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Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach
Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots.
Sketch-Plan-Generalize: Continual Few-Shot Learning of Inductively Generalizable Spatial Concepts
Additionally, the approach should generalize inductively to novel structures of different sizes or complex structures expressed as a hierarchical composition of previously learned concepts.
Learning Neuro-symbolic Programs for Language Guided Robot Manipulation
Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot.
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