SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction

TL;DR

SHARP introduces a planning method for human-robot interaction that proactively considers safety shields, balancing efficiency and safety by predicting and accounting for potential safety overrides due to human behavior.

Contribution

The paper presents a novel shielding-aware planning approach that explicitly incorporates the possibility of safety overrides into the robot's decision-making process.

Findings

Outperforms shielding-agnostic baseline in simulated driving scenarios.

Proactively balances nominal performance with safety considerations.

Uses Bayesian human motion prediction for anticipatory planning.

Abstract

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.