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Long-horizon planning in realistic environments requires the ability toreason over sequential tasks in high-dimensional state spaces with complexdynamics. Classical motion planning algorithms, such as rapidly-exploringrandom trees, are capable of efficiently exploring large state spaces andcomputing long-horizon, sequential plans. However, these algorithms aregenerally challenged with complex, stochastic, and high-dimensional statespaces as well as in the presence of narrow passages, which naturally emerge intasks that interact with the environment. Machine learning offers a promisingsolution for its ability to learn general policies that can handle complexinteractions and high-dimensional observations. However, these policies aregenerally limited in horizon length. Our approach, Broadly-Exploring,Local-policy Trees (BELT), merges these two approaches to leverage thestrengths of both through a task-conditioned, model-based tree search. BELTuses an RRT-inspired tree search to efficiently explore the state space.Locally, the exploration is guided by a task-conditioned, learned policycapable of performing general short-horizon tasks. This task space can be quitegeneral and abstract; its only requirements are to be sampleable and towell-cover the space of useful tasks. This search is aided by atask-conditioned model that temporally extends dynamics propagation to allowlong-horizon search and sequential reasoning over tasks. BELT is demonstratedexperimentally to be able to plan long-horizon, sequential trajectories with agoal conditioned policy and generate plans that are robust.

Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning