Spotlights: Deep Object-Centric Representations for Generalizable Robot Learning < Coline Devin>

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

Learning Deep Composable Maximum-Entropy Policies for Real-World Robotic Manipulation

SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Control

Learning Flexible and Reusable Locomotion Primitives for a Microrobot

Policy Search using Robust Bayesian Optimization

Learning Robotic Assembly from CAD

Learning Robot Skill Embeddings

Self-Supervised Visual Planning with Temporal Skip Connections

Overcoming Exploration in Reinforcement Learning with Demonstrations

Deep Reinforcement Learning for Vision-Based Robotic Grasping

Soft Value Iteration Networks for Planetary Rover Path Planning

Posters: One-Shot Visual Imitation Learning via Meta-Learning

One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay < Jake Bruce; Niko Suenderhauf; Piotr Mirowski; Raia Hadsell; Michael Milford >

Bayesian Active Edge Evaluation on Expensive Graphs < Sanjiban Choudhury >

Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control < Mengyuan Yan; Iuri Frosio*; Stephen Tyree; Kautz Jan >

Learning Robotic Manipulation of Granular Media < Connor Schenck*; Jonathan Tompson; Dieter Fox; Sergey Levin>

End-to-End Learning of Semantic Grasping < Eric Jang >

Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

Efficient Robot Task Learning and Transfer via Informed Search in Movement Parameter Space < Nemanja Rakicevic*; Kormushev Petar >

Metrics for Deep Generative Models based on Learned Skills

Unsupervised Hierarchical Video Prediction < Nevan wichers*; Dumitru Erhan; Honglak Lee >

Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

Domain Randomization and Generative Models for Robotic Grasping

Learning to Grasp from Vision and Touch

Neural Network Dynamics Models for Control of Under-actuated Legged Millirobots

On the Importance of Uncertainty for Control with Deep Dynamics Models

Increasing Sample-Efficiency via Online Meta-Learning

Stochastic Variational Video Prediction

(Author information copied from CMT please contact the workshop organisers under nips17robotlearning@gmail.com for any changes)