PhD Thesis Proposal

Abstract: Human-centered computer vision technology relies heavily on large, diverse datasets, but collecting data from human subjects is time-consuming, labor-intensive, and raises privacy concerns. To address these challenges, researchers are increasingly using synthetic data to augment real-world datasets. This thesis explores efficient methods for generating and utilizing synthetic data to train human-based computer vision models. [...]

Abstract: Visual imitation learning has shown promising results in robotic manipulation in recent years. However, its generalization to unseen objects is often limited by the size and diversity of training data. Although more large-scale robotic datasets are available, they remain significantly smaller than image and text datasets. Additionally, scaling these datasets is time-consuming and labor-intensive, [...]

Abstract: The healthcare system prioritizes reactive care for acute illnesses, often overlooking the ongoing needs of individuals with chronic conditions that require long-term management and personalized care. Addressing this gap through technology can empower patients to better manage their conditions, enhancing independence and quality of life. Multimodal sensing, incorporating inertial, acoustic, and vision-based sensors, within [...]

Abstract: Navigation in unstructured environments is a capability critical to many robotics applications such as forestry, construction, disaster response and defense. In these domains, robots have the potential to eliminate much of the dull, dirty and/or dangerous work that is currently performed by humans. Unfortunately, these environments pose a unique set of challenges for navigation [...]

Abstract:  Soft robot hands offer compelling advantages for manipulation tasks, including inherent safety through material compliance, robust adaptation to uncertain object geometries, and the ability to conform to complex shapes passively. However, these same properties create significant challenges for conventional sensing and control approaches. This talk presents approaches to bridging advances in geometric learning and [...]

Abstract: As autonomous robots are increasingly expected to operate in dynamic, human-centered environments, it is crucial to develop robot policies that ensure safe and seamless interactions with humans, all while allowing robots to complete their intended tasks efficiently. To achieve this, robots must be capable of making informed decisions that account for human preferences, ensuring [...]

Abstract: Teams of multiple robots working together can achieve challenging tasks like warehouse automation, search and rescue, and cooperative construction. However, finding efficient collision-free motions for all agents is extremely challenging as the complexity of the multi-agent motion planning (MAMP) problem grows exponentially with the number of agents. Multi-Agent Path Finding (MAPF) is a subset [...]

Abstract: Building a generalist robot capable of performing diverse tasks in unstructured environments remains a longstanding challenge. A recent trend in robot learning aims to address this by scaling up demonstration datasets for imitation learning. However, most large-scale robotics datasets are collected in the real-world, often via manual teleoperation. This process is labor-intensive, slow, hardware-dependent, [...]