Robotic manipulation demands generalist systems that adapt to diverse tasks without task-specific retraining. Typically, these roles are handled by foundation models for high-level planning or simulation for motion validation, but existing approaches fail to unify semantic reasoning with physical feasibility checks bef...
Gustav Bay Baastrup, Kasper B. Lauritsen, Signe Møller-Skuldbøl et al.· IEEE International Conferenc...· 0 citations
A multi-modal, data-driven machine learning model for classifying behavioral patterns associated with distinct trust states under controlled trust-inducing conditions is introduced and demonstrates the effectiveness of multi-modal behavior analysis and sensor data fusion for accurate trust classification in human–robot...
As Industry 5.0 emphasizes a human-centric manufacturing paradigm, effective training of operators for Human-Robot Collaboration is increasingly important. This study compares three pedagogically distinct training strategies, namely Imitation Learning, Cognitive Apprenticeship, and Self-Regulated Learning, to evaluate...
Giulio Campagna, Dimitrios Chrysostomou, A. Lassen· IEEE Transactions on Automat...· 0 citations
This work introduces IRWOZ 2.0, which addresses limitations through large language model (LLM) enhanced generation (Mistral/Claude-3.5) and quality refinements and expands the dataset to 390 dialogues across 4 industrial domains, featuring manual corrections and automated typo removal.
Chen Li, Dimitrios Chrysostomou· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
