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Towards Automatically Correcting Robot Behavior Using Non-Expert Feedback

Time: Mon 2022-12-05 14.00

Location: Kollegiesalen, Brinellvägen 6

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Language: English

Subject area: Computer Science

Doctoral student: Sanne van Waveren , Robotik, perception och lärande, RPL

Opponent: Professor Hadas Kress-Gazit, Cornell University, Ithaca, NY, USA

Supervisor: Iolanda Leite, Robotik, perception och lärande, RPL

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QC 20221109


Robots that operate in human environments need the capability to adapt their behavior to new situations. Most robots so far rely on pre-programmed behavior or machine learning algorithms trained offline with selected data. Due to the large number of possible situations robots might encounter, it becomes impractical to define or learn all behaviors before deployment, causing them to inevitably fail at some point in time. As a result of this inability to adapt to new situations, the robot might fail to successfully complete its task or achieve a goal in a way that defies people's expectations or preferences. Ideally, robots need to ability to autonomously collect additional behaviors and constraints that enable them to correct their behaviors.The topic of this dissertation is robot behavior correction using feedback from non-experts, people who are not necessarily programmers or roboticists. We explore how non-experts can help robots recover when their plan or policy fails. Furthermore, working with and around humans, robots need to adapt to user preferences. For instance, users might prefer their autonomous vehicle to adopt a defensive driving style over an aggressive one, or someone might prefer their coffee mug to be placed on the coffee table left of their chair. In many everyday situations, robots will require additional rules that do not require technical knowledge. For instance, a rule that the robot should not place the coffee mug too close to the edge of the table, or that the robot might need to open the door of a cabinet first before it can place something in it. We propose an approach that leverages knowledge from non-experts to provide input to correct robot behaviors. We identify two main types of input: what the robot should do (task goals and constraints) and how the robot should achieve its task (preferences and decision-making). This dissertation explores this approach by drawing on human-robot interaction research on robot failures, crowdsourcing, and machine learning for large-scale data collection and generation, and techniques from formal methods to ensure the safety and correctness of the robot. The work described in this dissertation is a step towards better understanding how we can design robots that can automatically correct their behavior using non-expert feedback and what the challenges are of non-expert robot behavior correction.