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Toward an idiomatic framework for cognitive robotics

ABSTRACT: Summary Inspired by the “cognitive hourglass” model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by encouraging cooperation and re-use of existing results. This is done by proposing a framework dividing development of cognitive architectures into a series of layers that can be considered partly in isolation, some of which directly relate to other research fields. Finally, we introduce and review some topics essential for the proposed framework. We also outline a set of applications. Graphical abstract Highlights • The proposed framework divides development of cognitive architectures into layers• The framework spans the best-known approaches employed within cognitive robotics• The framework is centered around modern probabilistic programming techniques• Two applications demonstrate the concepts of the framework The bigger picture For many decades, robots have been expected to transfigure the world we live in, and in many ways they already have, by increasingly taking over dull, dirty, and dangerous jobs. However, for robots to integrate fully and seamlessly into human societies, robots need to be able to learn and reason from experience and effectively deal with unpredictable and dynamic environments. Developing robotic systems with such intelligence is a tremendous and difficult task, which has led to the foundation of the new multi-disciplinary scientific field called cognitive robotics, merging research in adaptive robotics, cognitive science, and artificial intelligence. To ease merging research from these scientific fields, we propose a general framework for developing intelligent robotic systems based on recent advancements in the machine learning community. We hope that this framework will aid researchers and practitioners in bringing even more helpful robots into our societies. Damgaard et al. propose a general framework for developing cognitive architectures for robots. The proposed framework divides development into a series of layers separating models of cognition from the algorithms that implement them. Based on modern probabilistic programming and variational inference algorithms, the proposed framework aims to support the four desiderata generic cognition, grand unification, functional elegance, and sufficient efficiency. The concepts of the framework are demonstrated through two robotic applications.

SUBMITTER: Damgaard M

PROVIDER: S-EPMC9278519 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveChemotherapy-related cognitive impairment (CRCI) is a well-known phenomenon among breast cancer survivors. Cognitive impairment among breast cancer survivors can significantly affect their quality of life and ability to function independently. However, there is a lack of specific and focused cognitive intervention to improve their cognitive performances. This study aimed to develop a tailored cognitive intervention framework module by adapting the attention and memory interventions from the Cognitive Rehabilitation Manual of the Brain Injury Interdisciplinary Special Interest Group (BI-SIG) of the American Congress of Rehabilitation Medicine (ACRM) and incorporating them with the relevant exercises for cognitive rehabilitation for Malaysian breast cancer survivors with CRCI based on the consensus agreement of the expert panel.Methods and analysisThe Delphi consensus technique was conducted online to review and evaluate the framework module. A panel of experts, including rehabilitation medicine physicians, occupational therapists, and clinical psychologists in Malaysia, was invited to participate in this study. For each round, the expert consensus was defined as more than 90% of the expert panel agreeing or strongly agreeing with the proposed items.ResultsA total of 33 practitioners completed the three Delphi rounds. 72.7% of the expert panel have been practising in their relevant clinical fields for more than six years (M = 10.67, SD = 5.68). In Round 1, 23% of the experts suggested that the framework module for attention training required further improvements, specifically in the language (M = 1.97, SD = 0.75) and instructions (M = 2.03, SD = 0.71) provided. In Round 2, 15% of the experts recommended additional changes in the instruction (M = 2.15, SD = 0.67) for attention training. Amendments made to the framework module in line with the recommendations provided by the experts resulted in a higher level of consensus, as 94% to 100% of the experts in Round 3 concluded the framework module was suitable and comprehensive for our breast cancer survivors. Following the key results, the objectives were practical, and the proposed approaches, strategies, and techniques for attention and memory training were feasible. The clarity of the instructions, procedures, verbatim transcripts, and timeframe further enhanced the efficacy and utility of the framework module.ConclusionsThis study found out that the cognitive intervention framework module for breast cancer survivors with cognitive impairment following chemotherapy can be successfully developed and feasible to be implemented using Delphi technique.

Project description:Introduction: Robot-assisted therapy for upper extremity (UE) impairments post-stroke has yielded modest gains in motor capacity and little evidence of improved UE performance during activities of daily living. A paradigm shift that embodies principles of motor learning and exercise dependent neuroplasticity may improve robot therapy outcomes by incorporating active problem solving, salience of trained tasks, and strategies to facilitate the transfer of acquired motor skills to use of the paretic arm and hand during everyday activities. Objective: To pilot and test the feasibility of a novel therapy protocol, the Active Learning Program for Stroke (ALPS), designed to complement repetitive, robot-assisted therapy for the paretic UE. Key ALPS ingredients included training in the use of cognitive strategies (e.g., STOP, THINK, DO, CHECK) and a goal-directed home action plan (HAP) to facilitate UE self-management and skill transfer. Methods: Ten participants with moderate impairments in UE function >6 months after stroke received eighteen 1-h treatment sessions 2-3/x week over 6-8 weeks. In addition to ALPS training, individuals were randomly assigned to either robot-assisted therapy (RT) or robot therapy and task-oriented training (RT-TOT) to trial whether the inclusion of TOT reinforced participants' understanding and implementation of ALPS strategies. Results: Statistically significant group differences were found for the upper limb subtest of the Fugl-Meyer Assessment (FMA-UE) at discharge and one-month follow-up favoring the RT group. Analyses to examine overall effects of the ALPS protocol in addition to RT and RT-TOT showed significant and moderate to large effects on the FMA-UE, Motor Activity Log, Wolf Motor Function Test, and hand portion of the Stroke Impact Scale. Conclusion: The ALPS protocol was the first to extend cognitive strategy training to robot-assisted therapy. The intervention in this development of concept pilot trial was feasible and well-tolerated, with good potential to optimize paretic UE performance following robot-assisted therapy.

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Toward an idiomatic framework for cognitive robotics

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