HoloBoxing: A Simple Recipe for Humanoid Boxing with 7 Minutes of Single-GPU Training
HoloBoxing presents a recipe for learning humanoid boxing by building on a pretrained whole-body controller. A compact planner requests future body-point targets while the frozen controller handles joint-level execution. A learned decoder maps 32-dimensional actions to structured target windows, and measured-state imitation initializes the planner before reinforcement learning refines interaction behavior. The authors report about seven minutes of additional training on a single GPU, excluding controller pretraining, data preparation, evaluation, and search. Simulated boxing experiments and component controls evaluate the behavior.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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HoloBoxing presents a recipe for learning humanoid boxing by building on a pretrained whole-body controller. A compact planner requests future body-point targets while the frozen controller handles joint-level execution. A learned decoder maps 32-dimensional actions to structured target windows, and measured-state imitation initializes the planner before reinforcement learning refines interaction behavior. The authors report about seven minutes of additional training on a single GPU, excluding controller pretraining, data preparation, evaluation, and search. Simulated boxing experiments and component controls evaluate the behavior.
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Original link: huggingface.co. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Results are from simulated boxing experiments; the seven-minute figure excludes controller pretraining, data preparation, evaluation, and search. Source claims not independently checked.
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Key findings from the stored brief
- The planner predicts body-point targets while a frozen whole-body controller handles joint-level execution.
- Imitation initialization gives the planner a starting behavior before reinforcement learning refines it.
- The reported additional training is about seven minutes on a single GPU, excluding controller pretraining and data preparation.
Results are from simulated boxing experiments; the seven-minute figure excludes controller pretraining, data preparation, evaluation, and search. Source claims not independently checked.
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Why this may matter
It suggests humanoid task learning can reuse an existing whole-body controller and train a small upper policy cheaply, lowering the compute needed for interactive humanoid skills.
Limits and open questions
Results are from simulated boxing experiments; the seven-minute figure excludes controller pretraining, data preparation, evaluation, and search. Source claims not independently checked.
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