Search Results for author:
Found 14 papers, 7 papers with code
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e. g., a door might open inwards or outwards.
Object Memory Transformer for Object Goal Navigation
This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes.
CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces
Multi-agent path planning (MAPP) in continuous spaces is a challenging problem with significant practical importance.
OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching
Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering can be tedious.
Training Larger Networks for Deep Reinforcement Learning
Previous work has shown that this is mostly due to instability during training of deep RL agents when using larger networks.
Deep Reactive Planning in Dynamic Environments
The main novelty of the proposed approach is that it allows a robot to learn an end-to-end policy which can adapt to changes in the environment during execution.
Path Planning using Neural A* Search
We present Neural A*, a novel data-driven search method for path planning problems.
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering
The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study.
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
Specifically, we train a deep convolutional network that can predict collision-free paths based on a map of the environment-- this is then used by a reinforcement learning algorithm to learn to closely follow the path.
Salient object detection on hyperspectral images using features learned from unsupervised segmentation task
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes.
An Integration of Bottom-up and Top-Down Salient Cues on RGB-D Data: Saliency from Objectness vs. Non-Objectness
Bottom-up and top-down visual cues are two types of information that helps the visual saliency models.
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper. challenge.
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category.
Recognizing Activities of Daily Living with a Wrist-mounted Camera
We present a novel dataset and a novel algorithm for recognizing activities of daily living (ADL) from a first-person wearable camera.
