Humanoid robots hold great promise in assisting humans in diverse environments and tasks, due to their flexibility and adaptability leveraging human-like morphology. However, research in humanoid robots is often bottlenecked by the costly and fragile hardware setups. To accelerate algorithmic research in humanoid robots, we present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands and a variety of challenging whole-body manipulation and locomotion tasks. Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies, such as walking or reaching. With HumanoidBench, we provide the robotics community with a platform to identify the challenges arising when solving diverse tasks with humanoid robots, facilitating prompt verification of algorithms and ideas. The open-source code is available at https://humanoid-bench.github.io.

翻译：人形机器人凭借其类人形态带来的灵活性与适应性，在协助人类完成多样化环境与任务方面展现出巨大潜力。然而，人形机器人的研究常受限于昂贵且脆弱的硬件配置。为加速人形机器人算法研究，我们提出了一个高维仿真机器人学习基准——HumanoidBench，该基准以配备灵巧手的人形机器人为载体，包含一系列具有挑战性的全身操作与运动任务。我们的研究结果表明，当前最先进的强化学习算法在多数任务上表现不佳，而采用分层学习方法在稳健的低层策略（如行走或抓取）支持下能获得更优性能。通过HumanoidBench，我们为机器人学界提供了一个平台，用以识别人形机器人在执行多样化任务时面临的挑战，促进算法与构想的快速验证。开源代码发布于 https://humanoid-bench.github.io。