# Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving

arXiv: https://arxiv.org/abs/2303.03504 · HTML: https://mlsanity.com/paper/2303.03504/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2023.

## Built on by (most cited first, 7 of 7)

- [mmDEAR: mmWave Point Cloud Density Enhancement for Accurate Human Body Reconstruction](https://mlsanity.com/paper/2503.02375/) (2025)
- [Language-Enhanced Latent Representations for Out-of-Distribution Detection in Autonomous Driving](https://mlsanity.com/paper/2405.01691/) (2024)
- [Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions](https://mlsanity.com/paper/2410.07409/) (2024)
- [Suite-IN: Aggregating Motion Features from Apple Suite for Robust Inertial Navigation](https://mlsanity.com/paper/2411.07828/) (2024)
- [Responsibility-aware Strategic Reasoning in Probabilistic Multi-Agent Systems](https://mlsanity.com/paper/2411.00146/) (2024)
- [Learning Probabilistic Responsibility Allocations for Multi-Agent Interactions](https://mlsanity.com/paper/2604.13128/) (2026)
- [SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2610.13737/) (2026)

## Abstract (arXiv)

Drivers have a responsibility to exercise reasonable care to avoid collision with other road users. This assumed responsibility allows interacting agents to maintain safety without explicit coordination. Thus to enable safe autonomous vehicle (AV) interactions, AVs must understand what their responsibilities are to maintain safety and how they affect the safety of nearby agents. In this work we seek to understand how responsibility is shared in multi-agent settings where an autonomous agent is interacting with human counterparts. We introduce Responsibility-Aware Control Barrier Functions (RA-CBFs) and present a method to learn responsibility allocations from data. By combining safety-critical control and learning-based techniques, RA-CBFs allow us to account for scene-dependent responsibility allocations and synthesize safe and efficient driving behaviors without making worst-case assumptions that typically result in overly-conservative behaviors. We test our framework using real-world driving data and demonstrate its efficacy as a tool for both safe control and forensic analysis of unsafe driving.