CS&E Colloquium: Robust and Generalized Perception Towards Mainstreaming Domestic Robots

The computer science colloquium takes place on Mondays and Fridays from 11:15 a.m. - 12:15 p.m.

This week's speaker, Karthik Desingh (University of Washington), will be giving a talk titled "Robust and Generalized Perception Towards Mainstreaming Domestic Robots".

Abstract

My long-term goal is to build general-purpose robots that can care for and assist the aging and disabled population by autonomously performing various real-world tasks. To robustly execute a variety of tasks, a general-purpose robot should be capable of seamlessly perceiving and manipulating a wide variety of objects present in our environment. To achieve a given task, a robot should continually perceive the state of its environment, reason with the task at hand, plan and execute appropriate actions. In this pipeline, perception is largely unsolved and one of the more challenging problems. Common indoor environments typically pose two main problems: 1) inherent occlusions leading to unreliable observations of objects, and 2) presence and involvement of a wide range of objects with varying physical and visual attributes (i.e., rigid, articulated, deformable, granular, transparent, etc.). Thus, we need algorithms that can accommodate perceptual uncertainty in the state estimation and generalize to a wide range of objects.

In my research, I develop 1) probabilistic inference methods to estimate the world-state with the notion of uncertainty and 2) data-driven methods to learn object representations that can generalize the state estimation to a wide range of objects. This talk will highlight some of my research efforts in these two research thrusts. In the first part of the talk, I will describe an efficient belief propagation algorithm - Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) - for estimating the state of articulated objects using a factored approach. In the second part of the talk, I will describe the most recent work - Spatial Object-centric Representation Network (SORNet) - for learning object-centric representation grounded for sequential manipulation tasks. I will also discuss the open research problems on both these thrusts towards realizing general-purpose domestic robots.

Biography

Karthik Desingh works as a Postdoctoral Scholar at the University of Washington (UW) with Professor Dieter Fox. Before joining UW, he received his Ph.D. in Computer Science and Engineering from the University of Michigan, working with Professor Chad Jenkins. During his Ph.D., he was closely associated with the Robotics Institute and Michigan AI. He earned his B.E. in Electronics and Communication Engineering at Osmania University, India, and M.S. in Computer Science at IIIT-Hyderabad and Brown University. He researches at the intersection of robotics, computer vision, and machine learning, primarily focusing on providing perceptual capabilities to robots using deep learning and probabilistic techniques to perform tasks in unstructured environments. His work has been recognized with the best workshop paper award at RSS 2019 and nominated as a finalist for the best systems paper award at CoRL 2021.