Seminar
Calendar of Events
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VASC Seminar
Jhony Kaesemodel Pontes
Argo AI
Point Cloud Registration with or without Learning
Abstract: I will be presenting two of our recent works on 3D point cloud registration: A scene flow method for non-rigid registration: I will discuss our current method to recover scene flow from point clouds. Scene flow is the three-dimensional (3D) motion field of a scene, and it provides information about the spatial arrangement […]
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RI Seminar
Cynthia Sung
Mechanical Engineering & Applied Mechanics, University of Pennsylvania
Dynamical Robots via Origami-Inspired Design
Abstract: Origami-inspired engineering produces structures with high strength-to-weight ratios and simultaneously lower manufacturing complexity. This reliable, customizable, cheap fabrication and component assembly technology is ideal for robotics applications in remote, rapid deployment scenarios that require platforms to be quickly produced, reconfigured, and deployed. Unfortunately, most examples of folded robots are appropriate only for small-scale, low-load […]
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VASC Seminar
Arsalan Mousavian
NVIDIA
Propelling Robot Manipulation of Unknown Objects using Learned Object Centric Models
Abstract: There is a growing interest in using data-driven methods to scale up manipulation capabilities of robots for handling a large variety of objects. Many of these methods are oblivious to the notion of objects and they learn monolithic policies from the whole scene in image space. As a result, they don’t generalize well to [...]
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VASC Seminar
Phillip Isola
EECS, MIT
When and Why Does Contrastive Learning Work?
Abstract: Contrastive learning organizes data by pulling together related items and pushing apart everything else. These methods have become very popular but it's still not entirely clear when and why they work. I will share two ideas from our recent work. First, I will argue that contrastive learning is really about learning to forget. Different [...]
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VASC Seminar
Ehsan Adeli
Stanford University
Anticipating the Future: forecasting the dynamics in multiple levels of abstraction
Abstract: A key navigational capability for autonomous agents is to predict the future locations, actions, and behaviors of other agents in the environment. This is particularly crucial for safety in the realm of autonomous vehicles and robots. However, many current approaches to navigation and control assume perfect perception and knowledge of the environment, even though [...]