Planning Beyond The Sensing Horizon Using a Learned Context Lastmile delivery systems commonly propose the use of autonomous robotic vehicles to increase scalability and efficiency. The economic inefficiency of collecting accurate prior maps for navigation motivates the use of planning algorithms that operate in unmapped environments. However, these algorithms typically waste time exploring regions that are unlikely to contain the delivery destination. Context is key information about structured environments that could guide exploration toward the unknown goal location, but the abstract idea is difficult to quantify for use in a planning algorithm. Some approaches specifically consider contextual relationships between objects, but would perform poorly in objectsparse environments like outdoors. Recent deep learningbased approaches consider context too generally, making trainingtransferability difficult. Therefore, this work proposes a novel formulation of utilizing context for planning as an imagetoimage translation problem, which is shown to extract terrain context from semantic gridmaps, into a metric that an explorationbased planner can use. The proposed framework has the benefit of training on a static dataset instead of requiring a timeconsuming simulator. Across 42 test houses with layouts from satellite images, the trained algorithm enables a robot to reach its goal 189% faster than with a contextunaware planner, and within 63% of the optimal path computed with a prior map. The proposed algorithm is also implemented on a vehicle with a forwardfacing camera in a highfidelity, Unreal simulation of neighborhood houses.

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.