PhD Thesis Proposal

Event Location: NSH 1305Abstract: Motion planning is a central problem in robotics and is crucial to finding paths to navigate and manipulate safely and efficiently. Ideally, we want planners which find paths quickly and of good quality. Additionally, planners should generate predictable motions, which are safer when operating in the presence of humans. While the [...]

Event Location: NSH 1305Abstract: Planning optimal paths for large numbers of robots is computationally expensive.  In our research, we developed a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed.  More specifically, subdimensional expansion initially creates a one-dimensional search [...]

Event Location: NSH 1305Abstract: Increasingly, real world problems require multiple predictions. For instance in advertisement placement on the web, a list of advertisements is placed on a page with the objective of maximizing click- through rate. Traditionally machine learning has focused on producing a single best prediction. More generally, in this thesis, we build an [...]

Event Location: GHC 8102Abstract: Fast and accurate human pose estimation enables a wide spectrum of applications from interactive control, markerless motion capture and gesture recognition to providing rich semantic information for higher level vision tasks such as scene understanding. The goal of this work is to develop an accurate and real-time human pose estimation system [...]

Event Location: NSH 3305Abstract: Robot-assisted surgery has been increasingly adopted in a wide variety of surgical applications because it offers fine manipulation with high precision and dexterity. Despite the commercial success of robotic platforms, practical use in microsurgery is still challenging due to a considerable level of accuracy required at sub-millimeter scales. Limited visualization and [...]

Event Location: GHC 8102Abstract: This proposal focuses on dynamic model based state estimation for hydraulic humanoid robots. The goal is to produce state estimates that are robust and achieve good performance when combined with the controller. Three issues are addressed in this proposal. 1. How to handle modelling error using state estimation? 2. How to [...]

Event Location: NSH 3305Abstract: Effective use of Machine Learning to support extracting maximal information from limited sensor data is one of the important research challenges in robotic sensing. This thesis develops techniques for detecting and characterizing patterns in noisy sensor data. Our Bayesian Aggregation (BA) algorithmic framework can leverage data fusion from multiple low Signal-To-Noise [...]

Event Location: GHC 4405Abstract: Given a single image of a scene, humans have few issues answering questions about the 3D structure like “is this facing upwards?” even though mathematically speaking this should be impossible. We have similarly have few issues accounting for this 3D structure in answering viewpoint independent questions like “is this the same [...]

Event Location: NSH 1507Abstract: We present work to transfer decentralized neuromuscular control strategies of human locomotion to powered segmented robotic legs. State-of-the-art robotic locomotion control approaches, like centralized planning and tracking in fully robotic systems and predefined motion pattern replay in prosthetic systems, do not enable the dynamism and reactiveness of able-bodied humans. Animals largely [...]