Student Talks
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, [...]
Plan to Learn: Active Robot Learning by Planning
Abstract: Robots need a diverse repertoire of capable motor skills to succeed in the open world. Such a skillset cannot be learned or designed purely on human initiative. In this thesis, we advocate for an active continual learning approach that enables robots to take charge of their own learning. The goal of an autonomously learning [...]
Continual Personalization of Human Actions with Prompt Tuning
Abstract: In interactive computing devices (VR/XR headsets), users interact with the virtual world using hand gestures and body actions. Typically, models deployed in such XR devices are static and limited to their default set of action classes. The goal of our research is to provide users and developers with the capability to personalize their experience by [...]
Policy Decomposition
Abstract: Optimal Control is a popular formulation for designing controllers for dynamic robotic systems. Under the formulation, the desired long-term behavior of the system is encoded via a cost function and the policy, i.e. a mapping from the state of the system to control commands, to achieve the desired behavior are obtained by solving an [...]
Analysis by Synthesis for Modern Computer Vision
Abstract: Image denoising, depth completion, scene flow, and dynamic 3D reconstruction are all examples of recovery problems: the estimation of multidimensional signals from corrupted or partial measurements. This thesis examines these problems from the classic analysis-by-synthesis perspective, where a signal model is used to propose hypotheses, which are then compared to observations. This paradigm has [...]
Reinforcement Learning with Spatial Reasoning for Dexterous Robotic Manipulation
Abstract: Robotic manipulation in unstructured environments requires adaptability and the ability to handle a wide variety of objects and tasks. This thesis presents novel approaches for learning robotic manipulation skills using reinforcement learning (RL) with spatially-grounded action spaces, addressing the challenges of high-dimensional, continuous action spaces and alleviating the need for extensive training data. Our [...]
Leveraging Vision, Force Sensing, and Language Feedback for Deformable Object Manipulation
Deformable object manipulation represents a significant challenge in robotics due to its complex dynamics, lack of low-dimensional state representations, and severe self-occlusions. This challenge is particularly critical in assistive tasks, where safe and effective manipulation of various deformable materials can significantly improve the quality of life for individuals with disabilities and address the growing needs [...]
CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data
Abstract: This research introduces CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits in mammalian brains, which are crucial for critical thinking and decision-making. Unlike traditional neural network models that generate an output for each input or after a fixed sequence of inputs, CBGT-Net learns to produce an output once sufficient evidence [...]
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 [...]
Enhancing Robot Perception and Interaction Through Structured Domain Knowledge
Abstract: Despite the advancements in deep learning driven by increased computational power and large datasets, significant challenges remain. These include difficulty in handling novel entities, limited mechanisms for human experts to update knowledge, and lack of interpretability, all of which are crucial for human-centric applications like assistive robotics. To address these issues, we propose leveraging [...]
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 [...]