b'Shiqi Zhang'

research

∙ 09/08/2023

Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control

Seeing-eye robots are very useful tools for guiding visually impaired pe...

0 David DeFazio, et al. ∙

research

∙ 07/21/2023

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning

In existing task and motion planning (TAMP) research, it is a common ass...

0 Xiaohan Zhang, et al. ∙

research

∙ 05/27/2023

Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds

Task planning systems have been developed to help robots use human knowl...

0 Yan Ding, et al. ∙

research

∙ 05/25/2023

Efficient Approximation Algorithms for Spanning Centrality

Given a graph 𝒢, the spanning centrality (SC) of an edge e measures the ...

0 Shiqi Zhang, et al. ∙

research

∙ 05/08/2023

ARDIE: AR, Dialogue, and Eye Gaze Policies for Human-Robot Collaboration

Human-robot collaboration (HRC) has become increasingly relevant in indu...

0 Chelsea Zou, et al. ∙

research

∙ 04/22/2023

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Large language models (LLMs) have demonstrated remarkable zero-shot gene...

0 Bo Liu, et al. ∙

research

∙ 04/17/2023

Grounding Classical Task Planners via Vision-Language Models

Classical planning systems have shown great advances in utilizing rule-b...

0 Xiaohan Zhang, et al. ∙

research

∙ 03/10/2023

Task and Motion Planning with Large Language Models for Object Rearrangement

Multi-object rearrangement is a crucial skill for service robots, and co...

0 Yan Ding, et al. ∙

research

∙ 11/13/2022

Learning Visualization Policies of Augmented Reality for Human-Robot Collaboration

In human-robot collaboration domains, augmented reality (AR) technologie...

0 Kishan Chandan, et al. ∙

research

∙ 10/05/2022

GLAD: Grounded Layered Autonomous Driving for Complex Service Tasks

Given the current point-to-point navigation capabilities of autonomous v...

0 Yan Ding, et al. ∙

research

∙ 10/04/2022

Robot Task Planning and Situation Handling in Open Worlds

Automated task planning algorithms have been developed to help robots co...

0 Yan Ding, et al. ∙

research

∙ 05/03/2022

Residual Graph Convolutional Recurrent Networks For Multi-step Traffic Flow Forecasting

Traffic flow forecasting is essential for traffic planning, control and ...

0 Wei Zhao, et al. ∙

research

∙ 04/11/2022

RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification

Multi-fidelity modelling arises in many situations in computational scie...

0 Jiahao Zhang, et al. ∙

research

∙ 04/07/2022

MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochasti

A new data-driven method for operator learning of stochastic differentia...

0 Jiahao Zhang, et al. ∙

research

∙ 04/06/2022

PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations

In this work, a Gaussian process regression(GPR) model incorporated with...

0 Jiahao Zhang, et al. ∙

research

∙ 03/21/2022

STCGAT: Spatial-temporal causal networks for complex urban road traffic flow prediction

Traffic forecasting is an essential component of intelligent transportat...

0 Wei Zhao, et al. ∙

research

∙ 02/22/2022

Visually Grounded Task and Motion Planning for Mobile Manipulation

Task and motion planning (TAMP) algorithms aim to help robots achieve ta...

0 Xiaohan Zhang, et al. ∙

research

∙ 02/21/2022

Reasoning with Scene Graphs for Robot Planning under Partial Observability

Robot planning in partially observable domains is difficult, because a r...

0 Saeid Amiri, et al. ∙

research

∙ 02/15/2022

DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories

This paper proposes a new data-driven method for the reliable prediction...

0 Christian Moya, et al. ∙

research

∙ 02/14/2022

Learning to Ground Objects for Robot Task and Motion Planning

Task and motion planning (TAMP) algorithms have been developed to help r...

0 Yan Ding, et al. ∙

research

∙ 09/21/2021

Learning to Guide Human Attention on Mobile Telepresence Robots with 360 degree Vision

Mobile telepresence robots (MTRs) allow people to navigate and interact ...

0 Kishan Chandan, et al. ∙

research

∙ 07/27/2021

Task and Situation Structures for Service Agent Planning

Everyday tasks are characterized by their varieties and variations, and ...

0 Hao Yang, et al. ∙

research

∙ 07/23/2021

Learning Quadruped Locomotion Policies with Reward Machines

Legged robots have been shown to be effective in navigating unstructured...

