Learning Neuro-Symbolic Skills for Bilevel Planning
arXiv:2206.10680
Abstract
Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomposing decision-making into two or more levels of abstraction. In a setting where demonstrations and symbolic predicates are given, prior work has shown how to learn symbolic operators and neural samplers for TAMP with manually designed parameterized policies. Our main contribution is a method for learning parameterized polices in combination with operators and samplers. These components are packaged into modular neuro-symbolic skills and sequenced together with search-then-sample TAMP to solve new tasks. In experiments in four robotics domains, we show that our approach -- bilevel planning with neuro-symbolic skills -- can solve a wide range of tasks with varying initial states, goals, and objects, outperforming six baselines and ablations. Video: https://youtu.be/PbFZP8rPuGg Code: https://tinyurl.com/skill-learning
CoRL 2022
Cited by in corpus (5)
- Text2Motion: From Natural Language Instructions to Feasible Plans
- Neurosymbolic Reinforcement Learning and Planning: A Survey
- DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning
- Learning Top-k Subtask Planning Tree based on Discriminative Representation Pre-training for Decision Making
- Learning Multi-Object Symbols for Manipulation with Attentive Deep Effect Predictors