PhD Thesis Proposal

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 [...]

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 [...]

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 [...]

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 [...]

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. [...]

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 [...]

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. [...]

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 [...]

Abstract: The integration of soft and multifunctional materials in emerging technologies is becoming more widespread due to their ability to enhance or improve functionality in ways not possible using typical rigid alternatives. This trend is evident in various fields. For example, wearable technologies are increasingly designed using soft materials to improve modulus compatibility with biological [...]

Abstract: With modern robotic technologies, seafloor imageries have become more accessible to both researchers and the public. This thesis leverages deep learning and 3D vision techniques to deliver valuable information from seafloor image observations. Despite the widespread use of deep learning and 3D vision algorithms across various fields, underwater imaging presents unique challenges, such as [...]

Abstract: A long-standing problem in autonomous driving is grappling with the long-tail of rare scenarios for which little or no data is available. Although learning-based methods scale with data, it is unclear that simply ramping up data collection will eventually make this problem go away. Approaches which rely on simulation or world modeling offer some [...]

Abstract: As humans, we are constantly interacting with and observing a three-dimensional dynamic world; where objects around us change state as they move or are moved, and we, ourselves, move for navigation and exploration. Such an interaction between a dynamic environment and a dynamic ego-agent is complex to model as an ego-agent's perception of the [...]

Abstract: Since infancy, humans acquire motor skills, behavioral priors, and objectives by learning from their caregivers. Similarly, as we create humanoids in our own image, we aspire for them to learn from us and develop universal physical and cognitive capabilities that are comparable to, or even surpass, our own. In this thesis, we explore how [...]

Abstract: While Generative AI has shown breakthroughs in recent years in generating new digital contents such as images or 3D models from high-level goal inputs like text, Robotics technologies have not, instead focusing on low-level goal inputs. We propose Generative Robotics, as a new field of robotics which combines the high-level goal input abilities of [...]