Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations

被引:20
作者
Andriella, Antonio [1 ]
Torras, Carme [1 ]
Abdelnour, Carla [2 ,3 ]
Alenya, Guillem [1 ]
机构
[1] CSIC UPC, Inst Robot & Informat Ind, C Llorens & Artigas 4-6, Barcelona 08028, Spain
[2] Univ Int Catalunya, Res Ctr, Inst Catala Neurociencies Aplicades, Fundacio ACE, Barcelona, Spain
[3] Univ Int Catalunya, Memory Clin, Inst Catala Neurociencies Aplicades, Fundacio ACE, Barcelona, Spain
基金
欧盟地平线“2020”; 欧洲研究理事会;
关键词
Robot adaptivity; Robot personalisation; Human-robot interaction; Robot-assisted cognitive training; Socially assistive robotics; In situ learning; ALZHEIMERS-DISEASE; MEMORY DEFICITS;
D O I
10.1007/s11257-021-09316-5
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Socially assistive robots have the potential to augment and enhance therapist's effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots' behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist's expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients' performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist's preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human-human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.
引用
收藏
页码:441 / 496
页数:56
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