CroMoGen: Cross-Morphology Demonstration Retargeting for Mobile Manipulation

Automated demonstration synthesis for imitation learning avoids costly data collection, but transferring demonstrations across morphologies often produces trajectories that the target robot cannot execute or that collide with obstacles. Mobile manipulation amplifies these challenges as the planner must coordinate base placement and arm motion simultaneously. We present CroMoGen, a learning-free, planning-based framework that synthesizes mobile manipulation demonstrations from different robot and human sources. CroMoGen formulates synthesis as a constrained optimization problem that combines kinematic feasibility, whole-body collision avoidance, and a trajectory alignment objective that preserves task-relevant object interactions. The approach adapts demonstrated motions to the target morphology and environment. Further, unlike methods that decouple base placement from arm motion, CroMoGen jointly optimizes both for greater adherence to the source demonstration. We evaluate CroMoGen on four mobile manipulation task families in RoboCasa and ParaHome. In the challenging human-to-robot setting, CroMoGen reaches a 56% demonstration-generation success rate, exceeding the strongest baseline by 37 percentage points.