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12/07/2021
Information is Power: Intrinsic Control via Information Capture
Humans and animals explore their environment and acquire useful skills e...
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11/22/2021
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments
Geometric methods for solving open-world off-road navigation tasks, by l...
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07/12/2021
Explore and Control with Adversarial Surprise
Reinforcement learning (RL) provides a framework for learning goal-direc...
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04/21/2021
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Humans have a remarkable ability to make decisions by accurately reasoni...
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04/12/2021
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models
We describe a robotic learning system for autonomous navigation in diver...
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12/17/2020
ViNG: Learning Open-World Navigation with Visual Goals
We propose a learning-based navigation system for reaching visually indi...
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11/19/2020
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Reinforcement learning provides a general framework for flexible decisio...
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10/27/2020
Conservative Safety Critics for Exploration
Safe exploration presents a major challenge in reinforcement learning (R...
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06/26/2020
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Out-of-training-distribution (OOD) scenarios are a common challenge of l...
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03/18/2020
Sequential Forecasting of 100,000 Points
Predicting the future is a crucial first step to effective control, sinc...
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03/18/2020
Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting
Predicting the future is a crucial first step to effective control, sinc...
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05/03/2019
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings
For autonomous vehicles (AVs) to behave appropriately on roads populated...
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04/12/2019
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning
Automatically reasoning about future human behaviors is a difficult prob...
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10/15/2018
Deep Imitative Models for Flexible Inference, Planning, and Control
Imitation learning provides an appealing framework for autonomous contro...
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09/29/2018
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information
The use of imitation learning to learn a single policy for a complex tas...
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06/22/2018
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
Humans are able to understand and perform complex tasks by strategically...
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06/20/2018
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
We address the problem of spatial segmentation of a 2D object in the con...
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09/25/2017
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Recurrent neural networks (RNNs) are a vital modeling technique that rel...
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09/18/2017
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
While bigger and deeper neural network architectures continue to advance...
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12/22/2016
First-Person Activity Forecasting with Online Inverse Reinforcement Learning
We address the problem of incrementally modeling and forecasting long-te...
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10/27/2014
