HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation
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Abstract (arXiv)
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.
Key facts. Cited by 101 papers in the map (data through 7 Oct 2026). Builds on 47 of its 69 references that are in the map. First posted on arXiv 15 Mar 2024.
Builds on
47 of 69 references.
References · 47
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Citing papers per year: 2024: 14; 2025: 45; 2026: 42.
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