34 citations · 105 across the 27 of their papers we have counts for
29 papers
Disturbance Rejection Control for Autonomous Trolley Collection Robots with Prescribed Performance
Rui-Dong Xi, Liang Lu, Xue Zhang +4
Trajectory tracking control of autonomous trolley collection robots (ATCR) is an ambitious work due to the complex environment, serious noise and external disturbances. This work i…
Rethinking Social Robot Navigation: Leveraging the Best of Two Worlds
Amir Hossain Raj, Zichao Hu, Haresh Karnan +6
Empowering robots to navigate in a socially compliant manner is essential for the acceptance of robots moving in human-inhabited environments. Previously, roboticists have develope…
QoS-aware User Association and Transmission Scheduling for Millimeter-Wave Train-ground Communications
Xiangfei Zhang, Yong Niu, Tao Yang +6
With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help…
Addressing Racial Bias in Facial Emotion Recognition
Alex Fan, Xingshuo Xiao, Peter Washington
Fairness in deep learning models trained with high-dimensional inputs and subjective labels remains a complex and understudied area. Facial emotion recognition, a domain where data…
APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback
Zizhao Wang, Xuesu Xiao, Garrett Warnell +1
Classical autonomous navigation systems can control robots in a collision-free manner, oftentimes with verifiable safety and explainability. When facing new environments, however,…
From Agile Ground to Aerial Navigation: Learning from Learned Hallucination
Zizhao Wang, Xuesu Xiao, Alexander J Nettekoven +5
This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through…
Team Orienteering Coverage Planning with Uncertain Reward
Bo Liu, Xuesu Xiao, Peter Stone
Many municipalities and large organizations have fleets of vehicles that need to be coordinated for tasks such as garbage collection or infrastructure inspection. Motivated by this…
APPL: Adaptive Planner Parameter Learning
Xuesu Xiao, Zizhao Wang, Zifan Xu +5
While current autonomous navigation systems allow robots to successfully drive themselves from one point to another in specific environments, they typically require extensive manua…
Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain
Xuesu Xiao, Joydeep Biswas, Peter Stone
This paper presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road nav…
APPLI: Adaptive Planner Parameter Learning From Interventions
Zizhao Wang, Xuesu Xiao, Bo Liu +2
While classical autonomous navigation systems can typically move robots from one point to another safely and in a collision-free manner, these systems may fail or produce suboptima…
APPLR: Adaptive Planner Parameter Learning from Reinforcement
Zifan Xu, Gauraang Dhamankar, Anirudh Nair +5
Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-t…
Extended Abstract: Motion Planners Learned from Geometric Hallucination
Xuesu Xiao, Bo Liu, Peter Stone
Learning motion planners to move robot from one point to another within an obstacle-occupied space in a collision-free manner requires either an extensive amount of data or high-qu…
Agile Robot Navigation through Hallucinated Learning and Sober Deployment
Xuesu Xiao, Bo Liu, Peter Stone
Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous navigation, which uses training data collected in completely safe environments and adds numer…
Benchmarking Metric Ground Navigation
Daniel Perille, Abigail Truong, Xuesu Xiao +1
Metric ground navigation addresses the problem of autonomously moving a robot from one point to another in an obstacle-occupied planar environment in a collision-free manner. It is…
Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments
Xuesu Xiao
This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or…
A Lifelong Learning Approach to Mobile Robot Navigation
Bo Liu, Xuesu Xiao, Peter Stone
This paper presents a self-improving lifelong learning framework for a mobile robot navigating in different environments. Classical static navigation methods require environment-sp…
Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination
Xuesu Xiao, Bo Liu, Garrett Warnell +1
While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot n…
Best Viewpoints for External Robots or Sensors Assisting Other Robots
Jan Dufek, Xuesu Xiao, Robin R. Murphy
This work creates a model of the value of different external viewpoints of a robot performing tasks. The current state of the practice is to use a teleoperated assistant robot to p…
APPLD: Adaptive Planner Parameter Learning from Demonstration
Xuesu Xiao, Bo Liu, Garrett Warnell +2
Existing autonomous robot navigation systems allow robots to move from one point to another in a collision-free manner. However, when facing new environments, these systems general…
Tethered Aerial Visual Assistance
Xuesu Xiao, Jan Dufek, Robin R. Murphy
In this paper, an autonomous tethered Unmanned Aerial Vehicle (UAV) is developed into a visual assistant in a marsupial co-robots team, collaborating with a tele-operated Unmanned…
Robot Risk-Awareness by Formal Risk Reasoning and Planning
Xuesu Xiao, Jan Dufek, Robin Murphy
This paper proposes a formal robot motion risk reasoning framework and develops a risk-aware path planner that minimizes the proposed risk. While robots locomoting in unstructured…
Robot Motion Risk Reasoning Framework
Xuesu Xiao, Jan Dufek, Robin R. Murphy
This paper presents a formal and comprehensive reasoning framework for robot motion risk, with a focus on locomotion in challenging unstructured or confined environments. Risk whic…
ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle
Kshitij Tiwari, Xuesu Xiao, Ville Kyrki +1
This paper presents an approach for estimating the operational range for mobile robot exploration on a single battery discharge. Deploying robots in the wild usually requires unint…
Explicit Motion Risk Representation
Xuesu Xiao, Jan Dufek, Robin Murphy
This paper presents a formal definition and explicit representation of robot motion risk. Currently, robot motion risk has not been formally defined, but has already been used in m…