Anxing Xiao (肖岸星), Robotics Researcher & Engineer

I am a Ph.D. student in Computer Science at the National University of Singapore, advised by Prof. David Hsu. My research primarily focuses on developing compositional reasoning algorithms and multimodal interaction systems for intelligent robots to adaptively perform assistive tasks in dynamic and open human-centered environments .

Previously, I worked as a Research Assistant with Prof. Max Q.-H. Meng. I received my B.E. in Automation from the Harbin Institute of Technology, Shenzhen in 2021. During my junior year, I was a visiting student at UC Berkeley, working with Prof. Koushil Sreenath.

I love playing basketball 🏀 and table tennis 🏓 in my free time. I am also open to collaborating with people to explore the possibilities of robotics in various fields. Read more about my research or get in touch.

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News

Honors and Awards

  • Best Service Robot Paper Finalist in ICRA 2021
  • Dean's Award at HIT Shenzhen, 2021.4
  • National Scholarship, 2018.10
  • First-class Undergraduate Academic Scholarship at HIT Shenzhen, 2018-2021

Academic Services

    Journal Reviewer:

  • IEEE Transactions on Robotics (TRO), 2021, 2024
  • IEEE Robotics and Automation Letters (RAL), 2022, 2023, 2024
  • IEEE Transactions on Industrial Electronics (TIE), 2024
  • Biomimetic Intelligence and Robotics (BIROB), 2021
  • Conference Reviewer:

  • IEEE International Conference on Robotics and Automation (ICRA), 2022, 2023, 2024
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, 2024

Publications

2024

Octopi: Object Property Reasoning with Large Tactile-Language Models
Samson Yu, Kelvin Lin, Anxing Xiao, Jiafei Duan, and Harold Soh
Accepted to Robotics: Science and Systems (RSS) 2024.
arXiv / Website / Code

2023

Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots
Bingyi Xia, Hao Luan, Ziqi Zhao, Xuheng Gao, Peijia Xie, Anxing Xiao, Jiankun Wang, Max Q-H Meng
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2023.
arXiv / Video

Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach
Yanbo Chen, Zhengzhe Xu, Zhuozhu Jian, Gengpan Tang, Yunong Yangli, Anxing Xiao, Xueqian Wang, Bin Liang
IEEE International Conference on Robotics and Automation (ICRA) 2023.
arXiv / Video

2022

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control
Anxing Xiao*, Hao Luan*, Ziqi Zhao*, Yue Hong, Jieting Zhao, Jiankun Wang, Max Q-H Meng
IEEE International Conference on Robotics and Automation (ICRA) 2022.
arXiv / Video

PUTN: A Plane-fitting based Uneven Terrain Navigation Framework
Zhuozhu Jian, Zihong Lu, Xiao Zhou, Bin Lan, Anxing Xiao, Xueqian Wang, Bin Liang
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022.
arXiv / Video / Code

2021

Robotic Guide Dog: Leading Human with Leash-Guided Hybrid Physical Interaction
Anxing Xiao*, Wenzhe Tong*, Lizhi Yang*, Jun Zeng, Zhongyu Li and Koushil Sreenath
IEEE International Conference on Robotics and Automation (ICRA) 2021.
This paper was the ICRA Best Paper Award Finalist for Service Robotics.
arXiv / Video Media: New Scientist / Daily Mail / The Independent / Tech Xplore / Daily Californian

Autonomous Navigation with Optimized Jumping through Constrained Obstacles on Quadrupeds
Scott Gilroy, Derek Lau, Lizhi Yang, Ed Izaguirre, Kristen Biermayer, Anxing Xiao, Mengti Sun, Ayush Agrawal, Jun Zeng, Zhongyu Li, Koushil Sreenath
IEEE International Conference on Automation Science and Engineering (CASE) 2021.
arXiv / Video

2020

Amphibious Robot’s Trajectory Tracking with DNN-Based Nonlinear Model Predictive Control
Yaqi Wu*, Anxing Xiao*, Haoyao Chen, Shiwu Zhang and Yunhui Liu
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) 2020.
arXiv / Video

* co-first author

 

Research

My research interests span general purpose robotic algorithms and specialised robotic systems, including interactive decision making, motion planning and control, assistive robotics, and human-robot interaction. I aim to narrow the gap between robotics research and its applications in socially aware scenarios. My long-term research goal is to develop autonomous robots that can reason and interact with the dynamic and open human-centred environments.



