[Submitted on 21 Sep 2026]
Title:HOTICE: Whole-Body Humanoid Object Transportation in Cluttered Environments
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Abstract:Object transportation is a fundamental capability for humanoid robots operating in real-world, human-centric environments, yet existing methods struggle when clutter constrains free space around both the robot and its carried payload. We present HOTICE, a whole-body humanoid learning framework for transporting objects through such cluttered environments. First, we introduce Humanoid-Object Decoupled Potential Fields, which jointly encode collision-avoidance guidance for the robot and the carried object, enabling coordinated, obstacle-aware motion for both. Second, to address the large action space inherent to whole-body loco-manipulation, we design a dual-agent reinforcement learning architecture that decouples upper- and lower-body control while preserving whole-body coordination via shared state observations and rewards. To train a policy that generalizes across diverse cluttered scenes, we further employ a specialist-to-generalist distillation strategy, in which privileged teacher policies are distilled into a single deployable student policy. We evaluate HOTICE in MuJoCo simulation and on a real Unitree G1 humanoid, demonstrating effective and robust object transportation across cluttered scenarios for objects of varying shapes. Our results show that HOTICE reliably coordinates whole-body motion and object-aware collision avoidance, generalizing effectively to previously unseen cluttered environments while achieving strong performance in sim2real deployment.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.25363 [cs.RO]
(or arXiv:2609.25363v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25363
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Toan Nguyen [view email] [v1] Mon, 21 Sep 2026 20:01:04 UTC (31,068 KB)
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