User Tools
Site Tools
Timeline
Abstract
The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimization method that assists the engineers by proposing a morphing droneโs conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimization to a multi-body winged drone with trajectory optimization to solve the motion intelligence problem under diverse flight mission requirements, such as energy consumption and mission completion time. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and mission time, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.
Abstract:
This thesis introduces the concept of TEXterity (Tactile Extrinsic deXterity) to address challenges in robotic manipulation. Focusing on tactile sensing, TEXterity aims to enhance dexterity by enabling robots to perceive and act upon extrinsic contact between the grasped object and the environment. Identifying interpretability, observability, and uncertainty as key challenges in tactile sensing, this thesis sets out to answer four pivotal questions:
- Is tactile sensing actually useful?
- How can we exploit tactile sensing efficiently?
- How can we reason about extrinsic contact with tactile sensing?
- How can we achieve extrinsic dexterity with tactile sensing?
The conclusion summarizes the key findings, emphasizing the significance of tactile sensing and TEXterity in addressing challenges and advancing robotic manipulation. Strategies to tackle major challenges are outlined, focusing on interpretability, observability, and uncertainty. In essence, this thesis lays the groundwork for unlocking the potential of tactile sensing in robotic manipulation, offering insights, solutions, and avenues for future research to propel the field toward achieving TEXterity and further toward human-level dexterity.
Abstract
This book is a self-contained introduction to the design of modern (deep) neural networks. Because the term โneuralโ comes with a lot of historical baggage, I prefer the simpler term โdifferentiable modelsโ in the text. The focus of this 250-pages volume is on building efficient blocks for processing nD data, including convolutions, transformers, graph layers, and modern recurrent models (including linearized transformers and structured state-space models). Because the field is evolving quickly, I have tried to strike a good balance between theory and code, historical considerations and recent trends. I assume the reader has some exposure to machine learning and linear algebra, but I try to cover the preliminaries when necessary. The volume is a refined draft from a set of lecture notes for a course called Neural Networks for Data Science Applications that I teach in Sapienza. I do not cover many advanced topics (generative modeling, explainability, prompting, agents), which will be published over time in the companion website.
feed/start.txt ยท Last modified: 2025/04/24 20:30 by Horea Caramizaru