Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning
A new model for autonomous robot learning has been proposed, focusing on a thinking-learning interaction approach. This model allows robots to adapt to changing environments by moving beyond predefined learning settings. Experimental results indicate significant improvements in recognition accuracy and action routine efficiency.
- ▪The proposed model enhances robots' ability to adapt by integrating thinking and learning processes.
- ▪Experimental results show an increase in recognition accuracy from 0.419 to 0.845.
- ▪The model supports adaptive input feature discovery and action routine reconstruction.
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Computer Science > Artificial Intelligence arXiv:2605.23987 (cs) [Submitted on 17 May 2026] Title:Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning Authors:Hong Su View a PDF of the paper titled Beyond Predefined Learning Objects: A Thinking-Learning Interaction Model for Up-to-Date Autonomous Robot Learning, by Hong Su View PDF HTML (experimental) Abstract:Autonomous robots operating in open and changing environments cannot always rely on predefined inputs, outputs, and action routines.
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