108 citations · 210 across the 10 of their papers we have counts for
11 papers
Task-Driven Graph Attention for Hierarchical Relational Object Navigation
Michael Lingelbach, Chengshu Li, Minjune Hwang +6
Embodied AI agents in large scenes often need to navigate to find objects. In this work, we study a naturally emerging variant of the object navigation task, hierarchical relationa…
Modeling Dynamic Environments with Scene Graph Memory
Andrey Kurenkov, Michael Lingelbach, Tanmay Agarwal +7
Embodied AI agents that search for objects in large environments such as households often need to make efficient decisions by predicting object locations based on partial informati…
Saved You A Click: Automatically Answering Clickbait Titles
Oliver Johnson, Beicheng Lou, Janet Zhong +1
Often clickbait articles have a title that is phrased as a question or vague teaser that entices the user to click on the link and read the article to find the explanation. We deve…
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation
Josiah Wong, Albert Tung, Andrey Kurenkov +4
In mobile manipulation (MM), robots can both navigate within and interact with their environment and are thus able to complete many more tasks than robots only capable of navigatio…
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
Chengshu Li, Fei Xia, Roberto Martín-Martín +13
Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the…
Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search
Andrey Kurenkov, Roberto Martín-Martín, Jeff Ichnowski +2
Searching for objects in indoor organized environments such as homes or offices is part of our everyday activities. When looking for a target object, we jointly reason about the ro…
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter
Andrey Kurenkov, Joseph Taglic, Rohun Kulkarni +4
When searching for objects in cluttered environments, it is often necessary to perform complex interactions in order to move occluding objects out of the way and fully reveal the o…
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
Andrey Kurenkov, Ajay Mandlekar, Roberto Martin-Martin +2
The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial…
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna +6
When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects…
Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision
Kuan Fang, Yuke Zhu, Animesh Garg +4
Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and man…
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Andrey Kurenkov, Jingwei Ji, Animesh Garg +4
3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem thro…