Effects of Explanation Strategies to Resolve Failures in Human-Robot Collaboration

Khanna, Parag; Yadollahi, Elmira; Björkman, Mårten; Leite, Iolanda; Smith, Christian

doi:10.1109/ro-man57019.2023.10309394

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is not all about actions or tasks. Explanations can also have their origin in an unexpected failure or perception, which has arisen during the execution of the plan and that has changed this [39,40]. In many of these situations, the robot is forced to revisit the plan to determine what might have happened.…”

Section: Climbing the Ladder Of Causationmentioning

confidence: 99%

Causal-Based Approaches to Explain and Learn from Self-Extension—A Review

Marfil,

Bustos,

Bandera

2024

Electronics

View full text Add to dashboard Cite

The last decades have seen a revolution in autonomous robotics. Deep learning approaches and their hardware implementations have made it possible to endow robots with extraordinary perceptual capabilities. In addition, they can benefit from advances in Automated Planning, allowing them to autonomously solve complex tasks. However, on many occasions, the robot still acts without internalising and understanding the reasons behind a perception or an action, beyond an immediate response to a current state of the context. This gap results in limitations that affect its performance, reliability, and trustworthiness. Deep learning alone cannot bridge this gap because the reasons behind behaviour, when it emanates from a model in which the world is a black-box, are not accessible. What is really needed is an underlying architecture based on deeper reasoning. Among other issues, this architecture should enable the robot to generate explanations, allowing people to know why the robot is performing, or has performed, a certain action, or the reasons that may have caused a certain plan failure or perceptual anomaly. Furthermore, when these explanations arise from a cognitive process and are shared, and thus validated, with people, the robot should be able to incorporate these explanations into its knowledge base, and thus use this understanding to improve future behaviour. Our article looks at recent advances in the development of self-aware, self-evolving robots. These robots are designed to provide the necessary explanations to their human counterparts, thereby enhancing their functional capabilities in the quest to gain their trust.

show abstract

Section: Climbing the Ladder Of Causationmentioning

confidence: 99%