Search Results for author:
Found 25 papers, 10 papers with code
SeGAN: Segmenting and Generating the Invisible
Objects often occlude each other in scenes; Inferring their appearance beyond their visible parts plays an important role in scene understanding, depth estimation, object interaction and manipulation.
AI2-THOR: An Interactive 3D Environment for Visual AI
We introduce The House Of inteRactions (THOR), a framework for visual AI research, available at http://ai2thor. allenai. org.
Who Let The Dogs Out? Modeling Dog Behavior From Visual Data
We introduce the task of directly modeling a visually intelligent agent.
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
In this paper we study the problem of learning to learn at both training and test time in the context of visual navigation.
Ranked #2 on
Visual Navigation
on AI2-THOR
Learning Generalizable Visual Representations via Interactive Gameplay
A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making, and socialization.
Watching the World Go By: Representation Learning from Unlabeled Videos
Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks.
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
Our quantitative and qualitative results show that (a) we can predict meaningful forces from videos whose effects lead to accurate imitation of the motions observed, (b) by jointly optimizing for contact point and force prediction, we can improve the performance on both tasks in comparison to independent training, and (c) we can learn a representation from this model that generalizes to novel objects using few shot examples.
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision.
Deconstructing the Structure of Sparse Neural Networks
Although sparse neural networks have been studied extensively, the focus has been primarily on accuracy.
Learning Flexible Visual Representations via Interactive Gameplay
A growing body of research suggests that embodied gameplay, prevalent not just in human cultures but across a variety of animal species including turtles and ravens, is critical in developing the neural flexibility for creative problem solving, decision making and socialization.
Learning Visual Representation from Human Interactions
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision.
Contrasting Contrastive Self-Supervised Representation Learning Pipelines
In the past few years, we have witnessed remarkable breakthroughs in self-supervised representation learning.
ManipulaTHOR: A Framework for Visual Object Manipulation
Object manipulation is an established research domain within the robotics community and poses several challenges including manipulator motion, grasping and long-horizon planning, particularly when dealing with oft-overlooked practical setups involving visually rich and complex scenes, manipulation using mobile agents (as opposed to tabletop manipulation), and generalization to unseen environments and objects.
Act the Part: Learning Interaction Strategies for Articulated Object Part Discovery
People often use physical intuition when manipulating articulated objects, irrespective of object semantics.
Towards Disturbance-Free Visual Mobile Manipulation
In this paper, we study the problem of training agents to complete the task of visual mobile manipulation in the ManipulaTHOR environment while avoiding unnecessary collision (disturbance) with objects.
Object Manipulation via Visual Target Localization
Object manipulation is a critical skill required for Embodied AI agents interacting with the world around them.
Continuous Scene Representations for Embodied AI
Our method captures feature relationships between objects, composes them into a graph structure on-the-fly, and situates an embodied agent within the representation.
ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Massive datasets and high-capacity models have driven many recent advancements in computer vision and natural language understanding.
Structure from Action: Learning Interactions for Articulated Object 3D Structure Discovery
We introduce Structure from Action (SfA), a framework to discover 3D part geometry and joint parameters of unseen articulated objects via a sequence of inferred interactions.
Retrospectives on the Embodied AI Workshop
We present a retrospective on the state of Embodied AI research.
Phone2Proc: Bringing Robust Robots Into Our Chaotic World
Training embodied agents in simulation has become mainstream for the embodied AI community.
Objaverse: A Universe of Annotated 3D Objects
Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI.
Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World
Reinforcement learning (RL) with dense rewards and imitation learning (IL) with human-generated trajectories are the most widely used approaches for training modern embodied agents.
Harmonic Mobile Manipulation
Recent advancements in robotics have enabled robots to navigate complex scenes or manipulate diverse objects independently.
Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences
Customizing robotic behaviors to be aligned with diverse human preferences is an underexplored challenge in the field of embodied AI.
