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Found 14 papers, 4 papers with code
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.
Scene Graph Contrastive Learning for Embodied Navigation
Training effective embodied AI agents often involves expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.
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.
A General Purpose Supervisory Signal for Embodied Agents
Training effective embodied AI agents often involves manual reward engineering, expert imitation, specialized components such as maps, or leveraging additional sensors for depth and localization.
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.
ASC me to Do Anything: Multi-task Training for Embodied AI
We propose Atomic Skill Completion (ASC), an approach for multi-task training for Embodied AI, where a set of atomic skills shared across multiple tasks are composed together to perform the tasks.
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.
Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text
We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community.
AllenAct: A Framework for Embodied AI Research
The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities.
Bridging the Imitation Gap by Adaptive Insubordination
However, we show that when the teaching agent makes decisions with access to privileged information that is unavailable to the student, this information is marginalized during imitation learning, resulting in an "imitation gap" and, potentially, poor results.
Learning About Objects by Learning to Interact with Them
Much of the remarkable progress in computer vision has been focused around fully supervised learning mechanisms relying on highly curated datasets for a variety of tasks.
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems.
PSyCo: Manifold Span Reduction for Super Resolution
The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results.
Naive Bayes Super-Resolution Forest
This paper presents a fast, high-performance method for super resolution with external learning.
