Cross-entropy temporal logic motion planning

Livingston, Scott C.; Wolff, Eric M.; Murray, Richard M.

doi:10.1145/2728606.2728635

Cited by 17 publications

(9 citation statements)

References 27 publications

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“…It is however not applicable to discrete domains as STB. Recently, cross entropy planning has been used for searching sequences that satisfy a given temporal logic formula [13] in a continuous motion planning setting.…”

Section: Related Workmentioning

confidence: 99%

Stacked Thompson Bandits

Belzner

Gabor

2017

2017 IEEE/ACM 3rd International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS)

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We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement. STB uses a simulation for evaluation of plans, and takes a Bayesian approach to using the resulting information to guide its search. In particular, we show that stacking multiarmed bandits and using Thompson sampling to guide the action selection process for each bandit enables STB to generate plans that satisfy requirements with a high probability while only searching a fraction of the search space.

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Section: Related Workmentioning

confidence: 99%

Stacked Thompson Bandits

Belzner

Gabor

2017

2017 IEEE/ACM 3rd International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS)

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“…In the context of multi-agent system, synthesis constructs a controller that directs agent interactions in order to satisfy the specification. Among others, the synthesis of controllers finds vast application in motion planning in single-and multiple-robot systems [3,13,15,16,22].…”

Section: Introductionmentioning

confidence: 99%

Equilibria in Quantitative Concurrent Games

Almagor¹,

Alur²,

Bansal³

2018

Preprint

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Synthesis of finite-state controllers from high-level specifications in multi-agent systems can be reduced to solving multi-player concurrent games over finite graphs. The complexity of solving such games with qualitative objectives for agents, such as reaching a target set, is well understood resulting in tools with applications in robotics. In this paper, we introduce quantitative concurrent graph games, where transitions have separate costs for different agents, and each agent attempts to reach its target set while minimizing its own cost along the path. In this model, a solution to the game corresponds to a set of strategies, one per agent, that forms a Nash equilibrium. We study the problem of computing the set of all Pareto-optimal Nash equilibria, and give a comprehensive analysis of its complexity and related problems such as the price of stability and the price of anarchy. In particular, while checking the existence of a Nash equilibrium is NP-complete in general, with multiple parameters contributing to the computational hardness separately, two-player games with bounded costs on individual transitions admit a polynomial-time solution.

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“…The aim is to find a value function approximation that minimizes an upper bound on the total cost. Similar crossentropy (CE) methods have been used for trajectory planning in the past [13,19], with the goal of finding a sequence of motion primitives or a sequence of states for interpolationbased planning. In stochastic policy optimization [14], CE is also useful for computing linear feedback policies.…”

Section: Introductionmentioning

confidence: 99%

Sampling-based Approximate Optimal Control Under Temporal Logic Constraints

Papusha

Topcu

2017

Proceedings of the 20th International Conference on Hybrid Systems: Computation and Control

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We investigate a sampling-based method for optimal control of continuous-time and continuous-state (possibly nonlinear) systems under co-safe linear temporal logic specifications. We express the temporal logic specification as a deterministic, finite automaton (the specification automaton), and link the automaton's discrete transitions to the continuous system state as it passes through specified regions. The optimal hybrid controller is characterized by a set of coupled partial di↵erential equations. Because these equations are di cult to solve exactly in practice in all cases, we propose instead a sampling based technique to solve for an approximate controller through approximate value iteration. We adopt model reference adaptive search-an importance sampling optimization algorithm-to determine the mixing weights of the approximate value function expressed in a finite basis. Under mild technical assumptions, the algorithm converges, with probability one, to an optimal weight that ensures the satisfaction of temporal logic constraints, while minimizing an upper bound for the optimal cost. We demonstrate the correctness and e ciency of the method through numerical experiments, including temporal logic planning for a linear system, and a nonlinear mobile robot.

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Cross-entropy temporal logic motion planning

Cited by 17 publications

References 27 publications

Stacked Thompson Bandits

Stacked Thompson Bandits

Equilibria in Quantitative Concurrent Games

Sampling-based Approximate Optimal Control Under Temporal Logic Constraints

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