HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion
  and Manipulation
Carmelo Sferrazza Dun-Ming Huang Xingyu Lin Youngwoon Lee Pieter Abbeel
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://sferrazza.cc/humanoidbench_site.