KCIS Invited Talk by Dr Karthi Desingh (5 August 2022)

KCIS Invited Talk by Dr. Karthi Desingh
on 
Robust and Generalized Perception Towards Mainstreaming Domestic Robots

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   Dr. Karthik's 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 various tasks, a general-purpose robot should be capable of seamlessly perceiving and manipulating a wide variety of objects 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 his research, he developed 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 his research efforts in these two research thrusts. In the first part of the talk, he 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, he will describe the recent work - Spatial Object-centric Representation Network (SORNet) - for learning object-centric representation grounded for sequential manipulation tasks. He will also discuss the open research problems on both these thrusts towards realizing general-purpose domestic robots.

    

   Dr. Karthik Desingh will be joining the Department of Computer Science and Engineering at the University of Minnesota as an Assistant Professor in the Fall of 2022. Karthik recently completed his Postdoctoral position at the University of Washington (UW), where he worked 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. He is serving as an Associate Editor for IROS 2022.

    

   Venue: KRB Auditorium 

   Date and Time: 5 August, Friday, 4:00pm,

Page last updated on August, 2022