Learning is a fundamental capability in C-HRI systems. Robots must adapt to individual users, environments, and evolving tasks. Continuous learning allows interaction quality to https://c-hri.org/ improve over time.
Machine learning techniques enable robots to recognize patterns in human behavior. From preferences to task strategies, robots refine their responses through experience. Personalization increases user comfort and efficiency.
Cognitive adaptation also involves error recovery. Robots learn from interaction failures and adjust future behavior accordingly. This resilience is crucial in dynamic, real-world settings.
Human feedback plays a critical role in robot learning. Explicit corrections and implicit behavioral cues guide system updates. Collaborative learning strengthens the human–robot partnership.
Adaptive C-HRI systems move beyond rigid automation. They evolve alongside human partners. This adaptability is key to long-term deployment and acceptance.
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