10 citations · 21 across the 7 of their papers we have counts for
7 papers
Robot Planning and Situation Handling with Active Perception
Austine Oloo, Zainab Altaweel, Yohei Hayamizu +9
Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unforeseen situations frequently ar…
From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication
Yohei Hayamizu, David DeFazio, Hrudayangam Mehta +7
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 that helps vi…
LLM-GROP: Visually Grounded Robot Task and Motion Planning with Large Language Models
Xiaohan Zhang, Yan Ding, Yohei Hayamizu +6
Task planning and motion planning are two of the most important problems in robotics, where task planning methods help robots achieve high-level goals and motion planning methods m…
Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems
Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama +3
Rule representations significantly influence the search capabilities and decision boundaries within the search space of Learning Classifier Systems (LCSs), a family of rule-based m…
Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems
Hiroki Shiraishi, Yohei Hayamizu, Tomonori Hashiyama
This paper focuses on the impact of rule representation in Michigan-style Learning Fuzzy-Classifier Systems (LFCSs) on its classification performance. A well-representation of the…
DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning
Xiaohan Zhang, Zainab Altaweel, Yohei Hayamizu +6
Vision-language models (VLMs) have been applied to robot task planning problems, where the robot receives a task in natural language and generates plans based on visual inputs. Whi…
Guiding Robot Exploration in Reinforcement Learning via Automated Planning
Yohei Hayamizu, Saeid Amiri, Kishan Chandan +2
Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing…