BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics

Understand

Neural-network-based dynamics models learned from observational data have shown strong predictive capabilities for scene dynamics in robotic manipulation tasks.

• However, their inherent non-linearity presents significant challenges for effective planning.
• Current planning methods, often dependent on extensive sampling or local gradient descent, struggle with long-horizon motion planning tasks involving complex contact events.
• In this paper, we present a GPU-accelerated branch-and-bound (BaB) framework for motion planning in manipulation tasks that require trajectory optimization over neural dynamics models.