PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

An Effective Learning Framework for Active Perception and a Case Study on Liquid Property Estimation

GHC 6115

Abstract:  Active perception refers to a perception process where robot actions are taken to improve perception. To do this, the robot needs an observation model that knows what it will observe based on the actions it takes. However, existing approaches struggle to learn a good observation model since it needs to account for all possible [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Vision-based Proprioceptive and Tactile Sensing for Soft Robots

Abstract: Soft robotic manipulators present many unique advantages in difficult manipulation tasks. The inherent compliance of soft robots' constituent deformable material makes them safe and reliable in delicate tasks such as harvesting fruit and assisting in household work. To address challenges in proprioceptive and tactile sensing for soft robots, we present a family of vision-based [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Robot Learning for Assistive Dressing

NSH 4305

Abstract: Robot-assisted dressing could benefit the lives of many people such as older adults and individuals with disabilities. In this talk, I will present two pieces of work that use robot learning for this assistive task. In the first half of the talk, I will present our work on developing a robot-assisted dressing system that [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Towards Robotic Tree Manipulation: Leveraging Graph Representations

GHC 4405

Abstract: There is growing interest in automating agricultural tasks that require intricate and precise interaction with specialty crops, such as trees and vines. However, developing robotic solutions for crop manipulation remains a difficult challenge due to complexities involved in modeling their deformable behavior. In this study, we present a framework for learning the deformation behavior [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Tracking Any”Thing” in Videos

NSH 3001

Abstract: Being able to track anything is one of the fundamental steps to parse and understand a video. In this talk, I will present two pieces of work that tackle this problem at different spatial granularities. In the first half of the talk, I will discuss tracking any video pixel or particle through time in [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Customizing Large-scale Text-to-Image Models

NSH 4305

Abstract: Advancements in large-scale generative models represent a watershed moment. These models can generate a wide variety of objects and scenes with different styles and compositions. However, these models are trained on a fixed snapshot of available data and often contain copyrighted or private images. This assumption makes them lacking in two aspects – (a) [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

How to Design Robotic Hands That Wield Tools

NSH 1305

Abstract: Tool manipulation is an essential human skill. It extends our manipulation capability beyond the capability of the biological hand, and is a defining feature of many important jobs centered on physical interaction with the real world. Yet, wielding a tool is drastically different from generally grasping an object. The prime examples are pens and [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Learning Local Heuristics in Heuristic Search

NSH 3305

Abstract: Motion planning is a fundamental problem in robotics; how can we move robots efficiently and safely? Motion planning can be solved using several paradigms with their own strengths and weaknesses. This talk dives into Heuristic Graph Search and its application to motion planning by converting it to a problem of finding a start-goal path [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Joint 2D and 3D Semi-Supervised Object Detection

NSH 4305

Abstract: While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion. Current methods develop independent pipelines for 2D and 3D semi-supervised learning despite the availability of paired image and point cloud frames. Observing that the distinct [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Towards Agile Robotics: Creating Push-Off Skills for Dynamic Interactions

GHC 8102

Abstract: Dynamic interactions play a fundamental role in human capabilities, enabling us to achieve a wide range of tasks such as moving heavy objects, manipulating our surroundings, and changing directions rapidly and safely. In contrast, most conventional robotic systems lack this level of agility and cannot perform dynamic interactions, limiting their potential in practical applications. [...]