Unverified50% confidenceFactExact time
The HoloBoxing project, shared on Reddit, provides a method for training a humanoid boxing strategy from scratch on a consumer-grade GPU.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
11/1/2026
First Seen
Valid until: 1/30/2027
Sources
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UnverifiedHoloBoxing adopts a hierarchical control architecture: freezing a pretrained whole-body controller and training only a compact upper-layer planner.63% similarUnverifiedNVIDIA also provides a reinforcement-learning-based improvement approach and multi-node distributed training recipes, with related resources available in Nemo RL and Nemo Gym.60% similarUnverifiedWithout fine-tuning on real data, the localization head trained on synthetic data improved HOTA for MOTIP and HAT on real video by 0.96 to 1.14.57% similarUnverifiedDex-One2Many adopts a real-to-sim-to-real pipeline: human videos are parsed into scene graphs, policies are trained in simulation, and finally zero-shot transferred to a real multi-fingered dexterous hand.57% similarUnverifiedIt is theoretically feasible to use reinforcement learning to achieve a no-hit boss battle in an action game.57% similar
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