Proceedings ArticleDOI
A convex approach to inverse optimal control and its application to modeling human locomotion
Anne-Sophie Puydupin-Jamin,Miles J. Johnson,Timothy Bretl +2 more
- pp 531-536
TLDR
This work assumes that decisions are only approximately optimal and tries to minimize the extent to which observed decisions violate first-order necessary conditions for optimality, which leads to an efficient method of solution as an unconstrained least-squares problem.Abstract:
Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as an unconstrained least-squares problem We apply this approach to both simulated and experimental data to obtain a simple model of human walking trajectories This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areasread more
Citations
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Proceedings ArticleDOI
Inverse optimal control for deterministic continuous-time nonlinear systems
TL;DR: This work presents a new method of inverse optimal control based on minimizing the extent to which observed trajectories violate first-order necessary conditions for optimality, which is more computationally efficient than prior methods, performs similarly to prior approaches under large perturbations to the system, and better learns the true cost function under small perturbation.
Journal ArticleDOI
Inverse KKT: Learning cost functions of manipulation tasks from demonstrations:
TL;DR: The framework of Inverse KKT is proposed, which assumes that the demonstrations fulfill the Karush–Kuhn–Tucker conditions of an unknown underlying constrained optimization problem, and extracts parameters of this underlying problem.
Journal ArticleDOI
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior
Fanta Camara,Nicola Bellotto,Serhan Cosar,Florian Weber,Dimitris Nathanael,Matthias Althoff,Jingyuan Wu,Johannes Ruenz,André Dietrich,Gustav Markkula,Anna Schieben,Fabio Tango,Natasha Merat,Charles Fox +13 more
TL;DR: This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified.
Journal ArticleDOI
Inverse Optimization: Closed-Form Solutions, Geometry, and Goodness of Fit
TL;DR: In this paper, a unified framework for cost function estimation in linear optimization comprising a general inverse optimization model and a corresponding goodness-of-fit metric is presented, inspired by regression.
Journal ArticleDOI
From inverse optimal control to inverse reinforcement learning: A historical review
TL;DR: The history of the IOC and Inverse Reinforcement Learning approaches is reviewed and the connections and differences between them are described to cover the research gap in the existing literature and the general formulation of IOC/IRL is described.
References
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Book
Linear and nonlinear programming
David G. Luenberger,Yinyu Ye +1 more
TL;DR: Strodiot and Zentralblatt as discussed by the authors introduced the concept of unconstrained optimization, which is a generalization of linear programming, and showed that it is possible to obtain convergence properties for both standard and accelerated steepest descent methods.
Proceedings ArticleDOI
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel,Andrew Y. Ng +1 more
TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.
Journal ArticleDOI
Autonomous Helicopter Aerobatics through Apprenticeship Learning
TL;DR: These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics, including the first autonomous execution of a wide range of maneuvers, including in-place flips, in- place rolls, loops and hurricanes.
Proceedings ArticleDOI
Unfreezing the robot: Navigation in dense, interacting crowds
Pete Trautman,Andreas Krause +1 more
TL;DR: IGP is developed, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data that naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation.