ContextDependent Search for Generating Paths for Redundant Manipulators in Cluttered Environments We present a contextdependent bidirectional treesearch framework for pointtopoint path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes highquality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.

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.