About me

I'm Nick Rhinehart, a Ph.D Student at the Robotics Institute within the School of Computer Science at Carnegie Mellon University.

For systems to be generally intelligent, they must be able to reason about the future—
How should we learn, interpret, quantify, and leverage models that reason about the future?


Towards this question and others, I work on RL and IL 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 inform forecasting and control tasks. I currently collaborate with Kris Kitani, Sergey Levine, Paul Vernaza, and Drew Bagnell.

In the past, I've worked with Sergey Levine at UC Berkeley, 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.


News

Publications

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

N. Rhinehart, R. McAllister, K. M. Kitani, S. Levine

Best Paper, ICML 2019 Workshop on AI for Autonomous Driving
arXiv 2019 | pdf | show abs | show bib | project page | visualization code


	  		
	  	      

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Generative Hybrid Representations for Activity Forecasting with No-Regret Learning

J. Guan, Y. Yuan, K. M. Kitani, N. Rhinehart

arXiv 2019 | pdf | show abs | show bib


			
		      

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Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

M. Sharma, A. Sharma, N. Rhinehart, K. M. Kitani

ICLR 2019 | pdf | show abs | show bib | project page


			
		      

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Deep Imitative Models for Flexible Inference, Planning, and Control

N. Rhinehart, R. McAllister, S. Levine

arXiv 2018 | pdf | show abs | show bib | project page


			
		      

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First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning

N. Rhinehart, K. Kitani

TPAMI 2018 | pdf | show abs | show bib | project page


			    
			  

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R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting

N. Rhinehart, K. M. Kitani, P. Vernaza

ECCV 2018 | pdf | show abs | show bib | project page | supplement | blog post | dataset soon


			
		      

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Learning Neural Parsers with Deterministic Differentiable Imitation Learning

T. Shankar, N. Rhinehart, K. Muelling, K. M. Kitani

CORL 2018 | pdf | show abs | show bib | 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 | pdf | show abs | show bib


		      
		    

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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 | pdf | show abs | show bib | 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 | pdf | show abs | show bib


		        
		      

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First-Person Activity Forecasting with Online Inverse Reinforcement Learning

N. Rhinehart, K. Kitani

Best Paper Honorable Mention (3 of 2,143 submissions)
ICCV 2017 | pdf | show abs | show bib | project page | code


			    
			  

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Learning Action Maps of Large Environments Via First-Person Vision

N. Rhinehart, K. Kitani

CVPR 2016 | pdf | show abs | show bib



				  
			      

@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 | pdf | show abs | show bib


				 
			      

@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 Work


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


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

Misc. undergrad projects


© Nick Rhinehart