  Jointly achieving safety and efficiency in human-robot interaction (HRI)
settings is a challenging problem, 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. Our method outperforms the shielding-agnostic motion planning
baseline (equipped with the same human intent inference scheme) on simulated
driving examples with human trajectories taken from the recently released Waymo
Open Motion Dataset.
