Robotics: Science and Systems XX

HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Carmelo Sferrazza, Dun-Ming Huang, Xingyu Lin, Youngwoon Lee, Pieter Abbeel

Abstract:

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 baseline 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.

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Bibtex:

  
@INPROCEEDINGS{Sferrazza-RSS-24, 
    AUTHOR    = {Carmelo Sferrazza AND Dun-Ming Huang AND Xingyu Lin AND Youngwoon Lee AND Pieter Abbeel}, 
    TITLE     = {{HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2024}, 
    ADDRESS   = {Delft, Netherlands}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2024.XX.061} 
}