Nick Rhinehart

, PhD Candidate, CMU



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About Me

I'm a Ph.D Candidate at the Robotics Institute within the School of Computer Science at Carnegie Mellon University.

"How should we learn, interpret, quantify, and leverage models that reason about the future?"

Towards this question and others, I work on Reinforcement Learning and Imitation Learning methods at the interface of Computer Vision and Machine Learning. I'm specifically interested in building decision-theoretic models that leverage rich perception sources to drive activity forecasting, functional understanding, general prediction, and general control tasks. My research interests include forward and inverse reinforcement learning, imitation learning, activity analysis, generative modeling, egocentric vision, and recognition. I currently collaborate with Kris Kitani, Sergey Levine, Paul Vernaza, and Drew Bagnell.

In the past, I've worked with Paul Vernaza and Manmohan Chandraker at NEC Labs America, Yoichi Sato and Ryo Yonetani at The University of Tokyo, and Drew Bagnell at Uber ATG. I graduated from Swarthmore College with a degree in CS and a degree in Engineering. At Swarthmore I worked with Matt Zucker.


I am currently on the faculty market.


News



Past and Present Affiliations



Publications


Deep Imitative Models for Flexible Inference, Planning, and Control
N. Rhinehart, R. McAllister, S. Levine

arXiv, 2018

[show abstract] [show bib] [arXiv] [project page] [youtube]


			
		      

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First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning
N. Rhinehart, K. Kitani

TPAMI 2018

[show abstract] [show bib] [get TPAMI pdf] [conference pdf] [project page] [IEEE]


			    
			  

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Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
Arjun Sharma, Mohit Sharma, Nicholas Rhinehart, K. M. Kitani

arXiv 2018

[show abstract] [show bib] [arXiv]


      
    

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R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
N. Rhinehart, K. M. Kitani, P. Vernaza

ECCV, 2018

[show abstract] [show bib] [pdf] [supplement] [project page] [dataset soon] [talk] [short video] [poster]


			
		      

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Learning Neural Parsers with Deterministic Differentiable Imitation Learning
T. Shankar, N. Rhinehart, K. Muelling, K. M. Kitani

CORL, 2018

[show abstract] [show bib] [arxiv] [code]


			  
		        

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Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
X. Pan, E. Ohn-Bar, N. Rhinehart, Y. Xu, Y. Shen, K. M. Kitani

AAMAS 2018

[show abstract] [show bib] [arxiv]


		      
		    

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N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
A. Ashok, N. Rhinehart, F. Beainy, K. Kitani

ICLR 2018

[show abstract] [show bib] [arxiv] [openreview] [code]


		      
		    

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Predictive-State Decoders: Encoding the Future Into Recurrent Neural Networks
N. Rhinehart*, A. Venkataraman*, W. Sun, L. Pinto, M. Hebert, B. Boots, K. Kitani, J. A. Bagnell

NIPS 2017

[show abstract] [show bib] [arxiv]


		        
		      

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First-Person Activity Forecasting with Online Inverse Reinforcement Learning
N. Rhinehart, K. Kitani

ICCV 2017

Marr Prize (Best Paper) Honorable Mention Award.
[show abstract] [show bib] [project page] [recorded talk] [MaxEnt code] [get TPAMI pdf] [conference pdf]


			    
			  

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Learning Action Maps of Large Environments Via First-Person Vision
N. Rhinehart, K. Kitani

CVPR 2016

[show abstract] [show bib] [pdf] [slides] [arxiv] [IEEE]


				  
			      

@InProceedings{Rhinehart2016CVPR,
  author = {Rhinehart, Nicholas and Kitani, Kris M.},
  title = {Learning Action Maps of Large Environments via First-Person Vision},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2016}
  } 

Visual Chunking: A List Prediction Framework for Region-Based Object Detection
N. Rhinehart, J. Zhou, M. Hebert, J. A. Bagnell

ICRA 2015

[show abstract] [show bib] [pdf] [poster (key)] [poster (pdf)] [youtube]


				 
			      

@inproceedings{rhinehart2015visual,
  title={Visual chunking: A list prediction framework for region-based object detection},
  author={Rhinehart, Nicholas and Zhou, Jiaji and Hebert, Martial and Bagnell, J Andrew},
  booktitle={Robotics and Automation (ICRA), 2015 IEEE International Conference on},
  pages={5448--5454},
  year={2015},
  organization={IEEE}
}

Fine-Grained Detection via Efficient Extreme Classification
N. Rhinehart, J. Zhou, M. Hebert, J. A. Bagnell

NIPS 2014 workshop

[show abstract] [show bib] [poster (pdf) ] [poster (pptx)]


			    
			  

@inproceedings{rhinehart2015visual,
  title={Visual chunking: A list prediction framework for region-based object detection},
  author={Rhinehart, Nicholas and Zhou, Jiaji and Hebert, Martial and Bagnell, J Andrew},
  booktitle={Robotics and Automation (ICRA), 2015 IEEE International Conference on},
  pages={5448--5454},
  year={2015},
  organization={IEEE}
}


Unrefereed Research


Flight Autonomy in Obstacle-Dense Environments
N. Rhinehart, D. Dey, J. A. Bagnell
Robotics Institute Summer Scholars Symposium, August 2011;
Sigma-Xi Research Symposium, October, 2011
[poster (pdf)] [youtube]


Other Unrefereed Projects


Fast SFM-Based Localization of Temporal Sequences and Ground-Plane Hypothesis Consensus
Project for 16-822 Geometry Based Methods in Computer Vision, May, 2015
[pdf] [video (mp4)]
Online Anomaly Detection in Video
Project for 16-831 Statistical Techniques in Robotics, December, 2014
[pdf]
Autonomous Localization and Navigation of Humanoid Robot
Swarthmore College Senior Thesis Project, May, 2012
[pdf]


Miscellaneous Projects

Miscellaneous old projects


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© Nick Rhinehart