The Rope Planner Scored Up to 22× More Actions

Two robot arms controlling an unanchored rope face a combinatorial choice of grasp points, directions and magnitudes. ForwardDLO predicts segment displacement with a recurrent latent model grounded in the observed rope state at every step. It reduced open-loop error 13% below the strongest learned baseline and evaluated eight to 22 times more candidate actions within the same planning budget. In simulated routing at 30 hertz, throughput translated to 98% task success, versus at most 30% for comparison models at their own budgets. Real-world shape matching remained comparable to slower alternatives.