PhD Speaking Qualifier

Abstract: Collision detection between objects is critical for simulation, control, and learning for robotic systems. However, existing collision detection routines are inherently non-differentiable, limiting their applications in gradient-based optimization tools. In this talk, I present DCOL: a fast and fully differentiable collision-detection framework that reasons about collisions between a set of composable and highly expressive [...]

Abstract: A standard critique of machine learning models (especially neural networks) is that they pick up on spurious correlations rather than causal relationships and are therefore brittle in the face of distribution shift. Solving this problem in full generality is impossible (i.e. there might be no good way to distinguish between the two). However, if [...]

Abstract: In constraint tasks, the current task state heavily limits what actions are available to an agent. Mechanical constraints exist in many common tasks such as construction, disassembly, and rearrangement and task space constraints exist in an even broader range of tasks. Deep reinforcement learning algorithms have typically struggled with constraint tasks for two main [...]

Abstract: Mobile manipulators in the home can provide increased autonomy to individuals with severe motor impairments, who often cannot complete activities of daily living (ADLs) without the help of a caregiver. Teleoperation of an assistive mobile manipulator could enable an individual with motor impairments to independently perform self-care and household tasks, yet limited motor function [...]

Abstract: In this work, we introduce KeyClass, a weakly-supervised text classification framework that learns from class-label descriptions only, without the need to use any human-labeled documents. It leverages the linguistic domain knowledge stored within pre-trained language models and data programming to automatically label documents. We demonstrate its efficacy and flexibility by comparing it to state-of-the-art [...]

Abstract: In this talk, I will focus on the problem of multi-object tracking in crowded scenes. Tracking within crowds is particularly challenging due to heavy occlusion and frequent crossover between tracking targets. The problem becomes more difficult when we only have noisy bounding boxes due to background and neighboring objects. Existing tracking methods try to [...]

Abstract: Retinal surgery procedures like epiretinal membrane peeling and retinal vein cannulation require surgeons to manipulate very delicate structures in the eye with little room for error. Many robotic surgery systems have been developed to help surgeons and enforce safeguards during these demanding procedures. One essential piece of information that is required to create and [...]

Abstract: Deep generative models make visual content creation more accessible to novice and professional users alike by automating the synthesis of diverse, realistic content based on a collected dataset. People often use generative models as data-driven sources, making it challenging to personalize a model easily. Currently, personalizing a model requires careful data curation, which is [...]

Abstract: Many of our interactions with the environment involve making and breaking contacts. However, it is not always obvious how one should reason about these intermittent contacts (sequence, timings, locations) in an online and adaptive way. This is particularly relevant in gait generation for legged locomotion control, where it is standard to simply predefine and [...]

Abstract: In this work we present a method for robust optimization for online incremental Simultaneous Localization and Mapping (SLAM). Due to the NP-Hardness of data association in the presence of perceptual aliasing, tractable (approximate) approaches to data association will produce erroneous measurements. We require SLAM back-ends that can converge to accurate solutions in the presence [...]