SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation
  via Skill Blending
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/.
684ae246dbd21a9cc27b111c
Yuxuan Kuang
claimed_verified
2025-06-16T07:17:03.322Z
62359088a17d7271859c88f4
https://cdn-avatars.huggingface.co/v1/production/uploads/1647677549197-noauth.jpeg
Yuxuan Kuang
user
yxK
684ae246dbd21a9cc27b111d
Haoran Geng
684ae246dbd21a9cc27b111e
Amine Elhafsi
admin_assigned
2025-06-16T07:50:59.675Z
64da71311d19239f50483005
/avatars/d97a7177adca180d795bf0f9ec66c65c.svg
Amine Elhafsi
user
AmineElhafsi
684ae246dbd21a9cc27b111f
Tan-Dzung Do
684ae246dbd21a9cc27b1120
Pieter Abbeel
684ae246dbd21a9cc27b1121
Jitendra Malik
admin_assigned
2025-06-16T07:51:18.391Z
65369a95605a07338de78ab0
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/sGFjOjLT2akN-sn5beVWL.jpeg
Jitendra Malik 
user
jitendra1995
684ae246dbd21a9cc27b1122
Marco Pavone
684ae246dbd21a9cc27b1123
Yue Wang
SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation
  via Skill Blending
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/.
SkillBlender is a hierarchical reinforcement learning framework that uses pretrained primitive skills to efficiently solve diverse loco-manipulation tasks for humanoid robots.
reinforcement learning SkillBlender goal-conditioned task-agnostic primitive skills hierarchical reinforcement learning SkillBench cross-embodiment simulated benchmark loco-manipulation tasks reward engineering reward hacking