PhD Speaking Qualifier
Carnegie Mellon University
Data Collection for Screwdriving
Abstract: As the use of robotic manipulation in manufacturing continues to increase, the robustness requirements for fastening operations such as screwdriving increase as well. To investigate the reliability of screwdriving and the diverse failure categories that can arise, we collected a dataset of screwdriving operations and manually classified them into stages and result categories. I [...]
Carnegie Mellon University
Predictive Corrective Networks for Action Detection
Abstract: Although computer vision has seen significant advances in static image analysis, the relatively slow advances in video tasks such as action detection suggest we're struggling to build effective temporal models. In this talk, I will present a few main ideas that drive contemporary approaches, such as "two-stream networks" and "3D" convolutional networks. I'll also [...]
Characterization of Anchoring in Granular Soils
Abstract: I will present the results of tests conducted to characterize the pullout force of an anchor buried in cohesionless soils. Sensitivity analyses were conducted to understand how key measures of fin geometry affect an anchor's pullout force. To generalize the data collected, I propose a dimensionless model for predicting the performance of arbitrary fin [...]
Carnegie Mellon University
Liquid Metal-Microelectronics Integration for a Sensorized Soft Robot Skin
Abstract: Progress in the emerging field of soft robotics depends on the integration of sensors that are capable of sensing, power regulation, and signal processing. Commercially available microelectronics are well suited for these needs, as well as small enough to preserve the natural mechanics of a host system. Here, we present a method for integrating [...]
Model Predictive Path Following for Wheeled Mobile Robots
Abstract: The navigation success of a wheeled mobile robotic mission is directly correlated to the degree of accuracy to which the robot can follow a given path. This, in turn, is largely affected by two factors: a) the environment and b) the intrinsic properties of the robot – its design, actuation mechanism etc. In the [...]
Carnegie Mellon University
Generative Models of Orbital and In Situ Data for Autonomous Science
Abstract: The mapping and characterization of planetary bodies relies on the analysis of data collected by spacecraft and orbiters. For example, the instruments carried by the Mars Reconnaissance Orbiter have been crucial in the mapping of landforms, stratigraphy, minerals, and ice of Mars. These instruments provide extensive contextual information, but factors such as sparsity, resolution, [...]
Design with Interpretability in Mind: An Alternate Ethos for Data Science
Abstract: The fields of Machine Learning and Data Science generally follow the paradigm that “the ends justify the means”, where improving predictive power of an algorithm is considered of paramount value, even when implemented at the expense of model intelligibility. While accuracy is an important performance metric, interpretability should be a major consideration for many [...]
Carnegie Mellon University
Multi-Robot Routing and Scheduling with Spatio-Temporal And Ordering Constraints
Abstract We consider the problem of allocation and routing a fleet of robots to service a given set of locations while minimizing makespan. The service start times for the locations are subject to AND/OR type precedence constraints. Spatio-temporal constraints prohibit certain states from all feasible schedules where a state is defined as a tuple of [...]
Carnegie Mellon University
Robot Learning in Homes – Improving Generalization and Reducing Dataset Bias
Abstract: Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years. However, most existing policies are trained on large-scale datasets collected in curated lab settings. If we aim to deploy these models in unstructured visual environments like people’s homes, they will be unable to cope with the mismatch in data [...]
Carnegie Mellon University
Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning
Abstract: We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance [...]