Incremental Acquisition and Composition of Robotic Manipulation Skills from Virtual Demonstrations

We propose a unified framework for robotic manipulation that integrates incremental learning from demonstrations, reinforcement learning, and symbolic task planning. The system supports the training of manipulation skills from Virtual Reality (VR) demonstrations, exploiting the ease of integration provided by Unity for recording demonstrations and training policies. A key feature is incremental learning, where policies acquired in early stages (e.g., proximity grasps with multi-fingered hands) are frozen and reused to accelerate the learning of more complex tasks (e.g., grasp-and-lift). Learned and predefined behaviors are stored in a structured repository, which can be flexibly queried during execution. Task composition is achieved through Hierarchical Task Network (HTN) planning, enabling the generation of long-horizon activities. Validation is performed in CoppeliaSim, transferring trained policies to different manipulators and end-effectors and testing with novel objects not seen during training. Preliminary results from the integrated works underscore the potential of the proposed approach, suggesting its scalability toward increasingly complex robotic manipulation tasks.