Jointly achieving safety and efficiency in human-robot interaction (HRI) settings is challenging, as the robot’s planning objectives may be at odds with the human’s own intent and expectations.
Recent approaches ensure safe robot operation in uncertain environments through a supervisory control scheme, sometimes called shielding, which overrides the robot’s nominal plan with a safety fallback strategy when a safety-critical event is imminent. These reactive “last-resort” strategies (typically in the form of aggressive emergency maneuvers) focus on preserving safety without efficiency considerations; when the nominal planner is unaware of possible safety overrides, shielding can be activated more frequently than necessary, leading to degraded performance.
In this work, we propose a new shielding-based planning approach that allows the robot to plan efficiently by explicitly accounting for possible future shielding events. Leveraging recent work on Bayesian human motion prediction, the resulting robot policy proactively balances nominal performance with the risk of high-cost emergency maneuvers triggered by low-probability human behaviors. We formalize Shielding-Aware Robust Planning (SHARP) as a stochastic optimal control problem and propose a computationally efficient framework for finding tractable approximate solutions at runtime.
We evaluated our approach on simulated driving scenarios, with the human driver’s trajectories taken from the Waymo Open Motion Dataset. On average, our proposed planner improved the planning performance by at least 16% comparing to the state-of-the-art SMPC baseline across all testing scenarios.