Learning Systems & Robotics Lab (Angela Schoellig)

SM²ITH: Safe Mobile Manipulation with Interactive Human Prediction via Task-Hierarchical Bilevel MPC

The video introduces SM²ITH, a novel control architecture for mobile manipulators that embeds interactive human‑prediction into a task‑hierarchical bilevel model predictive control (MPC) scheme. By forecasting human trajectories, the system can plan safe, efficient motions while juggling several prioritized objectives such as manipulation, navigation, and obstacle avoidance. Key technical contributions include a two‑level MPC hierarchy: the upper level selects task priorities and generates reference trajectories, while the lower level solves a constrained optimization that respects predicted human motion. Compared with a purely reactive baseline, SM²ITH maintains a larger safety margin, reduces near‑collision events, and achieves comparable task completion times. Quantitative results on a wheeled‑arm platform show a 35% drop in minimum distance violations and a 12% improvement in overall task throughput. The presenters highlight a scenario where the robot hands over a tool to a human worker. "Our predictive controller anticipates the worker’s reach and adjusts the arm trajectory in real time," one researcher notes, illustrating how the framework yields smoother hand‑offs and eliminates abrupt stops. Video clips demonstrate the robot navigating crowded aisles without encroaching on personal space, underscoring the practical benefits of human‑aware planning. For industry, SM²ITH offers a pathway to deploy mobile manipulators in shared workspaces such as factories, hospitals, and warehouses without extensive safety cages. By marrying prediction with hierarchical control, the approach promises higher productivity while meeting stringent safety standards, accelerating the adoption of collaborative robots.