheless, the challenges of reproducibility and the limited accessibility of robot hardware have impaired research progress [5]. In recent years, advances in physics-based simulations and graphics have led to a series of simulated platforms and toolkits [1, 14, 8, 2, 16] that have accelerated scientific progress on robotics and embodied AI. Through the robosuite project we aim to provide researchers with: 1. a modular design that offers great flexibility to create new robot simulation environments and tasks;