The relevance of causation in robotics: A review, categorization, and analysis

Hellström, Thomas

doi:10.1515/pjbr-2021-0017

Cited by 17 publications

(10 citation statements)

References 90 publications

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“…Despite being acknowledged as an important concept, causality is relatively underexplored in the robotics domain [9], [2]. Some works explore causality to distinguish between task-relevant and -irrelevant variables.…”

Section: A Causality In Roboticsmentioning

confidence: 99%

“…One important component in human interactions is the ability to explain one's actions, especially when failures occur [1], [2]. It is argued that robots need this skill if they were to act in human-centered environments on a daily basis [3].…”

Section: Introductionmentioning

confidence: 99%

“…It is argued that robots need this skill if they were to act in human-centered environments on a daily basis [3]. Furthermore, explainability is also shown to increase trust and transparency in robots [1], [2], and the diagnoses capabilities of a robot are crucial for correcting its behavior [4].…”

Section: Introductionmentioning

confidence: 99%

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Why did I fail? A Causal-based Method to Find Explanations for Robot Failures

Diehl¹,

Ramirez-Amaro²

2022

Preprint

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Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on that model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for successful execution, which is found through breadth-first search and is based on success predictions from the learned causal model. We assess the sim2real transferability of the causal model on a cube stacking scenario. Based on real-world experiments with two differently embodied robots, we achieve a sim2real accuracy of 70% without any adaptation or retraining. Our method thus allowed real robots to give failure explanations like, 'the upper cube was dropped too high and too far to the right of the lower cube.'

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Section: A Causality In Roboticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Why did I fail? A Causal-based Method to Find Explanations for Robot Failures

Diehl¹,

Ramirez-Amaro²

2022

Preprint

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“…Once learned, these models can be used by the robot to understand the world and then, to learn the actions that allow it to accomplish the desired task with few trials and few examples. There are other related themes that should deserve attention in HRC research, such as interactive learning as a basis for self-supervised learning [156,157] and causal models to support explanation and understanding of the world [158,159].…”

Section: Representations and Understanding Of The Worldmentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

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“…O NE important component in human interactions is the ability to explain one's actions, especially when failures occur [1], [2]. It is argued that robots need this skill if they were to act in human-centered environments on a daily basis [3].…”

Section: Introductionmentioning

confidence: 99%

Why Did I Fail? A Causal-Based Method to Find Explanations for Robot Failures

Diehl

Ramirez-Amaro

2022

IEEE Robot. Autom. Lett.

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Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this letter are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on the obtained model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for successful execution. This state is found through breadth-first search and is based on success predictions from the learned causal model. We assessed our method in two different scenarios I) stacking cubes and II) dropping spheres into a container. The obtained causal models reach a sim2real accuracy of 70% and 72%, respectively. We finally show that our novel method scales over multiple tasks and allows real robots to give failure explanations like "the upper cube was stacked too high and too far to the right of the lower cube."

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The relevance of causation in robotics: A review, categorization, and analysis

Cited by 17 publications

References 90 publications

Why did I fail? A Causal-based Method to Find Explanations for Robot Failures

Why did I fail? A Causal-based Method to Find Explanations for Robot Failures

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Why Did I Fail? A Causal-Based Method to Find Explanations for Robot Failures

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