Embedded Files
Images (left to right): Pinto & Gupta ICRA '16, Blundell et al. '16, Chen et al. ICCV '15, Levine et al. ISER '16, Wang et al. '16
In conjunction with NIPS 2016, Barcelona.
Organizers: Chelsea Finn, Raia Hadsell, Dave Held, Sergey Levine, Percy Liang
Videos of the workshop are now available here.
This workshop is located in Area 3 of the Centre Convencions Internacional Barcelona.
Abstract
Deep learning systems that act in and interact with an environment must reason about how actions will change the world around them. The natural regime for such real-world decision problems involves supervision that is weak, delayed, or entirely absent, and the outputs are typically in the context of sequential decision processes, where each decision affects the next input. This regime poses a challenge for deep learning algorithms, which typically excel with: (1) large amounts of strongly supervised data and (2) a stationary distribution of independently observed inputs. The algorithmic tools for tackling these challenges have traditionally come from reinforcement learning, optimal control, and planning, and indeed the intersection of reinforcement learning and deep learning is currently an exciting and active research area. At the same time, deep learning methods for interactive decision-making domains have also been proposed in computer vision, robotics, and natural language processing, often using different tools and algorithmic formalisms from classical reinforcement learning, such as direct supervised learning, imitation learning, and model-based control. The aim of this workshop will be to bring together researchers across these disparate fields. The workshop program will focus on both the algorithmic and theoretical foundations of decision making and interaction with deep learning, and the practical challenges associated with bringing to bear deep learning methods in interactive settings, such as robotics, autonomous vehicles, and interactive agents.
Schedule
Saturday December 10
Morning Session 1
9:00 - 9:15: Introductions
9:15 - 9:40: Joelle Pineau: Deep learning models for natural language interaction
9:40 - 10:05: Honglak Lee: Learning Disentangled Representations with Action-Conditional Future Prediction
10:05 - 10:30: Chris Summerfield: How artificial and biological agents ride the subway
10:30 - 11:00: morning coffee break
Morning session 2:
11:00 - 11:25: Jianxiong Xiao: Bridging the gap between vision and robotics: Where are my labels?
11:25 - 11:35: Spotlight: Fereshteh Sadeghi, Collision Avoidance via Deep RL: Real Vision-Based Flight without a Single Real Image
11:35 - 11:45: Spotlight: Piotr Mirowski, Learning to Navigate in Complex Environments
11:45 - 12:00: morning poster session
12:00 - 14:00: lunch break
Afternoon session 1:
14:00 - 14:25: Abhinav Gupta: Scaling Self-supervision: From one task, one robot to multiple tasks and robots
14:25 - 14:35: Spotlight: Sebastian Höfer, Unsupervised Learning of State Representations for Multiple Tasks
14:35 - 14:45: Spotlight: Jacob Andreas, Modular Multitask Reinforcement Learning with Policy Sketches
14:45 - 15:00: afternoon poster session
15:00 - 15:30: afternoon coffee break (continuation of poster session)
Afternoon session 2:
15:30 - 15:55: Tim Lillicrap: Data-efficient deep reinforcement learning for continuous control
15:55 - 16:20: Raquel Urtasun: The role of perception for action
16:20 - 16:45: Jason Weston: Learning through Dialogue Interactions
16:45 - 16:55: Spotlight: Pararth Shah, Interactive reinforcement learning for task-oriented dialogue management
16:55 - 17:30: contributor “pitch” session
17:30 - 18:15: panel and audience discussion
Invited Talks
Joelle Pineau: Deep learning models for natural language interaction
This talk will review recent contributions by my team towards the problem of building neural models for dialogue agents. I will focus on generative models of dialogue, based on recurrent neural network architectures, and will present results from user studies using open vocabulary task-independent conversations. I will also present a scoring model trained to automatically evaluate dialogue agents, which can alleviate the need for expensive user studies, and show that this trained model can outperform other standard evaluation metrics for dialogue scoring.
Honglak Lee: Learning Disentangled Representations with Action-Conditional Future Prediction
Chris Summerfield: How artificial and biological agents ride the subway
Recent work in artificial intelligence and machine learning has made great strides towards building agents that behave intelligently in complex environments. For example, the Differentiable Neural Computer (DNC, Graves et al 2016) is a neural network with content-addressable external memory that can plan novel shortest-path trajectories random graphs, such as the London Underground system. In my talk, I will discuss this work in the context of studies of planning in humans. I will show evidence that humans plan by searching through hierarchically nested representations of the environment, describing behaviour and brain activity recorded as humans navigated a virtual subway environment.
Jianxiong Xiao: Bridging the gap between vision and robotics: Where are my labels?
Abhinav Gupta: Scaling Self-supervision: From one task, one robot to multiple tasks and robots
Tim Lillicrap: Data-efficient deep reinforcement learning for continuous control
Deep neural networks have recently been combined with reinforcement learning to solve problems such as playing Atari video games from just the raw pixels and rewards. Can the same basic approaches be applied in the context of robotics? One difference between these cases is that Atari games have only a small finite set of possible actions (e.g. up, down, jump, shoot). In robotics, the action selected by an agent at any given moment can be any of an infinite set of commands to move the joints in a large continuous space. I will describe work with model-free, off-policy algorithms that adapt insights from the discrete case and show successful learning for a variety of reaching, manipulation, and locomotion tasks in simulation. Further, I will demonstrate that these off-policy algorithms are data-efficient enough that they can learn a simple manipulation task from scratch with a 7 degree-of-freedom robot in the real world.