0 David DeFazio, et al. ∙

research

∙ 06/06/2021

Planning Multimodal Exploratory Actions for Online Robot Attribute Learning

Robots frequently need to perceive object attributes, such as "red," "he...

0 Xiaohan Zhang, et al. ∙

research

∙ 02/07/2021

Effective and Scalable Clustering on Massive Attributed Graphs

Given a graph G where each node is associated with a set of attributes, ...

0 Renchi Yang, et al. ∙

research

∙ 08/19/2020

A Survey of Knowledge-based Sequential Decision Making under Uncertainty

Reasoning with declarative knowledge (RDK) and sequential decision-makin...

0 Shiqi Zhang, et al. ∙

research

∙ 05/20/2020

Learning and Reasoning for Robot Dialog and Navigation Tasks

Reinforcement learning and probabilistic reasoning algorithms aim at lea...

7 Keting Lu, et al. ∙

research

∙ 05/07/2020

Adaptive Dialog Policy Learning with Hindsight and User Modeling

Reinforcement learning methods have been used to compute dialog policies...

0 Yan Cao, et al. ∙

research

∙ 04/23/2020

Guided Dyna-Q for Mobile Robot Exploration and Navigation

Model-based reinforcement learning (RL) enables an agent to learn world ...

0 Yohei Hayamizu, et al. ∙

research

∙ 04/22/2020

AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning

Deep reinforcement learning (RL) algorithms frequently require prohibiti...

0 Keting Lu, et al. ∙

research

∙ 04/18/2020

iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots

Robot sequential decision-making in the real world is a challenge becaus...

8 Shiqi Zhang, et al. ∙

research

∙ 03/08/2020

Task-Motion Planning for Safe and Efficient Urban Driving

Autonomous vehicles need to plan at the task level to compute a sequence...

0 Yan Ding, et al. ∙

research

∙ 01/18/2020

RCELF: A Residual-based Approach for Influence Maximization Problem

Influence Maximization Problem (IMP) is selecting a seed set of nodes in...

0 Xinxun Zeng, et al. ∙

research

∙ 01/18/2020

RCELF: A Residual-based Approach for InfluenceMaximization Problem

Influence Maximization Problem (IMP) is selecting a seed set of nodes in...

0 Xinxun Zeng, et al. ∙

research

∙ 09/24/2019

Negotiation-based Human-Robot Collaboration via Augmented Reality

Effective human-robot collaboration (HRC) requires extensive communicati...

0 Kishan Chandan, et al. ∙

research

∙ 07/31/2019

Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language

To be responsive to dynamically changing real-world environments, an int...

0 Yi Wang, et al. ∙

research

∙ 07/08/2019

Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog

Some robots can interact with humans using natural language, and identif...

0 Saeid Amiri, et al. ∙

research

∙ 01/16/2019

Robot Sequential Decision Making using LSTM-based Learning and Logical-probabilistic Reasoning

Sequential decision-making (SDM) plays a key role in intelligent robotic...

30 Saeid Amiri, et al. ∙

research

∙ 11/21/2018

Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots

Task-motion planning (TMP) addresses the problem of efficiently generati...

0 Yuqian Jiang, et al. ∙

research

∙ 09/28/2018

Robot Representing and Reasoning with Knowledge from Reinforcement Learning

Reinforcement learning (RL) agents aim at learning by interacting with a...

0 Keting Lu, et al. ∙

research

∙ 08/20/2018

Learning to Dialogue via Complex Hindsight Experience Replay

Reinforcement learning methods have been used for learning dialogue poli...

0 Keting Lu, et al. ∙

research

∙ 04/23/2018

An Empirical Comparison of PDDL-based and ASP-based Task Planners

General purpose planners enable AI systems to solve many different types...

0 Yuqian Jiang, et al. ∙

research

∙ 08/17/2015

A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture that combines the complementary str...

0 Mohan Sridharan, et al. ∙

research

∙ 05/05/2014

KR^3: An Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture that combines the complementary str...

0 Shiqi Zhang, et al. ∙

research

∙ 07/29/2013

Combining Answer Set Programming and POMDPs for Knowledge Representation and Reasoning on Mobile Robots

For widespread deployment in domains characterized by partial observabil...

0 Shiqi Zhang, et al. ∙

Shiqi Zhang

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