HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
<|reference_start|>HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation: 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.<|reference_end|>
@article{sferrazza2024humanoidbench:,
    title={HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion
  and Manipulation},
    author={Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, Pieter
  Abbeel},
    journal={arXiv preprint arXiv:2403.10506},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10506},
    primaryClass={cs.RO cs.AI cs.LG}
}