Article

SP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control Framework for Humanoid Loco-Manipulation

SP-HLM proposes a sparse-pose-driven interface framework that uses five task-critical body poses plus a learned implicit motion prior to represent spatial intent and whole-body coordination in complementary forms. The SP-VLA vision-language-action model first predicts task-relevant sparse poses and finger joint positions from multimodal observations and language instructions via asynchronous action-chunk inference. The SP-MGM motion generation model, trained through two-stage distillation, reconstructs coordinated full-body motion in real time, and the SP-GMT general motion tracker then combines the explicit sparse targets with the implicit motion prior for whole-body control.

Part of the event: SP-HLM: Sparse Pose-Driven Motion Generation and Whole-Body Control Framework for Humanoid Robots

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