Debidatta Dwibedi

I am a researcher in Google Brain. I completed my Masters in Robotics from the Robotics Institute at CMU, where I was advised by Martial Hebert. Prior to that I completed my undergrad from IIT Kanpur, where I worked with Amitabha Mukerjee.

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Publications  /  Patents  /  Talks /  Theses /  Misc

Research

I want to build intelligent agents that interact with our world in useful ways.

My research lies at the intersection of machine learning, computer vision and robotics. Presently, I am working on imitation learning from videos.

In the past, I have worked on human-object interaction recognition in videos, object detection, pose estimation, reinforcement learning, game rule learning and image segmentation.

Publications

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
Ilya Kostrikov, Kumar Krishna Agrawal , Debidatta Dwibedi, Sergey Levine , and Jonathan Tompson
International Conference on Learning Representations (ICLR) 2019

Sample efficient imitation learning using off-policy updates and proper handling of terminal states.

paper | abstract | bibtex

Learning Actionable Representations from Visual Observations
Debidatta Dwibedi, Jonathan Tompson, Corey Lynch and Pierre Sermanet
International Conference on Intelligent Robots (IROS) 2018

Control agents from pixels by learning self-supervised representations from videos.

paper | abstract | bibtex | project

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
Debidatta Dwibedi, Ishan Misra and Martial Hebert
International Conference on Computrer Vision (ICCV) 2017

Generate synthetic data for detecting objects in scenes.

paper | abstract | bibtex | code | poster

Deep Cuboid Detection: Beyond 2D Bounding Boxes
Debidatta Dwibedi, Tomasz Malisiewicz, Vijay Badrinarayanan and Andrew Rabinovich
Arxiv Preprint, 2016

Cuboid detector using deep learning: finds cuboids in scenes and localizes their corners.

paper | abstract | bibtex

Characterizing Predicate Arity and Spatial Structure for Inductive Learning of Game Rules
Debidatta Dwibedi and Amitabha Mukerjee
ECCV 2014 Workshop on Computer Vision + Ontology Applied Cross-Disciplinary Technologies 2014

Represent videos as dynamic graphs. Learn rules of games from observing people play games in Kinect videos.

paper | abstract | bibtex | videos

Patents

Deep learning system for cuboid detection

Talks

Temporal Reasoning in Videos Using Convolutional Gated Recurrent Units
2nd Workshop in Brave New Ideas in Video Understanding at CVPR 2018

paper | slides | poster | bibtex

Self-Supervised Representation Learning for Continuous Control
3rd Workshop in Machine Learning in the Planning and Control of Robot Motion at ICRA 2018

Theses

Synthesizing Scenes for Instance Detection
How can we create annotated datasets without humans for tasks like object detection and pose estimation?

Observational Learning of Rules of Games
Can we learn the rules of a game by observing people playing them?

Miscellaneous

Some other unpublished work:

Playing Games with Deep Reinforcement Learning

Towards Pose Estimation of 3D Objects in Monocular Images via Keypoint Detection

HandNet: Using Faster R-CNN to Detect Hands in Egocentric Videos

A Grounded Framework for Gestures and its Applications


this guy's webpage is awesome