Shubham Agrawal

I am a Senior Researcher at Samsung Research America in New York City, where I work on robotics, computer vision, and machine learning. I am fortunate to be working under supervision of Prof. Volkan Isler. Before joining Samsung, I received my CS masters from Columbia University with a focus on robotics and computer vision. During that time, I did several research projects under supervision of Prof. Shuran Song. I did my Bachelors in Computer Science from IIT Kanpur. In past, I also worked at Tesla Inc and Adobe Sytems as software development engineer.

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We present a method for scene reconstruction by structurally breaking the problem into two steps: rendering novel views via inpainting and 2D to 3D scene lifting. Specifically, we leverage the generalization capability of large visual language models (Dalle-2) to inpaint the missing areas of scene color images rendered from different views. Next, we lift these inpainted images to 3D by predicting normals of the inpainted image and solving for the missing depth values.

We present a novel method to provide geometric and semantic information of all objects in the scene as well as feasible grasps on those objects simultaneously. The main advantage of our method is its speed as it avoids sequential perception and grasp planning steps

We present a new approach to simultaneously reconstruct a mesh and a dense grasp quality map of an object from a depth image. At the core of our approach is a novel cameracentric object representation called the “object shell” which is composed of an observed “entry image” and a predicted “exit image”.

We create a manipulatable digital-twin of a real-world 6DoF-Kitting scene and propose a deep-learning based approach to infer precise 6DoF object poses inside kit from imprecise poses provided by user.

We learn a single grasping policy that generalizes to novel grippers.

Given an object to be grasped, we create a 3D generative model to generate 3D geometry of parallel jaw gripper fingers that optimizes various design objectives.