Author name cluster

David DeFazio

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication

Yohei Hayamizu
David DeFazio
Hrudayangam Mehta
Zainab Altaweel
Jacqueline Choe
Chao Lin
Jake Juettner
Furui Xiao

Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot for assisting visually impaired people in obstacle avoidance and navigation. Enabling language capabilities on robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language to the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses large language models to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we performed a human study to evaluate different verbalization strategies, and a simulation study to evaluate the efficiency and accuracy in navigation tasks.

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ICAPS Conference 2024 Conference Paper

Learning Quadruped Locomotion Policies Using Logical Rules

David DeFazio
Yohei Hayamizu
Shiqi Zhang 0001

Quadruped animals are capable of exhibiting a diverse range of locomotion gaits. While progress has been made in demonstrating such gaits on robots, current methods rely on motion priors, dynamics models, or other forms of extensive manual efforts. People can use natural language to describe dance moves. Could one use a formal language to specify quadruped gaits? To this end, we aim to enable easy gait specification and efficient policy learning. Leveraging Reward Machines (RMs) for high-level gait specification over foot contacts, our approach is called RM-based Locomotion Learning (RMLL), and supports adjusting gait frequency at execution time. Gait specification is enabled through the use of a few logical rules per gait (e. g. , alternate between moving front feet and back feet) and does not require labor-intensive motion priors. Experimental results in simulation highlight the diversity of learned gaits (including two novel gaits), their energy consumption and stability across different terrains, and the superior sample-efficiency when compared to baselines. We also demonstrate these learned policies with a real quadruped robot. Video and supplementary materials: https: //sites. google. com/view/rm-locomotion-learning/home

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AAAI Conference 2019 Conference Paper

Deep Latent Generative Models for Energy Disaggregation

Gissella Bejarano
David DeFazio
Arti Ramesh

Thoroughly understanding how energy consumption is disaggregated into individual appliances can help reduce household expenses, integrate renewable sources of energy, and lead to efficient use of energy. In this work, we propose a deep latent generative model based on variational recurrent neural networks (VRNNs) for energy disaggregation. Our model jointly disaggregates the aggregated energy signal into individual appliance signals, achieving superior performance when compared to the state-of-the-art models for energy disaggregation, yielding a 29% and 41% performance improvement on two energy datasets, respectively, without explicitly encoding temporal/contextual information or heuristics. Our model also achieves better prediction performance on lowpower appliances, paving the way for a more nuanced disaggregation model. The structured output prediction in our model helps in accurately discerning which appliance(s) contribute to the aggregated power consumption, thus providing a more useful and meaningful disaggregation model.

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