Student Talks
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, [...]
Carnegie Mellon University
Computational Design Tools for Accessible Robotics
Abstract: A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones are today. Such pervasive integration of robots would greatly benefit from faster design and manufacturing of robots that cater to individual needs. However, robots of today often take years to be created [...]
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 [...]
Carnegie Mellon University
Soft-Matter Robotic Materials
Abstract: Soft machines and electronics are key components for emerging applications in wearable biomonitoring, human-machine interaction, and soft robotics. In contrast to conventional machines and electronics, soft-matter technologies provide a method for replicating these traditionally rigid devices using intrinsically soft materials that exhibit properties similar to soft biological tissue. This provides a path forward for [...]
Carnegie Mellon University
Exploiting Redundancy for Learning Visual Representations
Abstract: Our visual world is highly structured and the visual data is highly redundant. In recent years, the computer vision field has been transformed by the success of Convolutional Neural Networks (ConvNets). However, the structure and redundancy in visual data has not been well explored in deep learning. The benefits of exploring data redundancy are [...]
Carnegie Mellon University
Adaptive Motion Planning
Abstract: Mobile robots are increasingly being deployed in the real world in response to a heightened demand for applications such as transportation, delivery and inspection. The motion planning systems for these robots are expected to have consistent performance across the wide range of scenarios that they encounter. While state-of-the-art planners, with provable worst-case guarantees, can [...]
Carnegie Mellon University
Kernel and Moment based Prediction and Planning: Applications to Robotics and Natural Language Processing
Abstract This thesis focuses on moment and kernel-based methods for applications in Robotics and Natural Language Processing. Kernel and moment-based learning leverage information about correlated data that allow the design of compact representations and efficient learning algorithms. We explore kernel algorithms for planning by leveraging inherently continuous properties of reproducing kernel Hilbert spaces. We introduce [...]
Carnegie Mellon University
Towards Generalization and Efficiency of Reinforcement Learning
Abstract In classic supervised machine learning, a learning agent behaves as a passive observer: it receives examples from some external environment which it has no control over and then makes predictions. The predictions the agent made will not affect any future examples it will see (i.e., examples are identically and independently sampled from some unknown [...]
Carnegie Mellon University
Harnessing Task Mechanics for Robotic Pushing and Grasping
Abstract: A high-fidelity and tractable mechanics model of physical interaction is essential for autonomous robotic manipulation in complex and uncertain environments. This thesis studies several aspects of harnessing task mechanics for robotic pushing and grasping operations: mechanics model learning, pose and model uncertainty reduction, and planning and control synthesis in the minimal coordinate space. We [...]