HumanoidBench：面向全身运动与操作的仿人机器人仿真基准
HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
AI总结：
针对仿人机器人研究受硬件限制的问题，提出仿真基准HumanoidBench，包含多种全身操作与运动任务，验证了分层学习方法的优势，为算法研究提供平台。
摘要：
仿人机器人凭借类人形态带来的灵活性与适应性，有望在多样环境和任务中辅助人类，但相关研究常受限于成本高昂且易损坏的硬件装置。为推动仿人机器人算法研究，我们提出高维度仿真机器人学习基准HumanoidBench，该基准搭载带灵巧手的仿人机器人，包含多项高难度全身操作与运动任务。研究发现，当前最先进的强化学习算法在多数任务上表现不佳，而分层学习方法在行走、伸手等鲁棒底层策略的支撑下能取得更优性能。HumanoidBench为机器人领域提供了一个探究仿人机器人完成多样任务时面临的挑战的平台，可助力算法与想法的快速验证，开源代码见https://humanoid-bench.github.io。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
