Abstract-This paper explores the connection between sensorbased perception and exploration in the context of haptic object identification. The proposed approach combines (i) object recognition from tactile appearance with (ii) purposeful haptic exploration of unknown objects to extract appearance information. The recognition component brings to bear computer vision techniques by viewing tactile sensor readings as images. We present a bag-of-features framework that uses several tactile image descriptors, some adapted from the vision domain, others novel, to estimate a probability distribution over object identity as an unknown object is explored. Haptic exploration is treated as a search problem in a continuous space to take advantage of sampling-based motion planning to explore the unknown object and construct its tactile appearance.Simulation experiments of a robot arm equipped with a haptic sensor at the end-effector provide promising validation, indicating high accuracy in identifying complex shapes from tactile information gathered during exploration. The proposed approach is also validated by using readings from actual tactile sensors to recognize real objects.

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We propose HyDICE, Hybrid DIscrete Continuous Exploration, a multi-layered approach for hybrid-system testing that integrates continuous sampling-based robot motion planning with discrete searching. The discrete search uses the discrete transitions of the hybrid system and coarse-grained decompositions of the continuous state spaces or related projections to guide the motion planner during the search for witness trajectories. Experiments presented in this paper, using a hybrid system inspired by robot motion planning and with nonlinear dynamics associated with each of several thousand modes, provide an initial validation of HyDICE and demonstrate its promise as a hybrid-system testing method. Comparisons to related work show computational speedups of up to two orders of magnitude.

Abstract-This paper presents the Discrete Search Leading continuous eXploration (DSLX) planner, a multi-resolution approach to motion planning that is suitable for challenging problems involving robots with kinodynamic constraints. Initially the method decomposes the workspace to build a graph that encodes the physical adjacency of the decomposed regions. This graph is searched to obtain leads, that is, sequences of regions that can be explored with sampling-based tree methods to generate solution trajectories. Instead of treating the discrete search of the adjacency graph and the exploration of the continuous state space as separate components, DSLX passes information from one to the other in innovative ways. Each lead suggests what regions to explore and the exploration feeds back information to the discrete search to improve the quality of future leads. Information is encoded in edge weights, which indicate the importance of including the regions associated with an edge in the next exploration step. Computation of weights, leads, and the actual exploration make the core loop of the algorithm.Extensive experimentation shows that DSLX is very versatile. The discrete search can drastically change the lead to reflect new information allowing DSLX to find solutions even when sampling-based tree planners get stuck. Experimental results on a variety of challenging kinodynamic motion planning problems show computational speedups of two orders of magnitude over other widely used motion planning methods.

Abstract. This paper develops a novel computational method for the falsification of safety properties specified by syntactically safe linear temporal logic (LTL) formulas φ for hybrid systems with general nonlinear dynamics and input controls. The method is based on an effective combination of robot motion planning and model checking. Experiments on a hybrid robotic system benchmark with nonlinear dynamics show significant speedup over related work. The experiments also indicate significant speedup when using minimized DFA instead of non-minimized NFA, as obtained by standard tools, for representing the violating prefixes of φ.

This paper develops a novel computational method for the falsification of safety properties specified by syntactically safe linear temporal logic (LTL) formulas φ for hybrid systems with general nonlinear dynamics and input controls. The method is based on an effective combination of robot motion planning and model checking. Experiments on a hybrid robotic system benchmark with nonlinear dynamics show significant speedup over related work. The experiments also indicate significant speedup when using minimized DFA instead of non-minimized NFA, as obtained by standard tools, for representing the violating prefixes of φ.