For a variety of use cases, the utility of autonomous mobile robots is strengthened by a capacity to explore spatial environments with particular focus on objects or phenomena of interest. To generally support such a capacity, an agile, application-agnostic interest operator is constructed to visually guide focused autonomous exploration. An interest operator is proposed that serves to identify unique features in a scene in an application-agnostic manner by maintaining a sort of “short-term memory” and identifying regions that are unique within the context of what has already been ascertained about the scene, not necessarily what is unique or of interest for a given application. The proposed interest operator is particularly suited to render-based mapping and perception, taking advantage of the natural characterization of spaces comprising an environment as free, occupied, and unknown, and the rich, highly-detailed texture information supported in graphical renderings of visual scenes. With focus on two scales of image features, one being driven by discontinuities between surfaces and the other based on variations in texture of surfaces in a region, the proposed interest operator can inform robot sensing and navigation decisions and resource allocation during autonomous exploration. Results from numerical experiments demonstrate the efficacy of the approach.

Human-robot collaboration towards achieving a common goal is most effective when the robot has the capability to estimate the intentions and needs of its human partner, and to plan complementary actions accordingly. To this end, synergistic coupling between inference engines and task-planning algorithms is essential: the earlier the robot can anticipate the actions performed by its partner, the safer and more seamless the interaction between the two parties will be.In this work, we propose a perception-based analytics framework that incorporates discriminative and generative models, which together estimate the current and future class of an action being performed by a human. The analytics leverage a sequence of human skeletal joint locations extracted from a depth map video stream of the human partner. The generative model ingests current and previous joint positions and outputs a sequence of predicted future positions. The discriminative model produces a vector of probabilities indicating the likelihood that the future action belongs to each class within a set of action classes being considered. The information on current and future actions is fed to a task planning module which selects the robot collaborative action that better suits the estimated present and future human states.

No abstract available.

This talk will present reachability analysis as a tool for model checking and controller synthesis for dynamic systems. We will consider the problem of guaranteeing reachability to a given desired subset of the state space while satisfying a safety property defined in terms of state constraints. We allow for nonlinear and hybrid dynamics, and possibly nonconvex state constraints. We use these results to synthesize controllers that ensure safety and reachability properties under bounded model disturbances that vary continuously.

We present an efficient algorithm which computes, for a kinematic point mass moving in the plane, a time-optimal path that visits a sequence of target sets while conservatively avoiding collision with moving obstacles, also modelled as kinematic point masses, but whose trajectories are unknown. The problem is formulated as a pursuit-evasion differential game, and the underlying construction is based on optimal control. The algorithm, which is a variant of the fast marching method for shortest path problems, can handle general dynamical constraints on the players and arbitrary domain geometry (e.g. obstacles, non-polygonal boundaries). Applications to a two-stage game, capture-the-flag, is presented.

The task of planning and controlling robot motion in practical applications is often complicated by the effects of model uncertainties and environment disturbances. We present in this paper a systematic approach for generating robust motion control strategies to satisfy high level specifications of safety, target attainability, and invariance, under unknown but bounded, continuous disturbances. The motion planning task is decomposed into the two sub-problems of finite horizon reach with avoid and infinite horizon invariance. The set of states for which each of the sub-problems is robustly feasible is computed via iterative reachability calculations under a differential game framework. We discuss how the results of this computation can be used to inform selections of control inputs based upon state measurements at run-time and provide an algorithm for implementing the corresponding feedback control policies. Finally, we demonstrate an experimental application of this method to the control of an autonomous helicopter in tracking a moving ground vehicle.

Capture-the-flag is a complex, challenging game that is a useful proxy for many problems in robotics and other application areas. The game is adversarial, with multiple, potentially competing, objectives. This interplay between different factors makes the problem complex, even in the case of only two players. To make analysis tractable, previous approaches often make various limiting assumptions upon player actions. In this paper, we present a framework for analyzing and solving a two-player capture-the-flag game as a zero-sum differential game. Our problem formulation allows each player to make decisions rationally based upon the current player positions, assuming only an upper bound on the movement speeds. Using Hamilton-Jacobi reachability analysis, we compute winning regions for each player as subsets of the joint configuration space and derive the corresponding winning strategies. Simulation results are presented along with implications of the work as a tool for automation-aided decision-making for humans and mixed human-robot teams.