Escaping Local Minima in SearchBased Planning using Soft Duplicate Detection Searchbased planning for relatively lowdimensional motionplanning problems such as for autonomous navigation and autonomous flight has been shown to be very successful. Such framework relies on laying a grid over a statespace and constructing a set of actions (motion primitives) that connect the centers of cells. However, in some cases such as kinodynamic motion planning, planning for bipedal robots with high balance requirements, computing these actions can be highly nontrivial and often impossible depending on the dynamic constraints. In this paper, we explore a soft version of discretization, wherein the statespace remains to be continuous but the search tries to avoid exploring states that are likely to be duplicates of states that have already been explored. We refer to this property of the search as soft duplicate detection and view it as a relaxation of the standard notion of duplicate detection. Empirically, we show that the search can efficiently compute paths in highlyconstrained settings and outperforms alternatives on several domains.

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.