Failing Gracefully: Mitigating Impact of Inevitable Robot Failures
Duc M. Nguyen, Saad A. Ghani, Andrew Marshall, Allison Andreyev, Gregory J. Stein, Xuesu Xiao
cs.RO, cs.AI, cs.HC, cs.LG
Submitted: 2026-08-05
Comments: Accepted to IEEE International Conference on Robotics and Automation (ICRA 2026)
Code: https://github.com/kevinzakka/mink
License: http://creativecommons.org/licenses/by/4.0/
The gist: Service robots operate in household environments shared with humans, pets, and everyday objects, where they are highly susceptible to failures such as software crashes, hardware degradation, or
Terminology
Abstract
Service robots operate in household environments shared with humans, pets, and everyday objects, where they are highly susceptible to failures such as software crashes, hardware degradation, or unpredictable interactions. While roboticists strive to minimize failures, some remain inevitable, making it critical to mitigate their potential consequences for safe and reliable deployment. This paper introduces a novel safety formulation that evaluates both the probability of impactful interactions between robots and surrounding entities during failures, and the severity of their outcomes. By quantifying the impact of failures on different entities, our approach enables robots to make informed planning decisions that balance safety with task efficiency. To support systematic evaluation, we also present FailBench, a MuJoCo-based simulation framework for studying robot-environment interactions under diverse failure modes, including sensing issues and actuator malfunctions. Together, our safety formulation and FailBench provide a foundation for developing safer and more robust motion plans and learned policies in real-world household environments.
Sources
- REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
- RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
- robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
- DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning
- Generalizing Safety Beyond Collision-Avoidance via Latent-Space Reachability Analysis
- Anticipatory Task and Motion Planning
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies