MedManip: Scan-Anchored Hierarchical Optimization for 4D Medical Loco-Manipulation

Humanoid robots have the potential to assist with specialized medical manipulation tasks, yet collecting large-scale whole-body demonstrations in this domain requires skilled operators, specialized equipment, and substantial human effort. Video generation models provide a scalable alternative, but their outputs often exhibit unstable object motion, inaccurate medical-object geometry, and implausible physical contact, which are unacceptable in medical settings. We present MedManip, a pipeline that leverages scanned medical objects as geometric anchors to convert generated interaction videos into physically grounded 4D human-object trajectories. Specifically, MedManip progressively propagates the accurate object geometry through three stages: scan-anchored object trajectory estimation, object-guided whole-body motion refinement, and geometry-aware contact optimization. We first recover a temporally stable object trajectory from the generated video, then use the recovered object motion to correct whole-body reachability and human-object alignment, and finally refine fine-grained contact while keeping the object trajectory fixed. The resulting trajectories are retargeted to a humanoid robot and converted into executable whole-body motions through low-level control. The recovered trajectories are subsequently retargeted to a humanoid robot and executed through physics-based control. Experiments on generated sequences across over 80 objects show that MedManip improves the valid demonstration rate from around 10% to over 30% and achieves higher physics-based execution success than existing generation pipelines.