Search Results for author:
Found 25 papers, 6 papers with code
Visual Chunking: A List Prediction Framework for Region-Based Object Detection
We present an efficient algorithm with provable performance for building a high-quality list of detections from any candidate set of region-based proposals.
Learning Action Maps of Large Environments via First-Person Vision
When people observe and interact with physical spaces, they are able to associate functionality to regions in the environment.
First-Person Activity Forecasting with Online Inverse Reinforcement Learning
We address the problem of incrementally modeling and forecasting long-term goals of a first-person camera wearer: what the user will do, where they will go, and what goal they seek.
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
Our approach takes a larger `teacher' network as input and outputs a compressed `student' network derived from the `teacher' network.
Predictive-State Decoders: Encoding the Future into Recurrent Networks
We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations.
Learning Neural Parsers with Deterministic Differentiable Imitation Learning
We introduce a novel deterministic policy gradient update, DRAG (i. e., DeteRministically AGgrevate) in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural parser.
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states.
R2P2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting
We propose a method to forecast a vehicle's ego-motion as a distribution over spatiotemporal paths, conditioned on features (e. g., from LIDAR and images) embedded in an overhead map.
Learning Gibbs-regularized GANs with variational discriminator reparameterization
We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.
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 task that has multiple modes or hierarchical structure can be challenging.
Deep Imitative Models for Flexible Inference, Planning, and Control
Yet, reward functions that evoke desirable behavior are often difficult to specify.
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning
Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings
For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information.
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments
Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche.
Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting
Through experiments on a robotic manipulation dataset and two driving datasets, we show that SPFNet is effective for the SPF task, our forecast-then-detect pipeline outperforms the detect-then-forecast approaches to which we compared, and that pose forecasting performance improves with the addition of unlabeled data.
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions.
Conservative Safety Critics for Exploration
Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning.
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn.
ViNG: Learning Open-World Navigation with Visual Goals
We propose a learning-based navigation system for reaching visually indicated goals and demonstrate this system on a real mobile robot platform.
Rapid Exploration for Open-World Navigation with Latent Goal Models
We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments.
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models
Humans have a remarkable ability to make decisions by accurately reasoning about future events, including the future behaviors and states of mind of other agents.
Intrinsic Control of Variational Beliefs in Dynamic Partially-Observed Visual Environments
We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.
Explore and Control with Adversarial Surprise
Unsupervised reinforcement learning (RL) studies how to leverage environment statistics to learn useful behaviors without the cost of reward engineering.
Information is Power: Intrinsic Control via Information Capture
We study this question in dynamic partially-observed environments, and argue that a compact and general learning objective is to minimize the entropy of the agent's state visitation estimated using a latent state-space model.
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting
We demonstrate the utility of our method in realistic scenarios using the CARLA driving simulator, where CARFF can be used to enable efficient trajectory and contingency planning in complex multi-agent autonomous driving scenarios involving visual occlusions.
