PhD Thesis Defense
Seungmoon Song: The Development, Evaluation and Applications of a Neuromechanical Control Model of Human Locomotion
Seungmoon Song Ph.D. Thesis Defense Abstract: The neural control of human locomotion is not fully understood. As current experimental techniques provide only partial and indirect access to the neural control network, our understanding remains fragmentary with large gaps between detectable neural circuits and measurable behavioral data. Neuromechanical simulation studies can help bridging these gaps. By [...]
Juan Pablo Mendoza: Regions of Inaccurate Modeling for Robot Anomaly Detection and Model Correction
Juan Pablo Mendoza Ph.D. Thesis Defense Abstract: To make intelligent decisions, robots often use models of the stochastic effects of their actions on the world. Unfortunately, in complex environments, it is often infeasible to create models that are accurate in every plausible situation, which can lead to suboptimal performance. This thesis enables robots to reason [...]
Reasoning About Spatial Patterns of Human Behavior During Group Conversations with Robots
Abstract: The goal of this dissertation is to develop computational models for robots to detect and sustain the spatial patterns of behavior that naturally emerge during free-standing group conversations with people. These capabilities have often been overlooked by the Human-Robot Interaction (HRI) community, but they are essential for robots to appropriately interact with and around [...]
Acting under Uncertainty for Information Gathering and Shared Autonomy
Abstract: Acting under uncertainty is a fundamental challenge for any decision maker in the real world. As uncertainty is often the culprit of failure, many prior works attempt to reduce the problem to one with a known state. However, this fails to account for a key property of acting under uncertainty: we can often gain [...]
Situational Awareness and Mixed Initiative Markup for Human-Robot Team Plans
Abstract: As robots become more reliable and user interfaces (UI) become more powerful, human-robot teams are being applied to more real world problems. Human-robot teams offer redundancy and heterogeneous capabilities desirable in scientific investigation, surveillance, disaster response, and search and rescue operations. Large teams are overwhelming for a human operator, so systems employ high level [...]
Discovering and Leveraging Visual Structure for Large-scale Recognition
Abstract: Our visual world is extraordinarily varied and complex, but despite its richness, the space of visual data may not be that astronomically large. We live in a well-structured, predictable world, where cars almost always drive on roads, sky is always above the ground, and so on. As humans, the ability to learn this structure [...]
Deliberative Perception
Abstract: A recurrent and elementary robot perception task is to identify and localize objects of interest in the physical world. In many real-world situations such as in automated warehouses and assembly lines, this task entails localizing specific object instances with known 3D models. Most modern-day methods for the 3D multi-object localization task employ scene-to-model feature [...]
Carnegie Mellon University
Compact Generative Models of Point Cloud Data for 3D Perception
Abstract: One of the most fundamental tasks for any robotics application is the ability to adequately assimilate and respond to incoming sensor data. In the case of 3D range sensing, modern-day sensors generate massive quantities of point cloud data that strain available computational resources. Dealing with large quantities of unevenly sampled 3D point data is [...]
Carnegie Mellon University
Mathematical Models of Adaptation in Human-Robot Collaboration
Abstract: While much work in human-robot interaction has focused on leader- follower teamwork models, the recent advancement of robotic systems that have access to vast amounts of information suggests the need for robots that take into account the quality of the human decision making and actively guide people towards better ways of doing their task. [...]
Carnegie Mellon University
Training Strategies for Time Series: Learning for Prediction, Filtering, and Reinforcement Learning
Abstract: Data driven approaches to modeling time-series are important in a variety of applications from market prediction in economics to the simulation of robotic systems. However, traditional supervised machine learning techniques designed for i.i.d. data often perform poorly on these sequential problems. This thesis proposes that time series and sequential prediction, whether for forecasting, filtering, [...]