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Found 20 papers, 6 papers with code
TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning
In our experiments, we use this interface to collect data and show that the resulting learned policies can successfully perform a variety of common household mobile manipulation tasks.
UltraGlove: Hand Pose Estimation with Mems-Ultrasonic Sensors
Hand tracking is an important aspect of human-computer interaction and has a wide range of applications in extended reality devices.
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.
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting
Once clutter is removed, we inpaint geometry and texture in the resulting holes by merging inpainted RGB-D images.
Differentiable Point-Based Radiance Fields for Efficient View Synthesis
We propose a differentiable rendering algorithm for efficient novel view synthesis.
Self-supervised Neural Articulated Shape and Appearance Models
In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis.
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.
Closed-Loop Control of Additive Manufacturing via Reinforcement Learning
We further show that in combination with reinforcement learning, our model can be used to discover control policies that outperform state-of-the-art controllers.
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
We compare our approach to direct optimization of the design using the learned surrogate, and to supervised learning of the synthesis problem.
Spatial Intention Maps for Multi-Agent Mobile Manipulation
The ability to communicate intention enables decentralized multi-agent robots to collaborate while performing physical tasks.
Learning to Infer Semantic Parameters for 3D Shape Editing
Many applications in 3D shape design and augmentation require the ability to make specific edits to an object's semantic parameters (e. g., the pose of a person's arm or the length of an airplane's wing) while preserving as much existing details as possible.
SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images
We propose an end-to-end deep neural network which is able to predict both reflectional and rotational symmetries of 3D objects present in the input RGB-D image.
Efficient Spatially Adaptive Convolution and Correlation
Fast methods for convolution and correlation underlie a variety of applications in computer vision and graphics, including efficient filtering, analysis, and simulation.
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.)
Accelerating Large-Kernel Convolution Using Summed-Area Tables
In this work, we explore using learnable box filters to allow for convolution with arbitrarily large kernel size, while keeping the number of parameters per filter constant.
Learning to Detect Features in Texture Images
Local feature detection is a fundamental task in computer vision, and hand-crafted feature detectors such as SIFT have shown success in applications including image-based localization and registration.
PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction
We introduce a novel RGB-D patch descriptor designed for detecting coplanar surfaces in SLAM reconstruction.
High-Precision Localization Using Ground Texture
We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside.
Depth From Shading, Defocus, and Correspondence Using Light-Field Angular Coherence
Using shading information is essential to improve the shape estimation.
Wide-Baseline Hair Capture Using Strand-Based Refinement
We propose a novel algorithm to reconstruct the 3D geometry of human hairs in wide-baseline setups using strand-based refinement.
