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Found 11 papers, 3 papers with code
Task-Driven Graph Attention for Hierarchical Relational Object Navigation
Embodied AI agents in large scenes often need to navigate to find objects.
Modeling Dynamic Environments with Scene Graph Memory
We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy.
Saved You A Click: Automatically Answering Clickbait Titles
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.
Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation
Doing this is challenging for two reasons: on the data side, current interfaces make collecting high-quality human demonstrations difficult, and on the learning side, policies trained on limited data can suffer from covariate shift when deployed.
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
We evaluate the new capabilities of iGibson 2. 0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI.
Semantic and Geometric Modeling with Neural Message Passing in 3D Scene Graphs for Hierarchical Mechanical Search
We propose to use a 3D scene graph representation to capture the hierarchical, semantic, and geometric aspects of this problem.
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter
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 object of interest and make it graspable.
AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers
The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency.
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
In this paper, we formalize Mechanical Search and study a version where distractor objects are heaped over the target object in a bin.
Robotics
Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision
We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering.
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins.
