Search Results for author:
Found 27 papers, 12 papers with code
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small neural nets instead of collections of images.
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Consequently, a policy that is optimal for one user might be sub-optimal for another.
Invariance Through Latent Alignment
A robot's deployment environment often involves perceptual changes that differ from what it has experienced during training.
Self-Supervised Camera Self-Calibration from Video
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.
Language Understanding for Field and Service Robots in a Priori Unknown Environments
This progress now creates an opportunity for robots to operate not only in isolation, but also with and alongside humans in our complex environments.
Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
While positive pairs can be generated reliably (e. g., as different views of the same image), it is difficult to accurately establish negative pairs, defined as samples from different images regardless of their semantic content or visual features.
Pow-Wow: A Dataset and Study on Collaborative Communication in Pommerman
In this work we perform a controlled study of human language use in a competitive team-based game, and search for useful lessons for structuring communication protocol between autonomous agents.
Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents
As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible.
Concurrent Training Improves the Performance of Behavioral Cloning from Observation
In contrast, learning from observation offers a way to utilize unlabeled demonstrations (e. g., video) to perform imitation learning.
Residual Policy Learning for Shared Autonomy
Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals.
Robotics
Loop Estimator for Discounted Values in Markov Reward Processes
At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process.
Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments
Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards
We propose a new complexity measure for Markov decision processes (MDPs), the maximum expected hitting cost (MEHC).
Multigrid Neural Memory
We introduce a novel approach to endowing neural networks with emergent, long-term, large-scale memory.
Inferring Compact Representations for Efficient Natural Language Understanding of Robot Instructions
The speed and accuracy with which robots are able to interpret natural language is fundamental to realizing effective human-robot interaction.
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment.
Satellite Image-based Localization via Learned Embeddings
We propose a vision-based method that localizes a ground vehicle using publicly available satellite imagery as the only prior knowledge of the environment.
Jointly Optimizing Placement and Inference for Beacon-based Localization
The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements.
Coherent Dialogue with Attention-based Language Models
We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism.
Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation
We first decide which information to share with the user according to their preferences, using a policy trained from human demonstrations via inverse reinforcement learning.
Learning Articulated Motion Models from Visual and Lingual Signals
In this paper, we present a multimodal learning framework that incorporates both visual and lingual information to estimate the structure and parameters that define kinematic models of articulated objects.
Accurate Vision-based Vehicle Localization using Satellite Imagery
We propose a method for accurately localizing ground vehicles with the aid of satellite imagery.
On the Performance of Hierarchical Distributed Correspondence Graphs for Efficient Symbol Grounding of Robot Instructions
Natural language interfaces are powerful tools that enables humans and robots to convey information without the need for extensive training or complex graphical interfaces.
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i. e., the joint task of content selection and surface realization.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents.
Learning Articulated Motions From Visual Demonstration
This paper describes a method by which a robot can acquire an object model by capturing depth imagery of the object as a human moves it through its range of motion.
