59 citations · 77 across the 9 of their papers we have counts for
9 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…
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…
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Yan Ding, Xiaohan Zhang, Saeid Amiri +5
Task planning systems have been developed to help robots use human knowledge (about actions) to complete long-horizon tasks. Most of them have been developed for "closed worlds" wh…
Grounding Classical Task Planners via Vision-Language Models
Xiaohan Zhang, Yan Ding, Saeid Amiri +4
Classical planning systems have shown great advances in utilizing rule-based human knowledge to compute accurate plans for service robots, but they face challenges due to the stron…
Robot Task Planning and Situation Handling in Open Worlds
Yan Ding, Xiaohan Zhang, Saeid Amiri +4
Automated task planning algorithms have been developed to help robots complete complex tasks that require multiple actions. Most of those algorithms have been developed for "closed…
Reasoning with Scene Graphs for Robot Planning under Partial Observability
Saeid Amiri, Kishan Chandan, Shiqi Zhang
Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many ob…
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…
Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog
Saeid Amiri, Sujay Bajracharya, Cihangir Goktolga +2
Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few robots are able to improve their language capabi…
Learning and Reasoning for Robot Sequential Decision Making under Uncertainty
Saeid Amiri, Mohammad Shokrolah Shirazi, Shiqi Zhang
Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a…