Xingyu Lin
Carmelo (Carlo) Sferrazza
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 manipulati
We benchmark a variety state-of-the-art reinforcement learning algorihtms on all tasks. Our results show how these end-to-end (flat) algorithms struggle with controlling the complex humanoid robot dynamics and solving the most challenging tasks. In fact, many of such tasks require long-horizon planning and necessitate acquiring a diverse set of skills (e.g., balancing, walking, reaching, etc.) to successfully achieve the desired objective.
We argue that these issues can be mitigated by introducing additional structure into the learning problem. In particular, we explore a hierarchical learning paradigm, where one or multiple low-level skill policies are provided to a high-level planning policy that sends setpoints to lower-level policies.
Xingyu Lin
Carmelo (Carlo) Sferrazza