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Found 10 papers, 8 papers with code
TidyBot: Personalized Robot Assistance with Large Language Models
For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios.
Metric-Fair Classifier Derandomization
We study the problem of classifier derandomization in machine learning: given a stochastic binary classifier $f: X \to [0, 1]$, sample a deterministic classifier $\hat{f}: X \to \{0, 1\}$ that approximates the output of $f$ in aggregate over any data distribution.
Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower
We investigate pneumatic non-prehensile manipulation (i. e., blowing) as a means of efficiently moving scattered objects into a target receptacle.
Spatial Intention Maps for Multi-Agent Mobile Manipulation
The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks.
DV-ConvNet: Fully Convolutional Deep Learning on Point Clouds with Dynamic Voxelization and 3D Group Convolution
3D point cloud interpretation is a challenging task due to the randomness and sparsity of the component points.
Spatial Action Maps for Mobile Manipulation
Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e. g., step forward, turn left, turn right, etc.)
Real-Time Object Pose Estimation with Pose Interpreter Networks
In this work, we introduce pose interpreter networks for 6-DoF object pose estimation.
DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions.
Expert identification of visual primitives used by CNNs during mammogram classification
This work interprets the internal representations of deep neural networks trained for classification of diseased tissue in 2D mammograms.
SegICP: Integrated Deep Semantic Segmentation and Pose Estimation
Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios.
