Colloquium Details
Integrative Learning, Modeling, Perception, and Planning Solutions for Modern Robotics
Speaker: Kostas Bekris, Rutgers University
Location: 60 Fifth Avenue 150
Date: October 30, 2024, 2 p.m.
Host: Lerrel Pinto
Synopsis:
Robots are increasingly deployed in the real-world but there are still critical gaps that limit their adaptability, robustness and safety. This talk will discuss how tighter integration of perception, learning and planning for vision-driven robot manipulation can address gaps that lie at the interface of these subdomains, which must be viewed holistically in robotics. We will demonstrate how progress in 3D robot perception, appropriate representations, tighter closed-loop operation and compliant end-effectors allow full-stack robot manipulation systems in applications, such as robot packing, assembly under tight tolerances and constrained placement. The talk will also focus on the Sim2Real gap in the context of novel, soft-rigid robotic platforms, such as tensegrity robots for uneven terrain locomotion. Modeling and controlling is complicated for such robots due to high-dim., complex dynamics and the sim2real gap. Reinforcement learning (RL) is promising but limited due to high data needs. Training RL in simulation is also blocked by the sim2real gap. We will present differentiable engines for tensegrity robots, which can be trained with few real trajectories, to provide accurate-enough models to define controllers transferable back to the real system. As learning plays an increasing role in robot control, it also becomes critical to identify the conditions under which learned controllers can successfully control robots to a desired state. We have been exploring topological tools for computing Region of Attraction (RoA) of black-box robot controllers. These tools achieve reduced data requirements relative to alternatives in approximating RoAs, and combined with machine learning methods, can be applied for RoA identification of high-dim. robotic systems.
Speaker Bio:
Kostas Bekris is a Professor of Computer Science at Rutgers University in New Jersey and an Amazon Scholar with Amazon Robotics. He is working in computational robotics, where his group is developing algorithms for robot planning, learning and perception especially in the context of robot manipulation, dealing with significant dynamics and for novel soft, adaptive mechanisms. Applications include logistics and manufacturing as well as field robotics. He has served as Program Chair for the Robotics: Science and Systems (RSS) and the Workshop on the Algorithmic Foundations of Robotics (WAFR) conferences. His research has been supported by NSF, DHS, DOD and NASA, including a NASA Early Career Faculty award.
Notes:
In-person attendance only available to those with active NYU ID cards.