PhD Thesis Defense

Abstract: Image denoising, depth completion, scene flow, and dynamic 3D reconstruction are all examples of recovery problems: the estimation of multidimensional signals from corrupted or partial measurements. This thesis examines these problems from the classic analysis-by-synthesis perspective, where a signal model is used to propose hypotheses, which are then compared to observations. This paradigm has [...]

Abstract: Touch is an essential sensing modality for making autonomous robots more dexterous and allowing them to work collaboratively with humans. In particular, the advent of vision-based tactile sensors has resulted in efforts to design them for different robotic manipulation tasks. However, this design task remains a challenging problem. This is for two reasons: first, [...]

Abstract: Motion planning for dynamically complex robotic tasks requires explicit reasoning within constraints on velocity, acceleration, force/torque, and kinematics such as avoiding obstacles. To meet these constraints, planning algorithms must simultaneously make high-level discrete decisions and low-level continuous decisions. For example, pushing a heavy object involves making discrete decisions about contact locations and continuous decisions [...]

Abstract: Robots often need to communicate their navigation goals to assist observers in anticipating the robot's future actions. Enabling observers to infer where a robot is going from its movements is particularly important as robots begin to share workplaces, sidewalks, and social spaces with humans. We can use legible motion, or movements that use intentional [...]

Abstract:  Intelligent vehicles have been proposed as one path to increasing traffic safety and reducing on-road crashes. Driving “intelligence” today takes many forms, ranging from simple blind spot occupancy or forward collision warnings to distance-aware cruise and all the way to full driving autonomy in certain situations. Primarily, these methods are outward-facing and operate on [...]

Abstract:  This thesis aims to build computer systems to understand everyday hand-object interactions in the physical world – both perceiving ongoing interactions in 3D space and predicting possible interactions. This ability is crucial for applications such as virtual reality, robotic manipulations, and augmented reality. The problem is inherently ill-posed due to the challenges of one-to-many [...]

Abstract: The role of sensing is widely acknowledged for robots interacting with the physical environment. However, few contemporary sensors have gained widespread use among roboticists. This thesis proposes a framework for incorporating sensors into a robot learning paradigm, from development to deployment, through the lens of ReSkin -- a versatile and scalable magnetic tactile sensor. [...]

Abstract: Humans are remarkably adept at learning to perform tasks by imitating other people demonstrating these tasks. Key to this is our ability to reason abstractly about the high-level strategy of the task at hand (such as the recipe of cooking a dish) and the behaviours needed to solve this task (such as the behaviour [...]

Abstract: As collaborative robots are increasingly deployed in personal environments, such as the home, it is critical they take actions to complete tasks consistent with personal preferences. Determining personal preferences for completing household chores, however, is challenging. Many household chores, such as setting a table or loading a dishwasher, are sequential and open-vocabulary, creating a [...]

Abstract: Data-driven learning is a powerful paradigm for enabling robots to learn skills. Current prominent approaches involve collecting large datasets of robot behavior via teleoperation or simulation, to then train policies. For these policies to generalize to diverse tasks and scenes, there is a large burden placed on constructing a rich initial dataset, which is [...]