Open-world Reasoning & Planning


Robi Butler: Multimodal Remote Interaction with Household Robotic Assistants

First Author | Mobile Manipulation Workshop @ ICRA 2024
In this paper, we introduce Robi Butler, a novel household robotic system that enables multimodal interaction with the user. Leveraging advanced communication interfaces, Robi Butler enables users to monitor the robot's status, give text/voice instruction, and select target objects with hand pointing. At the core of our robotic system are the high-level behavior module powered by Large Language Models (LLMs) that interpret received multimodal instructions to generate plans, and open-vocabulary primitives supported by the Vision-Language Models (VLMs) for executing the planned actions with text and pointing queries. The integration of above components allows Robi Butler to ground remote multimodal instruction in the real-world home environment in a zero-shot manner.
Paper Video


Octopi: Object Property Reasoning with Large Tactile-Language Models

Collaboration | Accepted by RSS 2024
In this work, we investigate combining tactile perception with language, which enables embodied systems to obtain physical properties through interaction and apply common-sense reasoning. We contribute a new dataset PHYSICLEAR, which comprises both physical/property reasoning tasks and annotated tactile videos obtained using a GelSight tactile sensor. We then introduce OCTOPI, a system that leverages both tactile representation learning and large vision-language models to predict and reason about tactile inputs with minimal language fine-tuning. Our evaluations on PHYSICLEAR show that OCTOPI is able to effectively use intermediate physical property predictions to improve physical reasoning in both trained tasks and for zero-shot reasoning.
Paper Website


LLM-State: Expandable State Representation for Long-horizon Task Planning in the Open World

Collaboration | Preprint
We propose a novel, expandable state representation that provides continuous expansion and updating of object attributes from the Language Model's inherent capabilities for context understanding and historical action reasoning. Our proposed representation maintains a comprehensive record of an object's attributes and changes, enabling robust retrospective summary of the sequence of actions leading to the current state. We validate our model through experiments across simulated and real-world task planning scenarios, demonstrating significant improvements over baseline methods in a variety of tasks requiring long-horizon state tracking and reasoning.
Paper Video


Intelligent Service Robots


Collaborative Trolley Transportation System with Autonomous Nonholonomic Robots

Research Mentor | IROS 2023
This paper presents an autonomous nonholonomic multi-robot system and a hierarchical autonomy framework for collaborative luggage trolley transportation. This framework finds kinematic-feasible paths, computes online motion plans, and provides feedback that enables the multi-robot system to handle long lines of luggage trolleys and navigate obstacles and pedestrians while dealing with multiple inherently complex and coupled constraints. We demonstrate the designed collaborative trolley transportation system through practical transportation tasks in complex and dynamic environments.
Paper Video


Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach

Research Mentor | ICRA 2023
We propose a novel guidance robot system with a comfort-based concept. To allow humans to be guided safely and more comfortably to the target position in complex environments, our proposed force planner can plan the forces experienced by the human with the force-based human motion model. And the proposed motion planner generate the specific motion command for robot and controllable leash to track the planned force. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios.
Paper Video


Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

First Author | ICRA 2022
We propose a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception stragy and MPC-based planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated.
Paper Video


Robotic Guide Dog: Leading a Human with Leash-Guided Hybrid Physical Interactions

