Towards self-reliant robots : skill learning, failure recovery, and real-time adaptation: integrating behavior trees, reinforcement learning, and vision-language models for robust robotic autonomy
Robots operating in real-world settings must manage task variability, environmental uncertainty, and failures during execution. This thesis presents a unified framework for building self-reliant robotic systems by integrating symbolic planning, reinforcement learning, behavior trees (BTs), and vision-language models (VLMs).At the core of the approach is an interpretable policy representation based
