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Found 6 papers, 2 papers with code
NEWTON: Are Large Language Models Capable of Physical Reasoning?
To address this gap, we introduce NEWTON, a repository and benchmark for evaluating the physics reasoning skills of LLMs.
AR2-D2:Training a Robot Without a Robot
By contrast, we introduce AR2-D2: a system for collecting demonstrations which (1) does not require people with specialized training, (2) does not require any real robots during data collection, and therefore, (3) enables manipulation of diverse objects with a real robot.
MVTrans: Multi-View Perception of Transparent Objects
Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings.
CONetV2: Efficient Auto-Channel Size Optimization for CNNs
Neural Architecture Search (NAS) has been pivotal in finding optimal network configurations for Convolution Neural Networks (CNNs).
Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects
To address the shortcomings of existing transparent object data collection schemes in literature, we also propose an automated dataset creation workflow that consists of robot-controlled image collection and vision-based automatic annotation.
Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset
We use this to predict 3D models of vessels and their content from a single image.
Ranked #1 on
Single-View 3D Reconstruction
on TransProteus
