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.



News



Past and Present Affiliations



Publications


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

ECCV, 2018

[preprint soon] [dataset soon]

Abstract:


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

arXiv, 2018

[show abstract] [show bib] [arxiv]

Abstract:


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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]

Abstract:


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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]

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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] [pdf] [arxiv] [recorded talk] [MaxEnt code]

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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]

Abstract: When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment. Our goal is to automate dense functional understanding of large spaces by leveraging sparse activity demonstrations recorded from an ego-centric viewpoint. The method we describe enables functionality estimation in large scenes where people have behaved, as well as novel scenes where no behaviors are observed. Our method learns and predicts "Action Maps", which encode the ability for a user to perform activities at various locations. With the usage of an egocentric camera to observe human activities, our method scales with the size of the scene without the need for mounting multiple static surveillance cameras and is well-suited to the task of observing activities up-close. We demonstrate that by capturing appearance-based attributes of the environment and associating these attributes with activity demonstrations, our proposed mathematical frame- work allows for the prediction of Action Maps in new environments. Additionally, we offer a preliminary glance of the applicability of Action Maps by demonstrating a proof-of-concept application in which they are used in concert with activity detections to perform localization.

@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]

Abstract: We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms' behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.

@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)]

Abstract: We consider detecting objects in an image by iteratively selecting from a set of arbitrarily shaped candidate regions. Our generic approach, which we term visual chunking, reasons about the locations of multiple object instances in an image while expressively describing object boundaries. We design an optimization criterion for measuring the performance of a list of such detections as a natural extension to a common per-instance metric. We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals. We also develop a simple class-specific algorithm to generate a candidate region instance in near-linear time in the number of low-level superpixels that outperforms other region generating methods. In order to make predictions on novel images at testing time without access to ground truth, we develop learning approaches to emulate these algorithms' behaviors. We demonstrate that our new approach outperforms sophisticated baselines on benchmark datasets.

@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