Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Towards opportunistic action selection in human-robot cooperation
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Designing a BIM-based serious game for fire safety evacuation simulations
Advanced Engineering Informatics
On efficient sensor scheduling for linear dynamical systems
Automatica (Journal of IFAC)
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Moving object detection with laser scanners
Journal of Field Robotics
Understanding suitable locations for waiting
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Legible user input for intent prediction
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
TherML: occupancy prediction for thermostat control
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
A policy-blending formalism for shared control
International Journal of Robotics Research
Probabilistic movement modeling for intention inference in human-robot interaction
International Journal of Robotics Research
CHOMP: Covariant Hamiltonian optimization for motion planning
International Journal of Robotics Research
Human-aware robot navigation: A survey
Robotics and Autonomous Systems
Learning intentions for improved human motion prediction
Robotics and Autonomous Systems
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We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.