activity
20232026
most citedDiff-DOPE: Differentiable Deep Object Pose Estimation

5 citations · 13 across the 15 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024

RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics

Chan Hee Song, Valts Blukis, Jonathan Tremblay +3

Spatial understanding is a crucial capability that enables robots to perceive their surroundings, reason about their environment, and interact with it meaningfully. In modern robot…

cs.RO2024

GRS: Generating Robotic Simulation Tasks from Real-World Images

Alex Zook, Fan-Yun Sun, Josef Spjut +3

We introduce GRS (Generating Robotic Simulation tasks), a system addressing real-to-sim for robotic simulations. GRS creates digital twin simulations from single RGB-D observations…

cs.RO2024★ 2 cited

RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics

Wentao Yuan, Jiafei Duan, Valts Blukis +5

From rearranging objects on a table to putting groceries into shelves, robots must plan precise action points to perform tasks accurately and reliably. In spite of the recent adopt…

cs.RO2024★ 1 cited

RVT-2: Learning Precise Manipulation from Few Demonstrations

Ankit Goyal, Valts Blukis, Jie Xu +3

In this work, we study how to build a robotic system that can solve multiple 3D manipulation tasks given language instructions. To be useful in industrial and household domains, su…

cs.RO2024

3D-MVP: 3D Multiview Pretraining for Robotic Manipulation

Shengyi Qian, Kaichun Mo, Valts Blukis +3

Recent works have shown that visual pretraining on egocentric datasets using masked autoencoders (MAE) can improve generalization for downstream robotics tasks. However, these appr…

cs.CV2024★ 2 cited

Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects

Yijia Weng, Bowen Wen, Jonathan Tremblay +4

We address the problem of building digital twins of unknown articulated objects from two RGBD scans of the object at different articulation states. We decompose the problem into tw…

cs.CV2024★ 1 cited

Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces

Mauro Comi, Alessio Tonioni, Max Yang +5

Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and p…