6 citations · 10 across the 7 of their papers we have counts for
9 papers
Latent RL Priors for Data-Efficient Dexterous Manipulation
Yixiao Wang, Peiyi Chen, Mustafa Mukadam +3
Learning dexterous manipulation from limited visual demonstrations is challenging because policies must acquire both perceptual grounding and precise control, while reinforcement l…
Understanding the Impact of Geometric Foundation Models on Vision-Language-Action Models
Yurou Yang, Muyuan Lin, Roberto Martin-Martin +4
Recent work explores new opportunities at the intersection of vision-language-action models (VLAs) and geometric foundation models (GFMs) for 3D reconstruction, such as VGGT. While…
Explicit Memory through Online 3D Gaussian Splatting Improves Class-Agnostic Video Segmentation
Anthony Opipari, Aravindhan K Krishnan, Shreekant Gayaka +4
Remembering where object segments were predicted in the past is useful for improving the accuracy and consistency of class-agnostic video segmentation algorithms. Existing video se…
UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References
Ming-Feng Li, Xin Yang, Fu-En Wang +5
6D object pose estimation has shown strong generalizability to novel objects. However, existing methods often require either a complete, well-reconstructed 3D model or numerous ref…
Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors
Hritam Basak, Hadi Tabatabaee, Shreekant Gayaka +6
3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild. Accurately reconstruct…
Configurable Embodied Data Generation for Class-Agnostic RGB-D Video Segmentation
Anthony Opipari, Aravindhan K Krishnan, Shreekant Gayaka +4
This paper presents a method for generating large-scale datasets to improve class-agnostic video segmentation across robots with different form factors. Specifically, we consider t…
SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments
Evin Pınar Örnek, Aravindhan K Krishnan, Shreekant Gayaka +4
Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it di…
A Theoretical Analysis of Deep Neural Networks for Texture Classification
Saikat Basu, Manohar Karki, Robert DiBiano +4
We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative represe…
Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets
Saikat Basu, Manohar Karki, Sangram Ganguly +3
Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, col…