MSR Thesis Defense
Building Micron: The Next Handheld Manipulator for Microsurgery
Abstract: Robotic assistance is used today in a variety of surgeries as a means of precise, dexterous, and minimally-invasive manipulation. However, practical use in microsurgical environments such as vitreoretinal surgery remains a challenge for the most common mechanically-grounded robotic platforms. Microsurgery requires micron-level accuracy and the ability to manipulate with interaction forces in millinewtons. Vitreoretinal [...]
Towards Estimation, Modeling, and Control of Mixed Material Flows on Variable-Speed Conveyor Belt Systems with Applications in Recycling
Abstract: Whether it is in sorting defects from grain in an agricultural setting, ore from tailings in a mine, or letters in a postal system, the sorting of bulk material has long been a crucial aspect of human industry. Today, in the face of dwindling natural resource deposits and accelerating climate change, a particularly important [...]
Expressive Attentional Communication Learning using Graph Neural Networks
Abstract: Multi-agent reinforcement learning presents unique hurdles such as the non-stationary problem beyond single-agent reinforcement learning that makes learning effective decentralized cooperative policies using an agent's local state extremely challenging. Effective communication to share information and coordinate is vital for agents to work together and solve cooperative tasks, as the ubiquitous evidence of communication in [...]
Estimating Object Importance and Modeling Driver’s Situational Awareness for Intelligent Driving
Abstract: The ability to identify important objects in a complex and dynamic driving environment can help assistive driving systems alert drivers. These assistance systems also require a model of the drivers' situational awareness (SA) (what aspects of the scene they are already aware of) to avoid unnecessary alerts. This thesis builds towards such intelligent driving [...]
Learning for Perception and Strategy: Adaptive Omnidirectional Stereo Vision and Tactical Reinforcement Learning
Abstract: Multi-view stereo omnidirectional distance estimation usually needs to build a cost volume with many hypothetical distance candidates. The cost volume building process is often computationally heavy considering the limited resources a mobile robot has. We propose a new geometry-informed way of distance candidates selection method which enables the use of a very small number [...]
Online-Adaptive Self-Supervised Learning with Visual Foundation Models for Autonomous Off-Road Driving
Abstract: Autonomous robot navigation in off-road environments currently presents a number of challenges. The lack of structure makes it difficult to handcraft geometry-based heuristics that are robust to the diverse set of scenarios the robot might encounter. Many of the learned methods that work well in urban scenarios require massive amounts of hand-labeled data, but [...]
VoxDet: Voxel Learning for Novel Instance Detection
Abstract: Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel [...]
Voxel Learning for Novel Instance Detection
Abstract: Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel [...]
Efficient Quadruped Mobility: Harnessing a Generalist Policy for Streamlined Planning
Abstract: Navigating quadruped robots through complex, unstructured environments over long horizons remains a significant challenge in robotics. Traditional planning methods offer guarantees such as optimality and long-horizon reasoning, while learning-based methods, particularly those involving deep reinforcement learning (DRL), provide robustness and generalization. In this thesis, we present S3D-OWNS (Skilled 3D-Optimal Waypoint Navigation System), a novel [...]