Integrated Robotic Skill Acquisition via Sparse Human Guidance

• Kaushal Thaker

  Author

This paper addresses the challenge of creating unified artificial intelligence systems for robots by proposing a novel approach for end-to-end skill learning. We introduce a framework that enables the joint acquisition and refinement of diverse robotic functionalities, from natural language understanding to context-aware motion planning, through a single, weak user feedback mechanism. This method reduces the need for complex, module-specific human intervention, demonstrating that coherent robotic behaviors can be learned efficiently and generalized across tasks with minimal, high-level human input, paving the way for more autonomous and adaptable AI-driven robots.

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