Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data

In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing

Non-absorbable iron chelators for the treatment of colorectal cancer

There is growing epidemiological and experimental evidence implicating excess luminal iron in the context of colorectal cancer. High levels of dietary iron is thought to aid carcinogenesis, due to the formation of reactive oxygen species from the redox cycling of iron, which can cause oxidative tissue damage and disrupt cellular signalling pathways. Hence, it is proposed that removal of this excess iron will suppress the development of this cancer. A clinically used iron chelator deferasirox and a modified version of this ligand, were attempted to be conjugated onto biopolymers chitosan and alginate. These non-absorbable polymers were hypothesised to be undigested in the gastrointestinal tract, thus specifically capable of targeting and removing excess iron from the colon. These chelator incorporated polymer materials, be they conjugated polymers or functional material blends, were subsequently shown to have improved iron binding properties compared to the parent polymers. Culturing RKO colorectal cancer cells with iron and alginate-ligand material did not significantly affect intracellular iron uptake, however culturing RKO cells with iron and chitosan-ligand material elicited a suppression in iron mediated ferritin expression and overall intracellular iron status. Based on these $in$ $vitro$ results, that the material obtained from reaction of chitosan with ligand elicits the desired inhibition of iron uptake, the chitosan-ligand material was administered to a mouse model of colorectal cancer. Apc Hom Pten Hom mice show reduced mitosis and increased apoptosis of intestinal crypt cells, demonstrating anti-neoplastic activity by iron chelation

Diffusion Augmented Agents: A Framework for Efficient Exploration and Transfer Learning

We introduce Diffusion Augmented Agents (DAAG), a novel framework that leverages large language models, vision language models, and diffusion models to improve sample efficiency and transfer learning in reinforcement learning for embodied agents. DAAG hindsight relabels the agent's past experience by using diffusion models to transform videos in a temporally and geometrically consistent way to align with target instructions with a technique we call Hindsight Experience Augmentation. A large language model orchestrates this autonomous process without requiring human supervision, making it well-suited for lifelong learning scenarios. The framework reduces the amount of reward-labeled data needed to 1) finetune a vision language model that acts as a reward detector, and 2) train RL agents on new tasks. We demonstrate the sample efficiency gains of DAAG in simulated robotics environments involving manipulation and navigation. Our results show that DAAG improves learning of reward detectors, transferring past experience, and acquiring new tasks - key abilities for developing efficient lifelong learning agents. Supplementary material and visualizations are available on our website https://sites.google.com/view/diffusion-augmented-agents/Comment: Published at 3rd Conference on Lifelong Learning Agents (CoLLAs), 202

The ethics of climate-induced community displacement and resettlement

Despite a burgeoning literature considering the ethical implications of climate change more broadly, and the ethics of migration, the nexus between the two issues has not been given enough attention by political theorists. The paper outlines the key nodes in a debate we think should take place, and which will be made even more urgent in the coming decades as climate change impacts on communities least resilient to it

Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data

In this work, we propose a robust network-in-the-loop control system for autonomous navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for autonomous navigation and landing

Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning

We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment. Specifically, we aim to improve the agent's ability to generalize to out-of-distribution goals. To achieve this, we propose to learn a dynamics model and check if it is equivariant with respect to a fixed type of transformation, namely translations in the state space. We then use an entropy regularizer to increase the equivariant set and augment the dataset with the resulting transformed samples. Finally, we learn a new policy offline based on the augmented dataset, with an off-the-shelf offline RL algorithm. Our experimental results demonstrate that our approach can greatly improve the test performance of the policy on the considered environments