An Efficient Learning-Based Task Planning Approach Using a Bio-Inspired Action Context-Free Grammar for Bimanual Manipulation

Abstract

Task and Motion Planning (TAMP) frameworks for bimanual robots are limited by the combinatorial explosion at the task planning level, which can negatively affect human-robot interaction. This work introduces BAG-Learn Planning, an efficient learning-based task planning approach that combines a Bio-Inspired Action Context-Free Grammar (BAG) with a Long-Short-Term Memory (LSTM) network to infer symbolic task plans and achieve bimanual manipulation. The proposed approach replaces costly symbolic search with efficient inference by formulating task planning as sequence prediction over grammar-compliant symbolic representations. Experimental comparisons with the classical Fast Downward task planner across three activities demonstrate significant reductions in task planning time, with millisecond-scale planning achieved for both seen and unseen goals. Additional results show robustness to increasing numbers of objects and symbolic locations, thus mitigating combinatorial explosion. BAG-Learn Planning is integrated with a Rapidly Exploring Random Tree (RRT) motion planner to form a complete TAMP framework. The latter is deployed on a physical bimanual robotic platform to achieve three household activities: pouring, opening, and passing.