9 research outputs found
Optimal Cost-Preference Trade-off Planning with Multiple Temporal Tasks
Autonomous robots are increasingly utilized in realistic scenarios with
multiple complex tasks. In these scenarios, there may be a preferred way of
completing all of the given tasks, but it is often in conflict with optimal
execution. Recent work studies preference-based planning, however, they have
yet to extend the notion of preference to the behavior of the robot with
respect to each task. In this work, we introduce a novel notion of preference
that provides a generalized framework to express preferences over individual
tasks as well as their relations. Then, we perform an optimal trade-off
(Pareto) analysis between behaviors that adhere to the user's preference and
the ones that are resource optimal. We introduce an efficient planning
framework that generates Pareto-optimal plans given user's preference by
extending A* search. Further, we show a method of computing the entire Pareto
front (the set of all optimal trade-offs) via an adaptation of a
multi-objective A* algorithm. We also present a problem-agnostic search
heuristic to enable scalability. We illustrate the power of the framework on
both mobile robots and manipulators. Our benchmarks show the effectiveness of
the heuristic with up to 2-orders of magnitude speedup.Comment: 8 pages, 4 figures, to appear in International Conference on
Intelligent Robots and Systems (IROS) 202
Computer Aided Verification
This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency
Reactive synthesis for finite tasks under resource constraints
There are many applications where robots have to operate in environments that other agents can change. In such cases, it is desirable for the robot to achieve a given highlevel task despite interference. Ideally, the robot must decide its next action as it observes the changes in the world, i.e. act reactively. In this paper, we consider a reactive planning problem for finite robotic tasks with resource constraints. The task is represented using a temporal logic for finite behaviors and the robot must achieve the task using limited resources under all possible finite sequences of moves of other agents. We present a formulation for this problem and an approach based on quantitative games. The efficacy of the approach is demonstrated through a manipulation case study
Reactive synthesis for finite tasks under resource constraints
There are many applications where robots have to operate in environments that other agents can change. In such cases, it is desirable for the robot to achieve a given highlevel task despite interference. Ideally, the robot must decide its next action as it observes the changes in the world, i.e. act reactively. In this paper, we consider a reactive planning problem for finite robotic tasks with resource constraints. The task is represented using a temporal logic for finite behaviors and the robot must achieve the task using limited resources under all possible finite sequences of moves of other agents. We present a formulation for this problem and an approach based on quantitative games. The efficacy of the approach is demonstrated through a manipulation case study