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Abstract
As human-robot collaboration (HRC) becomes central to Industry 4.0, integrating sensorimotor learning and contextual awareness is critical to enable robots to perform adaptively and safely in dynamic industrial environments. This paper investigates the synergistic application of sensorimotor learning techniques and context-aware mechanisms to enhance robotic responsiveness, adaptability, and decision-making in real-time human-robot interactions. It provides a literature-grounded perspective on current progress and gaps, emphasizing multimodal perception, reinforcement learning, and cognitive architectures. Results from reviewed studies demonstrate the importance of embedding task context and environmental cues into robotic control systems for seamless cooperation with human workers.
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