Robot Failure

Robot failure research aims to understand, predict, and mitigate malfunctions in robotic systems to improve their reliability and user acceptance. Current efforts focus on developing methods for detecting and explaining failures using multimodal data and advanced machine learning models like vision-language models and reinforcement learning algorithms, often incorporating causal reasoning and Bayesian networks. This research is crucial for enhancing the safety, robustness, and trustworthiness of robots across various applications, from industrial automation to human-robot collaboration in domestic settings. Improved failure prediction and explanation capabilities are key to building more reliable and user-friendly robots.