36 citations · 40 across the 15 of their papers we have counts for
15 papers
GuideNav: User-Informed Development of a Vision-Only Robotic Navigation Assistant For Blind Travelers
Hochul Hwang, Soowan Yang, Jahir Sadik Monon +4
While commendable progress has been made in user-centric research on mobile assistive systems for blind and low-vision (BLV) individuals, references that directly inform robot navi…
Agnostics: Learning to Code in Any Programming Language via Reinforcement with a Universal Learning Environment
Aleksander Boruch-Gruszecki, Yangtian Zi, Zixuan Wu +4
Large language models (LLMs) already excel at writing code in high-resource languages such as Python and JavaScript, yet stumble on low-resource languages that remain essential to…
Human-Centered Development of Guide Dog Robots: Quiet and Stable Locomotion Control
Shangqun Yu, Hochul Hwang, Trung M. Dang +4
A quadruped robot is a promising system that can offer assistance comparable to that of dog guides due to its similar form factor. However, various challenges remain in making thes…
ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments
Rohit Dwivedula, Sadanand Modak, Aditya Akella +3
The growing use of service robots in dynamic environments requires flexible management of on-board compute resources to optimize the performance of diverse tasks such as navigation…
ReasoningWeekly: A General Knowledge and Verbal Reasoning Challenge for Large Language Models
Zixuan Wu, Francesca Lucchetti, Aleksander Boruch-Gruszecki +5
Existing benchmarks for frontier models often test specialized, "PhD-level" knowledge that is difficult for non-experts to grasp. In contrast, we present a benchmark with 613 probl…
Creating and Repairing Robot Programs in Open-World Domains
Claire Schlesinger, Arjun Guha, Joydeep Biswas
Using Large Language Models (LLMs) to produce robot programs from natural language has allowed for robot systems that can complete a higher diversity of tasks. However, LLM-generat…
Joint Verification and Refinement of Language Models for Safety-Constrained Planning
Yunhao Yang, Neel P. Bhatt, William Ward +3
Large language models possess impressive capabilities in generating programs (e.g., Python) from natural language descriptions to execute robotic tasks. However, these generated pr…
Lessons Learned from Developing a Human-Centered Guide Dog Robot for Mobility Assistance
Hochul Hwang, Ken Suzuki, Nicholas A Giudice +3
While guide dogs offer essential mobility assistance, their high cost, limited availability, and care requirements make them inaccessible to most blind or low vision (BLV) individu…
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs
Zichao Hu, Junyi Jessy Li, Arjun Guha +1
Code LLMs have shown promising results with converting tasks in natural language to programs that can be executed by service robots. We are interested in finetuning small, speciali…
Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning
Arko Banerjee, Kia Rahmani, Joydeep Biswas +1
Among approaches for provably safe reinforcement learning, Model Predictive Shielding (MPS) has proven effective at complex tasks in continuous, high-dimensional state spaces, by l…
Vid2Real HRI: Align video-based HRI study designs with real-world settings
Elliott Hauser, Yao-Cheng Chan, Sadanand Modak +2
HRI research using autonomous robots in real-world settings can produce results with the highest ecological validity of any study modality, but many difficulties limit such studies…
SYNAPSE: SYmbolic Neural-Aided Preference Synthesis Engine
Sadanand Modak, Noah Patton, Isil Dillig +1
This paper addresses the problem of preference learning, which aims to align robot behaviors through learning user specific preferences (e.g. "good pull-over location") from visual…
Towards Robotic Companions: Understanding Handler-Guide Dog Interactions for Informed Guide Dog Robot Design
Hochul Hwang, Hee-Tae Jung, Nicholas A Giudice +3
Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation effi…
On a Foundation Model for Operating Systems
Divyanshu Saxena, Nihal Sharma, Donghyun Kim +15
This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that…
Deploying and Evaluating LLMs to Program Service Mobile Robots
Zichao Hu, Francesca Lucchetti, Claire Schlesinger +5
Recent advancements in large language models (LLMs) have spurred interest in using them for generating robot programs from natural language, with promising initial results. We inve…