HOIST: Humanoid Optimization with Imitation and Sample-efficient Tuning for Manipulating Suspended Loads

• URL: http://arxiv.org/abs/2606.00252v1
• Date: Fri, 29 May 2026 18:33:32 GMT
• Title: HOIST: Humanoid Optimization with Imitation and Sample-efficient Tuning for Manipulating Suspended Loads
• Abstract summary: We present HOIST-Humanoid optimized with Imitation and Sample-efficient Tuning for manipulating suspended loads.<n> HOIST first finetunes a high-level vision--action (VLA) policy from virtual-reality (VR) teleoperation demonstrations.<n>It then uses VLA rollouts and iterative batched RL to improve placement accuracy and stopping behavior.
• Score: 26.63242922377076
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manipulating suspended payloads with humanoid robots is challenging because the robot can only influence an underactuated, oscillatory load through whole-body motion and intermittent contact. Imitation learning provides safe initial behavior but does not directly optimize final placement, while reinforcement learning from scratch is unsafe and sample-inefficient on real humanoids. We present HOIST-Humanoid Optimized with Imitation and Sample-efficient Tuning for manipulating suspended loads. HOIST first finetunes a high-level vision-language-action (VLA) policy from virtual-reality (VR) teleoperation demonstrations and executes its commands through a whole-body controller. It then uses VLA rollouts and iterative batched RL to improve placement accuracy and stopping behavior. Experiments in simulation and on a real humanoid show that HOIST improves over imitation-only and additional-demonstration baselines; compared with pure VLA rollouts, HOIST reduces translational placement error by 19.9 cm and raw angular error by 3.56 degrees, demonstrating the potential of humanoids for underactuated material-handling tasks.

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