Guiding without Prescribing: A Differentiable Trajectory Learning Framework for Robot-Assisted Dressing

Robot-assisted dressing requires generating effective manipulation trajectories under complex cloth dynamics and sustained garment-body interactions. Learning a single trajectory solver across diverse arm configurations is further challenging because effective trajectories can vary substantially with pose. Differentiable physics provides first-order gradients for trajectory optimization, but obtaining these gradients requires complete garment states unavailable to the solver at inference. We propose Guiding without Prescribing (GwoP), a differentiable trajectory learning framework that integrates physical and body-relative guidance during training. GwoP separates observation-conditioned trajectory prediction from full-state physical evaluation within a differentiable computation graph, allowing physical gradients to train the solver without requiring complete garment states at inference. To support shared learning across arm poses, GwoP further predicts trajectories and auxiliary body-surface relations in parallel, providing structural guidance without prescribing trajectory geometry. Across unseen arm configurations, GwoP achieves high dressing progress with limited garment deformation. We further validate the learned trajectories through cross-simulator transfer and real-robot execution across diverse arm poses.