SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation
  via Skill Blending
Authors: name: Yuxuan Kuang, name: Haoran Geng, name: Amine Elhafsi, name: Tan-Dzung Do, name: Pieter Abbeel, name: Jitendra Malik, name: Marco Pavone, name: Yue Wang

Humanoid robots hold significant potential in accomplishing daily tasks
across diverse environments thanks to their flexibility and human-like
morphology. Recent works have made significant progress in humanoid whole-body
control and loco-manipulation leveraging optimal control or reinforcement
learning. However, these methods require tedious task-specific tuning for each
task to achieve satisfactory behaviors, limiting their versatility and
scalability to diverse tasks in daily scenarios. To that end, we introduce
SkillBlender, a novel hierarchical reinforcement learning framework for
versatile humanoid loco-manipulation. SkillBlender first pretrains
goal-conditioned task-agnostic primitive skills, and then dynamically blends
these skills to accomplish complex loco-manipulation tasks with minimal
task-specific reward engineering. We also introduce SkillBench, a parallel,
cross-embodiment, and diverse simulated benchmark containing three embodiments,
four primitive skills, and eight challenging loco-manipulation tasks,
accompanied by a set of scientific evaluation metrics balancing accuracy and
feasibility. Extensive simulated experiments show that our method significantly
outperforms all baselines, while naturally regularizing behaviors to avoid
reward hacking, resulting in more accurate and feasible movements for diverse
loco-manipulation tasks in our daily scenarios. Our code and benchmark will be
open-sourced to the community to facilitate future research. Project page:
https://usc-gvl.github.io/SkillBlender-web/.