James Davidson
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Publication
Featured researches published by James Davidson.
conference on recommender systems | 2010
James Davidson; Benjamin Liebald; Junning Liu; Palash Nandy; Taylor Van Vleet; Ullas Gargi; Sujoy Gupta; Yu He; Mike Lambert; Blake Livingston; Dasarathi Sampath
We discuss the video recommendation system in use at YouTube, the worlds most popular online video community. The system recommends personalized sets of videos to users based on their activity on the site. We discuss some of the unique challenges that the system faces and how we address them. In addition, we provide details on the experimentation and evaluation framework used to test and tune new algorithms. We also present some of the findings from these experiments.
computer vision and pattern recognition | 2017
Saurabh Gupta; James Davidson; Sergey Levine; Rahul Sukthankar; Jitendra Malik
We introduce a neural architecture for navigation in novel environments. Our proposed architecture learns to map from first-person views and plans a sequence of actions towards goals in the environment. The Cognitive Mapper and Planner (CMP) is based on two key ideas: a) a unified joint architecture for mapping and planning, such that the mapping is driven by the needs of the planner, and b) a spatial memory with the ability to plan given an incomplete set of observations about the world. CMP constructs a top-down belief map of the world and applies a differentiable neural net planner to produce the next action at each time step. The accumulated belief of the world enables the agent to track visited regions of the environment. Our experiments demonstrate that CMP outperforms both reactive strategies and standard memory-based architectures and performs well in novel environments. Furthermore, we show that CMP can also achieve semantically specified goals, such as go to a chair.
international conference on robotics and automation | 2017
Lerrel Pinto; James Davidson; Abhinav Gupta
There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots. For supplementary video see: youtu.be/QfK3Bqhc6Sk
european conference on computer vision | 2018
Tian Ye; Xiaolong Wang; James Davidson; Abhinav Gupta
Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes in mass, friction, etc. It is postulated this is primarily because we learn to model physics with meaningful latent variables. This does not imply we can estimate the precise values of these meaningful variables (estimate exact values of mass or friction). Inspired by this observation, we propose an interpretable intuitive physics model where specific dimensions in the bottleneck layers correspond to different physical properties. In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.) and test on collisions of unseen combinations of shapes. Furthermore, we demonstrate our model generalizes well even when similar scenes are simulated with different underlying properties.
international conference on machine learning | 2017
Lerrel Pinto; James Davidson; Rahul Sukthankar; Abhinav Gupta
arXiv: Learning | 2018
Luke Metz; Julian Ibarz; Navdeep Jaitly; James Davidson
arXiv: Learning | 2017
Danijar Hafner; James Davidson; Vincent Vanhoucke
international conference on robotics and automation | 2018
Aleksandra Faust; Kenneth Oslund; Oscar Ramirez; Anthony Gerald Francis; Lydia Tapia; Marek Fiser; James Davidson
international conference on robotics and automation | 2018
Xinchen Yan; Jasmined Hsu; Mohammad Khansari; Yunfei Bai; Arkanath Pathak; Abhinav Gupta; James Davidson; Honglak Lee
arXiv: Learning | 2018
Ashley D. Edwards; Laura Downs; James Davidson