Publications (15)
Interpretable Intuitive Physics Model
Tian Ye, Xiaolong Wang, James Davidson +1
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…
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Marc Pickett, Ayush Sekhari, James Davidson
Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample co…
Visual Representations for Semantic Target Driven Navigation
Arsalan Mousavian, Alexander Toshev, Marek Fiser +3
What is a good visual representation for autonomous agents? We address this question in the context of semantic visual navigation, which is the problem of a robot finding its way t…
TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow
Danijar Hafner, James Davidson, Vincent Vanhoucke
We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow. We simulate multiple environments in par…
Noise Contrastive Priors for Functional Uncertainty
Danijar Hafner, Dustin Tran, Timothy Lillicrap +2
Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open…
Cognitive Mapping and Planning for Visual Navigation
Saurabh Gupta, Varun Tolani, James Davidson +3
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 goa…
Supervision via Competition: Robot Adversaries for Learning Tasks
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 approac…
PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning
Aleksandra Faust, Oscar Ramirez, Marek Fiser +4
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling based path planning with reinforcement learning (RL). The RL agents learn…
Robust Adversarial Reinforcement Learning
Lerrel Pinto, James Davidson, Rahul Sukthankar +1
Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based…
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer +4
Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from intera…
Discrete Sequential Prediction of Continuous Actions for Deep RL
Luke Metz, Julian Ibarz, Navdeep Jaitly +1
It has long been assumed that high dimensional continuous control problems cannot be solved effectively by discretizing individual dimensions of the action space due to the exponen…
Modulated Policy Hierarchies
Alexander Pashevich, Danijar Hafner, James Davidson +2
Solving tasks with sparse rewards is a main challenge in reinforcement learning. While hierarchical controllers are an intuitive approach to this problem, current methods often req…
Learning Hierarchical Information Flow with Recurrent Neural Modules
Danijar Hafner, Alex Irpan, James Davidson +1
We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routin…
Tungsten boride shields in a spherical tokamak
Colin G Windsor, Jack O Astbury, James Davidson +4
The favourable properties of tungsten borides for shielding the central High Temperature Superconductor (HTS) core of a spherical tokamak fusion power plant are modelled using the…
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations
Xinchen Yan, Jasmine Hsu, Mohi Khansari +5
This paper focuses on the problem of learning 6-DOF grasping with a parallel jaw gripper in simulation. We propose the notion of a geometry-aware representation in grasping based o…