PhD Thesis Proposal
Sasanka Nagavalli: Behavior Composition in Human Interaction with Robotic Swarms
Sasanka Nagavalli Ph.D. Thesis Proposal Abstract: Robotic swarms are multi-robot systems whose global behavior emerges from local interactions between individual robots and spatially proximal neighboring robots. Each robot can be programmed with several local control laws that can be activated depending on an operator's choice of global swarm behavior (e.g. flocking, aggregation, formation control, area [...]
Dynamic 3D Reconstruction from the Crowd
Abstract: With the advent of affordable and high-quality smartphone cameras, any significant event, such as a wedding ceremony, a surprised birthday party, or a concert, can be easily captured from multiple of cameras. Automatically organizing such large scale visual data and creating a comprehensive 3D scene model for event browsing is an unsolved problem. State [...]
Measuring and Modeling Kinesic Signals in Social Communication
Abstract: Humans use subtle and elaborate body signals to convey their thoughts, emotions, and intentions. "Kinesics" is a term that refers to the study of such body movements used in social communication, including facial expressions and hand gestures. Understanding kinesic signals is fundamental to understanding human communication; it is among the key technical barriers to [...]
Probabilistic Approaches for Pose Estimation
Abstract: Pose estimation is central to several robotics applications such as registration, manipulation, SLAM, etc. In this thesis, we develop probabilistic approaches for fast and accurate pose estimation. A fundamental contribution of this thesis is formulating pose estimation in a parameter space in which the problem is truly linear and thus globally optimal solutions can [...]
Ada J. Zhang: Personalized Human Motion Classification
Abstract: Algorithms for human motion understanding have a wide variety of applications, including health monitoring, performance assessment, and user interfaces. However, differences between individual styles make it difficult to achieve robust performance, particularly for individuals who were not in the training population. We believe that adapting algorithms to individual behaviors is essential for effective human [...]
Carnegie Mellon University
Learning to learn from simulation: Using simulations to expedite learning on robots
Abstract: Robot controllers, including locomotion controllers, often consist of expert-designed heuristics. These heuristics can be hard to tune, particularly in higher dimensions. It is typical to use simulation to tune or learn these parameters and test on hardware. However, controllers learned in simulation often don't transfer to hardware due to model mismatch. This necessitates controller [...]
Carnegie Mellon University
Visual Learning without Exhaustive Supervision
Abstract Machine learning models have led to remarkable progress in visual recognition. A key driving factor for this progress is the abundance of labeled data. Over the years, researchers have spent a lot of effort curating visual data and carefully labeling it. However, moving forward, it seems impossible to annotate the vast amounts of visual [...]
Carnegie Mellon University
Learning with Clusters
Abstract As machine learning becomes more ubiquitous, clustering has evolved from primarily a data analysis tool into an integrated component of complex machine learning systems, including those involving dimensionality reduction, anomaly detection, network analysis, image segmentation and classifying groups of data. With this integration into multi-stage systems comes a need to better understand interactions between [...]
Carnegie Mellon University
Intra-Robot Replanning and Learning for Multi-Robot Teams in Complex Dynamic Domains
Abstract: In complex dynamic multi-robot domains, we have a set of individual robots that must coordinate together through a team planner that inevitably makes assumptions based on probabilities about the state of world and the actions of the individuals. Eventually, the individuals may encounter failures, because the team planner’s models of the states and actions [...]
Carnegie Mellon University
Automated Collaborations Among Neighborhood-based Search Heuristics
Abstract: For this thesis, we propose to study how to automatically combine multiple neighborhood-based heuristics. For most computationally challenging problems, there exists multiple heuristics, and it is generally the case that any such heuristic exploits only a limited number of aspects among all the possible problem characteristics that we can think of. As a result, [...]