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Here's the cleaned-up version: As micro-drones become increasingly deployed in indoor environments for applications ranging from warehouse inspection to emergency response, the challenge of precise automated landing emerges as a crucial barrier to their practical operation and ubiquitous adoption. Existing landing approaches often require complex hardware and substantial computation or perform unreliably indoors, making them impractical for palm-sized microdrones. We propose Moth, a low-cost infrared light-based solution that targets precise and efficient landing of low-resource microdrones. Moth consists of an infrared light source at the landing station along with an energy-efficient photodiode (PD) sensing platform attached to the bottom of the drone. At a cost under 83 USD, Moth achieves comparable performance to vision-based methods but at a fraction of the energy consumption and computation. Moth requires only three PDs without any complex pattern recognition models to land the drone accurately, under 10 cm of error, from up to 11.1 m away. 

Large Language Models (LLMs) have shown immense human-like capabilities for reasoning and generating digital content. However, their ability to freely sense, interact, and actuate the physical domain remains significantly limited due to three fundamental challenges: (1) physical environments require specialized sensors for different tasks, yet deploying dedicated sensors for each application is impractical; (2) events and objects of interest are often localized to small areas within large spaces, making them difficult to detect with static sensor networks; and (3) foundation models need flexible actuation capabilities to meaningfully interact with the physical world. To bridge this gap, we introduce EmbodiedFly, an embodied LLM agent combining a foundation model pipeline with a reconfigurable drone platform to observe, understand, and interact with the physical world. Our co-design approach features (1) a FM orchestration framework connecting multiple LLMs, VLMs, and an open-set object detection model; (2) a novel image segmentation technique that identifies task-relevant areas; and (3) a custom drone platform that autonomously reconfigures with appropriate sensors and actuators based on commands from the FM orchestration framework. Through real-world deployments, we demonstrate that EmbodiedFly completes diverse physical tasks with up to 85% higher success rates compared to traditional approaches leveraging static deployments. 

Foundation models (FM) have shown immense human-like capabilities for generating digital media. However, foundation models that can freely sense, interact, and actuate the physical domain is far from being realized. This is due to 1) requiring dense deployments of sensors to fully cover and analyze large spaces, while 2) events often being localized to small areas, making it difficult for FMs to pinpoint relevant areas of interest relevant to the current task. We propose FlexiFly, a platform that enables FMs to “zoom in” and analyze relevant areas with higher granularity to better understand the physical environment and carry out tasks. FlexiFly accomplishes by introducing 1) a novel image segmentation technique that aids in identifying relevant locations and 2) a modular and reconfigurable sensing and actuation drone platform that FMs can actuate to “zoom in” with relevant sensors and actuators. We demonstrate through real smart home deployments that FlexiFly enables FMs and LLMs to complete diverse tasks up to 85% more successfully. FlexiFly is critical step towards FMs and LLMs that can naturally interface with the physical world. 

We present the design and implementation of RECA, a novel human-centric recommender system for co-optimizing energy consumption, comfort and air quality in commercial buildings. Existing works generally optimize these objectives separately, or by only controlling energy consuming resources within the building without directly engaging occupants. We develop a deep reinforcement learning architecture based on multitask learning, demonstrate how it can be used to jointly learn energy savings, comfort and air quality improvements for different actions, and build a recommender system with humans-in-the-loop. Through real deployments in multiple commercial buildings, we found that RECA has the potential to further reduce energy consumption by up to in energy-focused optimization, improve all objectives by in joint optimization, and improve thermal comfort by up to in comfort and air quality focused optimization, over existing solutions. 

Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.