PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Learning and Inference in Factor Graphs with Applications to Tactile Perception

Abstract: Factor graphs offer a flexible and powerful framework for solving large-scale, nonlinear inference problems as encountered in robot perception and control. Typically, these methods rely on handcrafted models that are efficient to optimize. However, robots often perceive the world through complex, high-dimensional sensor observations. For instance, consider a robot manipulating an object in hand [...]

VASC Seminar
Deqing Sun
Senior Research Scientist
Google

Learning Optical Flow: Model, Data, and Applications

Abstract: Optical flow provides important information about the dynamic world and is of fundamental importance to many tasks. In this talk, I will present my work on different aspects of learning optical flow. I will start with the background and talk about PWC-Net, a compact and effective model built using classical principles for optical flow. Next, [...]

Faculty Events
Assistant Professor
Robotics Institute,
Carnegie Mellon University

Hands-On Interactions

Newell-Simon Hall 3305

Abstract: Our sense of touch is present in almost all our interactions with the world, from providing us with the feedback necessary to perceive and manipulate objects without having to look at them, to allowing our limbs to move and walk without us having to think about how to take the next step. We use [...]

RI Seminar
Leila Bridgeman
Assistant Professor of Mechanical Engineering & Materials Science
Duke University

Distributed Dissipativity: Applying Foundational Stability Theory to Modern Networked Control

Abstract: Despite its diverse areas of application, the desire to optimize performance and guarantee acceptable behaviour in the face of inevitable uncertainty is pervasive throughout control theory. This creates a fundamental challenge since the necessity of robustly stable control schemes often favors conservative designs, while the desire to optimize performance typically demands the opposite. While [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Towards Complex Robot Motions with Reinforcement Learning

Abstract: Reinforcement learning has shown to be a powerful tool for decision-making problems. In this talk, we present the opportunities and challenges of enabling increasingly complex robot behavior with reinforcement learning. First, we present a system that combines reinforcement learning and extrinsic dexterity to solve a novel task of “occluded grasping”. To reach an occluded [...]

RI Seminar
Assistant Professor
Robotics Institute,
Carnegie Mellon University

Haptic Perspective-taking from Vision and Force

Abstract: Physically collaborative robots present an opportunity to positively impact society across many domains. However, robots currently lack the ability to infer how their actions physically affect people. This is especially true for robotic caregiving tasks that involve manipulating deformable cloth around the human body, such as dressing and bathing assistance. In this talk, I [...]

VASC Seminar
Chen Sun
Assistant Professor, Computer Science
Brown University

Do Vision-Language Pretrained Models Learn Spatiotemporal Primitive Concepts?

Abstract:  Vision-language models pretrained on web-scale data have revolutionized deep learning in the last few years. They have demonstrated strong transfer learning performance on a wide range of tasks, even under the "zero-shot" setup, where text "prompts" serve as a natural interface for humans to specify a task, as opposed to collecting labeled data. These models are [...]

Faculty Events

RI Council Meeting

Newell Simon Hall 4119

RI Council is a leadership group made up of the Director of RI, Academic Program Leads, Committee Chairs, and members at large as appointed by the Director. RI Council meets generally once a week to discuss department business.

RI Seminar
Jing Xiao
Professor and Department Head
Robotics Engineering Department, Worcester Polytechnic Institute (WPI)

Perception-Action Synergy in Uncertain Environments

Abstract: Many robotic applications require a robot to operate in an environment with unknowns or uncertainty, at least initially, before it gathers enough information about the environment. In such a case, a robot must rely on sensing and perception to feel its way around. Moreover, it has to couple sensing/perception and motion synergistically in real [...]

PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Driving Reconfigurable Unmanned Vehicle Design for Mobility Performance

Abstract: Unmanned ground vehicles are being deployed in increasingly diverse and complex environments. Advances in the field of robotics, including perception technology, computing power, and machine learning, have brought robots from the lab to the real world. Remote and autonomous vehicles are now used to explore volcanoes, caves, pipes, war zones, disaster sites, and even [...]