First Author | ICRA 2021
We propose a hybrid physical Human-Robot Interaction model that involves leash tension to describe the dynamical relationship in the robot-guiding human system. This hybrid model is utilized in a mixed-integer programming problem to develop a reactive planner that is able to utilize slack-taut switching to guide a blind-folded person to safely travel in a confined space. The proposed leash-guided robot framework is deployed on a Mini Cheetah quadrupedal robot and validated in experiments.
Paper Video

Teaching

  • NUS CS2109S Introduction to AI and Machine Learning (24 Spring)

Selected Coursework

Artificial Intelligence:

  • NUS CS6216: Graph Machine Learning (Prof. Xavier Bresson)
  • NUS CS5340: Probabilistic Graphical Models (Prof. Harold Soh)
  • NUS CS5242: Neural Networks and Deep Learning (Prof. Yang You)
  • Berkeley CS294: Geometry and Learning for 3D Vision (Prof. Yi Ma)
  • HIT AUTO2012: Introduction to Machine Learning

Control:

  • Berkeley EE291E: Hybrid System and Intelligent Control (Prof. S. Shankar Sastry)
  • Berkeley ME232: Advanced Control Systems (Prof. Kameshwar Poolla)
  • Berkeley EE220C: State Estimation and Optimal Control (Prof. Mark Mueller)
  • Berkeley EE128: Feedback Control System (Prof. Ronald Fearing)

Robotics:

  • NUS CS6244: Using Language Models in Visual Perception (Prof. Angela Yao)
  • Berkeley EECS106B: Robotic Manipulation and Interaction (Prof. Ruzena Bajcsy, Prof. S. Shankar Sastry)
  • Berkeley ME102B: Mechatronics Design (Prof. Hannah Stuart)
  • HITSZ AUTO2004: Design and Practice of Robotic System

Theoretical:

  • NUS CS5461: Algorithmic Mechanism Design (Prof. Warut Suksompong)
  • NUS CS6235: Mathematical Toolkit for CS Theory Research. (Prof. Jonathan Scarlett)
  • Berkeley E231: Mathematical Methods in Engineering. (Prof. Andrew Packard, Prof. Murat Arcak, Prof. Mark Mueller)
  • HIT MATH1009: Advanced Linear Algebra I, II (张贤科)
  • HIT MATH1010: Mathematical Analysis I, II, III (严质彬)
  • HIT EMEC1002: Theoretical Mechanics

Misc

Email

The best way to reach me is via email:
anxingxiao [at] gmail.com (Primary)
anxingxiao [at] u.nus.edu. (Academic)
anxingx [at] comp.nus.edu.sg (Research)

Office

You can often find me at:
AI Lab 1, COM3 Building, 2nd Floor, Room 02-21, C95. (Primary)
Robot Living Studio, COM1 Building, 1nd Floor, Room 01-24.

Links

Interesting Course Projects

Localisation For Underwater Vehicles Using a Forward-Looking Sonar

NUS CS5340 Uncertainty Modelling in AI

Utilized Markov Random Fields (MRF) to denoise forward-looking sonar (FLS) images and Bayesian optimization to estimate the odometry; Implemented the Monte Carlo Localisation algorithm for Autonomous Underwater Vehicles (AUVs) on data collected from AUVs operating in open water.


Reach-Avoid Games via Deep Reinforcement Learning

HIT Auto2012 Introduction to Maching Learning

Designed training pipelines to solve reach-avoid games using the Soft Actor Criticism (SAC) algorithm; The model was trained in Robotarium simulations and transferred to real-world experiments; Learned policy performed better than the baseline MPC method and human policy in both defense and attack tasks.


Automatic notesbook scanner

Berkeley ME102B Mechatronics Design

Completed 3D model design in SolidWorks and manufactured parts of the scanner by 3D printing and laser cutting; Integrated electronics components to achieve autonomous page turning and scanning; Processed the scanner image with perspective transformation and adaptive threshold using OpenCV.


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