Raquel Urtasun: The role of perception for action
Jason Weston: Learning through Dialogue Interactions
A good dialogue agent should have the ability to interact with users. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow the learner to interact with a teacher by both asking and answering questions. We investigate how a learner can benefit from asking questions in both an offline and online reinforcement learning setting. We demonstrate that the learner improves when asking questions. Our work represents a first step in developing end-to-end learned interactive dialogue agents.
This is joint work with Jiwei Li, Alexander H. Miller, Sumit Chopra and Marc'Aurelio Ranzato.
Call for Papers
We invite the submission of extended abstracts related to machine learning methods for domains involving taking actions and interacting with other agents, including, but not limited to, the following application areas:
- robotics
- autonomous driving
- interactive language and dialog systems
- active perception
- navigation
- game playing
Most accepted papers will be presented as posters, but a few selected contributions will be given oral presentations. Accepted papers will be posted in a non-archival format on the workshop website.
Abstracts should be 4 pages long (not including references) in NIPS format. Submissions may include a supplement, but reviewers are not required to read any supplementary material. Abstracts should be submitted by November 8th, 2016 by sending an email to nips2016interaction@gmail.com. Submissions may be anonymized or not, at the authors' discretion. Work that has already appeared in a journal, workshop, or conference (including NIPS 2016) must be significantly extended to be eligible for workshop submission. Work that is currently under review at another venue or has not yet been published in an archival format as of the date of the deadline (Nov 8th) may be submitted. This includes submissions to ICLR, which are welcome.
Important Dates
Submission Deadline: Tuesday, November 8, 2016, any timezone
Acceptance Notification: Tuesday, November 22, 2016
Workshop: Saturday, December 10th, 2016
Registration
Please refer to the NIPS 2016 website for registration details.
Accepted papers
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, Sergey Levine
Modular Multitask Reinforcement Learning with Policy Sketches
Jacob Andreas, Dan Klein, and Sergey Levine
Collision Avoidance via Deep RL: Real Vision-Based Flight without a Single Real Image
Fereshteh Sadeghi, Sergey Levine
Reinforcement Learning with Few Expert Demonstrations
Aravind S. Lakshminarayanan, Sherjil Ozair, Yoshua Bengio
Interactive reinforcement learning for task-oriented dialogue management
Pararth Shah, Dilek Hakkani-Tür, Larry Heck
Learning Visual Servoing with Deep Features and Trust Region Fitted Q-Iteration
Alex X. Lee , Sergey Levine , Pieter Abbeel
Unsupervised Perceptual Rewards for Imitation Learning
Pierre Sermanet, Kelvin Xu, Sergey Levine
Decayed Markov Chain Monte Carlo for Interactive POMDP
Yanlin Han, Piotr Gmytrasiewicz
Learning to Query, Reason, and Answer Questions on Ambiguous Texts
Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerald Tesauro, Satinder Singh
Towards an end to end Dynamic Dialogue System
Vishal Bhalla
3D Simulation for Robot Arm Control with Deep Q-Learning
Stephen James, Edward Johns
In-Hand Robotic Manipulation via Deep Reinforcement Learning
Kapil D. Katyal, Edward W. Staley, Matthew S. Johannes, I-Jeng Wang, Austin Reiter, Phillipe Burlina
Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks
Sahand Sharifzadeh, Ioannis Chiotellis, Rudolph Triebel, Daniel Cremers
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Arna Ghosh, Biswarup Bhattacharya, Somnath Basu Roy Chowdhury
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
Gregory Kahn, Vitchyr Pong, Pieter Abbeel, Sergey Levine
Real-time Model-based Reinforcement Learning with Deep Function Approximation
Hussain Kazmi, Johan Driesen
Path Integral Guided Policy Search
Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine
Shape-independent Hardness Estimation Using a GelSight Tactile Sensor
Wenzhen Yuan, Chenzhuo Zhu, Andrew Owens, Mandayam Srinivasan, Edward Adelson
Learning to Navigate in Complex Environments
Piotr Mirowski , Razvan Pascanu , Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, Dharshan Kumaran, Raia Hadsell
Unsupervised Learning of State Representations for Multiple Tasks
Antonin Raffin, Sebastian Höfer, Rico Jonschkowski, Oliver Brock, Freek Stulp
End-to-End Learnable Histogram Filters
Rico Jonschkowski, Oliver Brock
Ilya Kostrikov, Dumitru Erhan, Sergey Levine
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Ashvin Nair, Pulkit Agrawal, Dian Chen, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine
GuessWhat?! Visual Object Discovery Through Multi-Modal Dialogue
Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron Courville
Contributor Pitch Session
During our workshop, attendees were invited to sign up for a 3-minute slot in the "pitch session," during which time they could present an interesting idea, a discussion point, late-breaking work, or some other point they wished to share with the group. The pitches that were presented are listed below:
Rico Jonschkowski - Combining Algorithms and Deep Learning
Denis Steckelmacher - Hierarchical RL in POMDPs with Options
Eric Danziger - Conditioning policies on tasks
Grady Williams (gradyrw@gmail.com) - Benchmarking Deep Control and Perception Algorithms with Aggressive Driving
Jay McClelland - [No title]
Page updated
Google Sites
Report abuse