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In order to safely operate around humans, robots can employ predictive models of human motion.
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Goal inference as inverse planning
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Maximum entropy inverse reinforcement learning
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Reactive path planning in a dynamic environment
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Hao Ding, Gunther Reißig, Kurniawan Wijaya, Dino Bortot, Klaus Bengler, and Olaf Stursberg · 2011
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Sampling-based algorithms for optimal motion planning
Sertac Karaman and Emilio Frazzoli · 2011
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Interaction primitives for human-robot cooperation tasks
Heni Ben Amor, Gerhard Neumann, Sanket Kamthe, Oliver Kroemer, and Jan Peters · 2014
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Analyzing the effects of human-aware motion planning on close-proximity human–robot collaboration
Przemyslaw A Lasota and Julie A Shah · 2015
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Socially compliant mobile robot navigation via inverse reinforcement learning
Henrik Kretzschmar, Markus Spies, Christoph Sprunk, and Wolfram Burgard · 2016
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Fastrack: a modular framework for fast and guaranteed safe motion planning
Sylvia L. Herbert*, Mo Chen*, SooJean Han, Somil Bansal, Jaime F. Fisac, and Claire J. Tomlin · 2017
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Georges S. Aoude, Brandon D. Luders, Joshua M. Joseph, Nicholas Roy, and Jonathan P. How · 2013
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Probabilistic human action prediction and wait-sensitive planning for responsive human-robot collaboration
Kelsey P Hawkins, Nam Vo, Shray Bansal, and Aaron F Bobick · 2013
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Hema Swetha Koppula and Ashutosh Saxena · 2013
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Edward Schmerling, Karen Leung, Wolf Vollprecht, and Marco Pavone · 2017
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Planning, fast and slow: A framework for adaptive real-time safe trajectory planning
David Fridovich-Keil*, Sylvia L Herbert*, Jaime F Fisac*, Sampada Deglurkar, and Claire J Tomlin · 2018
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