Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 6, Issue 3, Pages 5485-5492Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2021.3074880
Keywords
Multi-Robot systems; motion and path planning; intelligent transportation systems
Categories
Funding
- NSF NRI [1830402]
- DARPA YFA [D18AP00064]
- ONR [N00014-18-1-2830]
- Toyota Research Institute (TRI)
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1830402] Funding Source: National Science Foundation
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LUCIDGames is an inverse optimal control algorithm that can estimate other agents' objective functions in real time and incorporate those estimates into a game-theoretic planner. The algorithm solves the inverse optimal control problem by recasting it in a recursive parameter-estimation framework, using an unscented Kalman filter to iteratively update a Bayesian estimate of the other agents' cost function parameters.
Existing game-theoretic planning methods assume that the robot knows the objective functions of the other agents a priori while, in practical scenarios, this is rarely the case. This letter introduces LUCIDGames, an inverse optimal control algorithm that is able to estimate the other agents' objective functions in real time, and incorporate those estimates online into a recedinghorizon game-theoretic planner. LUCIDGames solves the inverse optimal control problem by recasting it in a recursive parameter-estimation framework. LUCIDGames uses an unscented Kalman filter (UKF) to iteratively update a Bayesian estimate of the other agents' cost function parameters, improving that estimate online as more data is gathered from the other agents' observed trajectories. The planner then takes account of the uncertainty in the Bayesian parameter estimates of other agents by planning a trajectory for the robot subject to uncertainty ellipse constraints. The algorithm assumes no explicit communication or coordination between the robot and the other agents in the environment. An MPC implementation of LUCIDGames demonstrates real-time performance on complex autonomous driving scenarios with an update frequency of 40 Hz. Empirical results demonstrate that LUCIDGames improves the robot's performance over existing game-theoretic and traditional MPC planning approaches. Our implementation of LUCIDGames is available at https://github.com/RoboticExplorationLab/LUCIDGames.jl.
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