We present RoboHive, a comprehensive software platform and ecosystem for research in the field of Robot Learning and Embodied Artificial Intelligence. Our platform encompasses a diverse range of pre-existing and novel environments, including dexterous manipulation with the Shadow Hand, whole-arm manipulation tasks with Franka and Fetch robots, quadruped locomotion, among others. Included environments are organized within and cover multiple domains such as hand manipulation, locomotion, multi-task, multi-agent, muscles, etc. In comparison to prior works, RoboHive offers a streamlined and unified task interface taking dependency on only a minimal set of well-maintained packages, features tasks with high physics fidelity and rich visual diversity, and supports common hardware drivers for real-world deployment. The unified interface of RoboHive offers a convenient and accessible abstraction for algorithmic research in imitation, reinforcement, multi-task, and hierarchical learning. Furthermore, RoboHive includes expert demonstrations and baseline results for most environments, providing a standard for benchmarking and comparisons. Details: https://sites.google.com/view/robohive

翻译：我们提出了RoboHive，一个用于机器人学习与具身人工智能研究的综合性软件平台和生态系统。该平台包含多种已有的和新创建的环境，包括使用Shadow Hand的灵巧操作、基于Franka和Fetch机器人的全身操作任务、四足运动等。所包含的环境覆盖并归类于多个领域，例如手部操作、运动控制、多任务、多智能体、肌肉建模等。与先前工作相比，RoboHive提供了一套精简且统一的任务接口，仅依赖少量维护良好的软件包；具有高物理保真度和丰富视觉多样性的任务特性；并支持用于真实世界部署的常见硬件驱动。RoboHive的统一接口为模仿学习、强化学习、多任务学习和分层学习等算法研究提供了便捷且易用的抽象层。此外，RoboHive还为大多数环境提供了专家演示和基线结果，为基准测试和比较建立了标准。详情：https://sites.google.com/view/robohive