2024 Fall Robotics Colloquium: Manav Kulshrestha (Purdue University)

Title: Concept Learning for Interpretable and Efficient Robotic Agents
Speaker: Manav Kulshrestha (Ph.D. Candidate at Purdue University)
Date: November 8, 2024
Abstract: Real-world autonomous systems must often operate in dynamic and unstructured environments - conditions that closely reflect the complexity and unpredictability of the physical world. Unlike classical approaches employing hand-crafted models, deep learning has enabled robotic systems to relax their assumptions about the environment by allowing them to utilize large amounts of observed information to make adaptive decisions. However, the inherent black-box nature of deep learning methods poses significant challenges for interpretability - a critical concern, especially for systems that operate collaboratively with humans in safety-critical or time-sensitive domains. The ability for humans to comprehend the reasoning, underlying beliefs, and potential decision trajectories of their robotic counterparts becomes essential for establishing trust, ensuring effective human-robot interaction, and maintaining operational safety across diverse contexts.
Biography: Manav Kulshrestha is a Ph.D. Candidate at Purdue University, advised by Prof. Aniket Bera and affiliated with the Intelligent Design for Empathetic and Augmented Systems (IDEAS) Lab where he works on novel techniques for scene understanding and representation to build interpretable models for robotics. Before Purdue, he received dual degrees in Computer Science and Mathematics from the University of Massachusetts at Amherst.
This video is closed captioned.