PhD Speaking Qualifier

Abstract We propose kernel density decision trees (KDDTs), a novel fuzzy decision tree (FDT) formalism based on kernel density estimation that improves the robustness of decision trees and ensembles and offers additional utility. FDTs mitigate the sensitivity of decision trees to uncertainty by representing uncertainty through fuzzy partitions. However, compared to conventional, crisp decision trees, [...]

Abstract: In this talk, I will describe a data-driven method for inferring camera poses given a sparse collection of images of an arbitrary object. This task is a core component of classic geometric pipelines such as structure-from-motion (SFM), and also serves as a vital pre-processing requirement for contemporary neural approaches (e.g. NeRF) to object reconstruction. [...]

Abstract: In this talk I'll describe how recent advances in neural rendering and novel view synthesis - namely NeRF - can be leveraged by robotic agents to improve performance in manipulation tasks. Specifically, I'll argue that NeRF can enable robotic policies to: (1) generalize to new viewpoints; (2) perceive specular and reflective surfaces in a [...]

Abstract: Achieving robust performance is crucial when applying deep reinforcement learning (RL) in safety critical systems. Some of the state of the art approaches try to address the problem with adversarial agents, but these agents often require expert supervision to fine tune and prevent the adversary from becoming too challenging to the trainee agent. While [...]

Abstract: This talk is the same one I will be presenting at the ACM CHI Conference on Human Factors in Computing Systems on May 2nd. Paper abstract: Today’s consumer virtual reality (VR) systems offer limited haptic feedback via vibration motors in handheld controllers. Rendering haptics to other parts of the body is an open challenge, [...]

Abstract: In this talk I will present our sampling-based approach to informative path planning that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a [...]

Abstract: Practical use of robotic manipulators made from soft materials requires generating and executing complex motions. We present the first approach for generating trajectories of a thermally-actuated soft robotic manipulator. Based on simplified approximations of the soft arm and its antagonistic shape-memory alloy actuator coils, we justify a dynamics model of a discretized rigid manipulator [...]

Abstract: Neural networks can represent and accurately reconstruct radiance fields for static 3D scenes (e.g., NeRF). Several works extend these to dynamic scenes captured with monocular video, with promising performance. However, the monocular setting is known to be an under-constrained problem, and so methods rely on data-driven priors for reconstructing dynamic content. We replace these [...]

Abstract: I will begin by describing our work on visual servoing a manipulator and localizing objects using a robot-mounted suite of vision and vision-based tactile sensors, our results, algorithms used, and lessons learned. We show that by collocating tactile, and global (e.g. an RGB(D) camera) sensors, our setup can perform better than using each type [...]

Abstract: Providing both the vertical take-off and landing capabilities and the ability to fly long distances to aircraft opens the door to a wide range of new real-world aircraft applications while improving many existing applications. Tiltrotor vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are a better choice than fixed-wing and multirotor aircraft for [...]

Abstract: Soft robot hands have shown promising results when it comes to dexterous grasping and manipulation. Compared to their rigid counterparts, soft hands can be manufactured for a fraction of the cost and offer robustness to uncertainty due to their inherent compliance. Unfortunately, the design and fabrication of soft robot hands is still a time-consuming [...]

Abstract: We present a thorough evaluation and analysis of state-of-the-art (SOTA) human trajectory forecasting methods with respect to metrics for safe and socially-aware prediction, e.g., collision rate, in addition to traditional displacement metrics, e.g., average displacement error. First, we introduce a system for trajectory classification which is used to evaluate the strengths and weaknesses of [...]

Abstract: Everyday human tasks such as picking up an object in one smooth motion, pushing a heavy door using the momentum of our bodies or pushing off a wall to quickly turn a corner involve complex dynamic interactions between the human and the environment, as well as switching dynamics when the robot makes and breaks [...]

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: Nonverbal communication in the field of education can allow teachers to emotionally support their students and improve educational experience and performance. Robot nonverbal movements have been shown to improve both subjective experiences and task performance, and this work investigates whether affective robot behavior can improve human learning. This is tested using an online sorting [...]

Abstract: In this talk, I focus on efficient online learning for solving real-world physics puzzles. I discuss challenges associated with learning in this domain and how those challenges inform certain design decisions. In particular, learning from scratch in the real world would be difficult. I present a practical mixture of experts framework for learning strategies [...]

Abstract: Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, we propose an end-to-end approach for detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Instead of predicting the current frame locations and [...]

Abstract: In theory, control barrier functions (CBFs) provide a convenient means to construct provably safe controllers. However, a typical problem is that the constructed controller will exceed input limits, and merely clipping the inputs will break all safety guarantees. To address this practical flaw, we consider synthesizing a CBF that will respect input limits. We [...]

Meeting ID: 940 0396 4889 Passcode: 906118 Abstract:  This research addresses the significant and unprecedented challenge of thermal regulation for lunar polar micro-rovers.  These are distinct from priors by way of very small size, mass, and power, but particularly for the extremes of ambient environment in which they must operate. On the lunar poles, rovers experience temperatures [...]

Abstract:  The ability to traverse complex environments and terrains is critical to autonomously driving off-road in a fast and safe manner. Challenges such as terrain navigation and vehicle rollover prevention become imperative due to the off-road vehicle configuration and the operating environment itself. This talk will introduce some of these challenges and the different tools [...]

Abstract: In this talk, I approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings. Meanwhile, there has been a lot of success in processing passive, unstructured human [...]

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 [...]