Carnegie Mellon University
MSR Thesis Talk: Avi Rudich
Title: Kinematic Analysis of 3D Printed Flexible Delta Robots Abstract: Flexible Delta robots show significant promise for use in a wide array of manipulation tasks. They are simple to design and manufacture, and they maintain a high level of repeatability and precision in open loop control. This thesis analyzes the kinematic properties of flexible [...]
Reconstructing common objects to interact with
Abstract: We humans are able to understand 3D shapes of common daily objects and interact with them from a wide range of categories. We understand cups are usually cylinder-like and we can easily predict the shape of one particular cup, both in isolation or even when it is held by a human. We aim to [...]
Activity Understanding of Scripted Performances
Abstract: The PSU Taichi for Smart Health project has been doing a deep-dive into vision-based analysis of 24-form Yang-style Taichi (TaijiQuan). A key property of Taichi, shared by martial arts katas and prearranged form exercises in other sports, is practice of a scripted routine to build both mental and physical competence. The scripted nature of routines [...]
Carnegie Mellon University
Dynamical Model Learning and Inversion for Aggressive Quadrotor Flight
Abstract: Quadrotor applications have seen a surge recently and many tasks require precise and accurate controls. Flying fast is critical in many applications and the limited onboard power source makes completing tasks quickly even more important. Staying on a desired course while traveling at high speeds and high accelerations is difficult due to complex and [...]
Carnegie Mellon University
Person Transfers Between Multiple Service Robots
Abstract: As more service robots are deployed in the world, human-robot interaction will not be limited to one-to-one interactions between users and robots. Instead, users will likely have to interact with multiple robots, simultaneously or sequentially, throughout their day to receive services and complete different tasks. In this thesis, I describe work in which my [...]
A causal framework to diagnose and fix issues with doors
Abstract: Many animals, such as ravens, (and a fortiori humans) exhibit a great deal of physical intelligence that allows them to solve complex multi-step physical puzzles. This ability indicates an understanding or a faculty to represent causality and mechanisms, understand when something goes wrong, and figure out how to deal with it. As a step [...]
Carnegie Mellon University
Understanding Unbalanced Datasets Through Simple Models and Dataset Exploration
Abstract: Computer vision models have proven to be tremendously capable of recognizing and detecting several classes and objects. They succeed in classes widely ranging in type and scale from humans to cans to pens. However, the best performing classes have abundant examples in large-scale datasets today. In unbalanced datasets, where some categories are seen in [...]
Domain adaptive object detection
Abstract: Recent advances in deep learning have led to the development of accurate and efficient models for object detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images. Domain adaptation tries to mitigate this degradation. In [...]
Carnegie Mellon University
Understanding, Exploiting and Improving Inter-view Relationships
Abstract: Multi-view machine learning has garnered substantial attention in various applications over recent years. Many such applications involve learning on data obtained from multiple heterogeneous sources of information, for example, in multi-sensor systems such as self-driving cars, or monitoring intensive care patient vital signs at their bed-side. Learning models for such applications can often benefit [...]
Model-Centric Verification of Artificial Intelligence
Abstract: This work shows how provable guarantees can be used to supplement probabilistic estimates in the context of Artificial Intelligence (AI) systems. Statistical techniques measure the expected performance of a model, but low error rates say nothing about the ways in which errors manifest. Formal verification of model adherence to design specifications can yield certificates [...]