PhD Thesis Proposal
Towards Influence-Aware Safe Human-Robot Interaction
Abstract: In recent years, we have seen through recommender systems on social media how influential (and potentially harmful) algorithms can be in our lives, sometimes creating polarization and conspiracies that lead to unsafe behavior. Now that robots are also growing more common in the real world, we must be very careful to ensure that they [...]
Design Principles for Robotics Systems that Support Human-Human Collaborative Learning
Abstract: Robots possess unique affordances granted by combining software and hardware. Most existing research focuses on the impact of these affordances on human-robot collaboration, but the theory of how robots can facilitate human-human collaboration is underdeveloped. Such theory would be beneficial in education. An educational device can afford collaboration in both assembly and use. This [...]
Robust Incremental Distributed Collaborative Simultaneous Localization and Mapping
Abstract: Multi-robot teams show exceptional promise across applications like Search-and-Rescue, disaster-response, agriculture, forestry, and scientific exploration due to their ability to go where humans cannot, parallelize activity, operate robustly to failures, and expand capabilities beyond that of an individual robot. Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for these multi-robot teams as [...]
A Unified Control Framework for Robust Aerial Manipulation
Abstract: Aerial robots are now widely employed in diverse applications, such as delivery, environmental monitoring, and especially aerial manipulation—the focus of this thesis. Aerial manipulation involves integrating robotic arms with drones to perform physical tasks remotely. This capability is particularly crucial for operations that are either too dangerous or inaccessible for humans, such as high-altitude [...]
Beyond Robot Safety: Adaptability and Interactivity
Abstract: The deployment of autonomous robots in various areas, including transportation and human-robot collaboration, requires strong safety measures for effective interaction with the physical world. Traditional safe control algorithms work well in controlled settings but struggle to adapt to more interactive and unpredictable real-world scenarios. This thesis emphasizes the need to explore beyond traditional robot [...]
Towards Pragmatic Time Series Intelligence
Abstract: The widespread adoption of time series machine learning (ML) models faces multiple challenges involving data, modeling and evaluation. Data. Modern ML models depend on copious amounts of cohesive and reliably annotated data for training and evaluation. However, labeled data is not always available and reliable, and can also be dispersed across different locations. We [...]
Improved Surface Estimation for use in Virtual Fixtures during Retinal Surgery
Abstract: Retinal surgery procedures require surgeons to manipulate very delicate tissues with little room for error. During epiretinal membrane surgery, to reduce chances of recurrence, surgeons may have to remove the 10 µm thick internal limiting membrane from the retinal surface. An experimental procedure to treat retinal vein occlusion is retinal vein cannulation. During this [...]
3D Perception In-The-Wild
Abstract: State estimation is a fundamental component of embodied perception. Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale data. Notably, although prior work has nearly solved 3D object detection for a few common classes (e.g., pedestrian and car), detecting many rare classes in-the-tail (e.g., debris and stroller) remains [...]
Differentiable Convex Modeling for Robotic Planning and Control
Abstract: Robotic simulation, planning, estimation, and control, have all been built on top of numerical optimization. In this same time, modern convex optimization has matured into a robust technology delivering globally optimal solutions in polynomial time. With advances in differentiable optimization and custom solvers capable of producing smooth derivatives, convex modeling has become fast, reliable, [...]
Information-Based Adaptive Allocation of Heterogeneous Multi-Agent Teams for Search and Coverage
Abstract: Information-based search and coverage are important in planetary exploration and disaster response applications. Efficient information acquisition can help with increasing geological understanding or situational awareness. Heterogeneous robots, each with different sensing and motion modalities, can be coordinated to optimize search and coverage in a target region. Information maps, which estimate the importance of visiting [...]
Dynamic Multi-Objective Trajectory Planning for Mobile Robots
Abstract: Robotic explorers play a crucial role in acquiring data from areas that are difficult or impossible for humans to reach. Whether for planetary exploration, search and rescue missions, agriculture, or other scientific exploration tasks, these robots can utilize pre-existing knowledge of the terrain to navigate effectively. In search- and coverage-oriented scenarios, robots must consider [...]
Motion planning for manipulation under pose uncertainty using contacts
Abstract: Numerous manipulation tasks, such as plug insertion and pipe assembly, demand an extremely high level of precision in pose estimation. Even minor errors, on the order of 2mm, can lead to task failure. While robots often rely on vision for object detection and localization, achieving consistent, high-precision localization using visual methods is not always [...]
Optimal Modular Robot Design for Mobile Manipulation in Agriculture
Abstract: Although agriculture is a highly mechanized industry, numerous sectors like horticulture and floriculture heavily depend on manual labor because they require safe handling of plants and produce that can only be left to humans. However, many research and commercial robots have succeeded in several challenging dexterous manipulation tasks like harvesting, pruning, and plant health [...]
Aligning Robot Task and Interaction Policies to Human Values
Abstract: The value alignment problem considers how robots can learn to behave in accordance with human values. Today, robot learning paradigms enable humans to provide data (e.g., preference labels or demonstrations), which the robot uses to update its behavior (e.g., reward model or policy) to be closer to the human’s values. However, the current paradigm [...]
Accelerating Robot Task Learning with Large Pretrained Models and Internet Data
Abstract: Large pre-trained models and internet data sources are key to general and efficient robot task learning. However, learning contact-rich behaviors, semantic task constraints, and robust task planning from internet data sources remains an open challenge. This proposal seeks to make progress towards a general robot task learning system leveraging pre-trained models and internet data. [...]
Unlocking Generalization for Robotics via Modularity and Scale
Abstract: How can we build generalist robot systems? Looking at fields such as vision and language, the common theme has been large scale end-to-end learning with massive, curated datasets. In robotics, on the other hand, scale alone may not be enough due to the significant multimodality of robotics tasks, lack of easily accessible data and [...]
Dynamic Route Guidance in Vehicle Networks by Simulating Future Traffic Patterns
Abstract: Roadway congestion leads to wasted time and money and environmental damage. One possible solution is adding more roadway capacity, but this can be impractical especially in urban environments and still may not make up for a poorly-calibrated traffic signal schedule. As such, it is becoming increasingly important to use existing road networks more efficiently. [...]
Multimodal Representations for Adaptable Robot Policies in Human-Inhabited Spaces
Abstract: Human beings sense and express themselves through multiple modalities. To capture multimodal ways of human communication, I want to build adaptable robot policies that infer task pragmatics from video and language prompts, reason about sounds and other sensors, take actions, and learn mannerisms of interacting with people and objects. Existing solutions for robot policies [...]