Continuous Relaxation of Symbolic Planner for OneShot Imitation Learning We address oneshot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direction, most of the approaches still require a few hundred tasks for metatraining, which limits the scalability of the approaches. Our main contribution is to formulate oneshot imitation learning as a symbolic planning problem along with the symbol grounding problem. This formulation disentangles the policy execution from the intertask generalization and leads to better data efficiency. The key technical challenge is that the symbol grounding is prone to error with limited training data and leads to subsequent symbolic planning failures. We address this challenge by proposing a continuous relaxation of the discrete symbolic planner that directly plans on the probabilistic outputs of the symbol grounding model. Our continuous relaxation of the planner can still leverage the information contained in the probabilistic symbol grounding and significantly improve over the baseline planner for the oneshot imitation learning tasks without using large training data.

LEGO: Leveraging Experience in Roadmap Generation for SamplingBased Planning We consider the problem of leveraging prior experience to generate roadmaps in samplingbased motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. Stateoftheart is to train a conditional variational autoencoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along nearoptimal paths that are otherwise difficult tosample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.