Don't Let Your Robot be Harmful: Responsible Robotic Manipulation via Safety-as-Policy

TL;DR

This paper introduces a novel framework called Safety-as-policy for responsible robotic manipulation, enabling robots to consider safety risks during task execution through virtual scenario generation and mental modeling, thereby reducing real-world hazards.

Contribution

The paper proposes Safety-as-policy, combining world and mental models, and introduces the SafeBox dataset for safe, synthetic evaluation of robotic manipulation tasks.

Findings

Safety-as-policy effectively avoids hazards in synthetic and real-world tests.

The SafeBox dataset provides a safe benchmark for evaluating responsible robotic manipulation.

Experiments show significant performance improvements over baseline methods.

Abstract

Unthinking execution of human instructions in robotic manipulation can lead to severe safety risks, such as poisonings, fires, and even explosions. In this paper, we present responsible robotic manipulation, which requires robots to consider potential hazards in the real-world environment while completing instructions and performing complex operations safely and efficiently. However, such scenarios in real world are variable and risky for training. To address this challenge, we propose Safety-as-policy, which includes (i) a world model to automatically generate scenarios containing safety risks and conduct virtual interactions, and (ii) a mental model to infer consequences with reflections and gradually develop the cognition of safety, allowing robots to accomplish tasks while avoiding dangers. Additionally, we create the SafeBox synthetic dataset, which includes one hundred responsible robotic manipulation tasks with different safety risk scenarios and instructions, effectively reducing the risks associated with real-world experiments. Experiments demonstrate that Safety-as-policy can avoid risks and efficiently complete tasks in both synthetic dataset and real-world experiments, significantly outperforming baseline methods. Our SafeBox dataset shows consistent evaluation results with real-world scenarios, serving as a safe and effective benchmark for future research.