Continual Learning for Affective Robotics: Why, What and How?

By Nikhil Churamani, Sinan Kalkan, Hatice Gunes. 2020

56 citations recorded by the source corpus.

Creating and sustaining closed-loop dynamic and social interactions with humans require robots to continually adapt towards their users’ behaviours, their affective states and moods while keeping them engaged in the task they are performing. Analysing, understanding and appropriately responding to human nonverbal behaviour and affective states are the central objectives of affective robotics research. Conventional machine learning approaches do not scale well to the dynamic nature of such real-world interactions as they require samples from stationary data distributions. The real-world is not stationary, it changes continuously. In such contexts, the training data and learning objectives may also change rapidly. Continual Learning (CL), by design, is able to address this very problem by learning incrementally. In this paper, we argue that CL is an essential paradigm for creating fully adaptive affective robots (why). To support this argument, we first provide an introduction to CL approaches and what they can offer for various dynamic (interactive) situations (what). We then formulate guidelines for the affective robotics community on how to utilise CL for perception and behaviour learning with adaptation (how). For each case, we reformulate the problem as a CL problem and outline a corresponding CL-based solution. We conclude the paper by highlighting the potential challenges to be faced and by providing specific recommendations on how to utilise CL for affective robotics.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology, Meta & Continual Learning, Robotics & Control

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In Artificial Intelligence

In Experimental and Cognitive Psychology

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In Robotics & Control

German Ignacio Parisi

Citations in the corpus

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “We present only some representative applications in the above two sections, with other more specialized and cross-cutting scenarios to be explored, such as robotics [25], [76], [243], graph learning [219], [423], [496], bioimaging [497], etc.”
Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI) (2021, background): “, [46]) on how agents and robots can effectively and efficiently learn from extended interactions with their users, and long-term memory systems, attention models, and forgetting mechanisms offer stepping stones for achieving efficiency in learning salient information, memory retrieval, and reducing”