Chao Jiang, Ph.D.

Chao Jiang, Ph.D.

He/Him/His

Associate Professor of Electrical Engineering and Computer Science

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

activities

news

August 2020

Published:

Dr. Vesna Novak and I received the NSF National Robotics Initiative (NRI) grant.

December 2020

Published:

Chao Jiang presented the work “Multi-Robot Guided Policy Search for Learning Decentralized Swarm Control” in the 59th IEEE Conference on Decision and Control (CDC).

October 2021

Published:

Our paper titled “Automated patient-robot assignment for a robotic rehabilitation gym: a simplified simulation model.” has been accepted for publication in the Journal of NeuroEngineering and Rehabilitation.

December 2022

Published:

Chao Jiang is selected as Associate Editor of IEEE Robotics and Automation Magazine (RAM).

June 2023

Published:

Our paper titled “Incorporating Control Barrier Functions in Distributed Model Predictive Control for Multi-Robot Coordinated Control.” has been accepted for publication in IEEE Transactions on Control of Network Systems.

Published:

Our paper titled “End-to-end decentralized formation control using a graph neural network-based learning method.” has been accepted for publication in Frontiers in Robotics and AI.

October 2023

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Our paper titled “Learning Skill Training Schedules from Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym.” has been accepted for publication in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

February 2024

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Our paper titled “Distributed Sampling-Based Model Predictive Control via Belief Propagation for Multi-Robot Formation Navigation” has been accepted for publication in IEEE Robotics and Automation Letters.

July 2024

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Ph.D. student, Umur Atan, presented our paper titled “Assistive Control of Robot Arms Via Adaptive Shared Autonomy” at the 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, USA.

September 2024

Published:

Ph.D. student, Varun Bharadwaj, presented our paper titled “Learning Skill Training Schedules from Domain Experts in a Rehabilitation Gym Using Inverse Reinforcement Learning” at the 2024 IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

March 2025

Published:

Congratulations to Ph.D. student, Varun Bharadwaj, on finishing 3rd in the College of Engineering and Physics Sciences Three-Minute Thesis Competition. He, among a few others, will move on to represent the College in the university-wide competition in the fall.

April 2025

Published:

Dr. Chao Jiang received the University of Wyoming Faculty Grant-In-Aid seed grant.

April 2025

Published:

Our paper titled “Dynamic Patient-Robot Assignment in a Simulated Stochastic Robotic Rehabilitation Gym” has been accepted for publication in IEEE Transactions on Medical Robotics and Bionics (T-MRB).

May 2025

Published:

Ph.D. student, Varun Bharadwaj, presented our paper titled “Preference Augmented Q-Learning for Patient Exercise Scheduling in a Robotic Rehabilitation Gym” at the 2025 IEEE International Conference on Rehabilitation Robotics (ICORR).

May 2025

Published:

Dr. Chao Jiang chaired the session “Reinforcement Learning III” at the 2025 International Conference on Robotics and Automation (ICRA).

July 2025

Published:

Our paper titled “A Probabilistic Inference Approach for Skill-Based Shared Autonomy in Assistive Robotic Manipulation” has been accepted for publication in IEEE Robotics and Automation Letters (RA-L).

portfolio

Multi-Robot Coordinated Planning and Control

Multi-robot motion planning and control has been investigated for decades and is still an active research area due to the growing demand for both performance optimality and safety assurance. We developed learning-based methods and distributed model predictive control (DMPC) methods: 1) a gradient-based algorithm that leverages the alternating direction method of multipliers (ADMM) to decompose the team-level trajectory optimization into subproblems solved by individual robots. The algorithm also incorporates a discrete-time control barrier functions (CBF) as safety constraints to provide formal guarantee of collision avoidance; and 2) a sampling-based method that formulates multi-robot optimal control as probabilistic inference over graphical models and leverages belief propagation to achieve inference via distributed computation. We developed a distributed sampling-based model predictive control (MPC) algorithm based on the proposed framework, which obtains optimal controls via variational inference (VI).

NRI: FND: The Robotic Rehab Gym: Specialized Co-Robot Trainers Working with Multiple Human Trainees for Optimal Learning Outcomes, sponsored by NSF, 2020-2025

Overview: A robotic rehabilitation gym is defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, this project investigates automated systems that can dynamically assign patients to different robots within a session in order to optimize group rehabilitation outcome.

Adaptive Shared Autonomy for Assistive Robotic Manipulation

Overview: Shared autonomy promotes collaboration between humans and robots by integrating human input with autonomous control, allowing humans to perform tasks without the need for precise manual control. A primary problem in shared autonomy is how to determine the optimal balance of control between the user and robot. This problem is further complicated by the variability in human proficiency and preference when performing manipulation tasks. Addressing this issue requires systems to infer human skill levels, enabling dynamic adjustments to the level of assistance. Such adaptability ensures that assistance is neither overly intrusive for experienced users nor insufficient for novices. To address this challenge, this project investigates human-centric shared autonomy approaches that offers adaptive and personalized level of assistance.

publications

Robot-assisted pedestrian regulation in an exit corridor

Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

Recommended citation: C. Jiang, Z. Ni, Y. Guo and H. He. (2016). "Robot-assisted pedestrian regulation in an exit corridor." IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp 815-822
Download Paper

Learning Skill Training Schedules from Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym

Published in IEEE Transactions on Neural Systems & Rehabilitation Engineering (T-NSRE, IF: 5.2), 2023

Recommended citation: B. Adhikari, V.R. Bharadwaj, B.A. Miller, V.D. Novak and C. Jiang. (2023). "Learning Skill Training Schedules from Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym." IEEE Transactions on Neural Systems & Rehabilitation Engineering (T-NSRE). 31. pp 4256-4265.
Download Paper

Assistive Control of Robot Arms via Adaptive Shared Autonomy

Published in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2024

Recommended citation: U. Atan, V.R. Bharadwaj and C. Jiang. (2024). "Assistive Control of Robot Arms via Adaptive Shared Autonomy." IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). pp. 1096-1102.
Download Paper

Learning Skill Training Schedules from Domain Experts in a Rehabilitation Gym using Inverse Reinforcement Learning

Published in IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2024

Recommended citation: V.R. Bharadwaj, B.A. Miller, V.D. Novak and C. Jiang. (2024). "Learning Skill Training Schedules from Domain Experts in a Rehabilitation Gym using Inverse Reinforcement Learning." IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). pp. 1815-1821.
Download Paper

research

Indoor Human Localization: A cooperative localization scheme using robot-smartphone collaboration (past project)

Overview: Smartphone-based human indoor localization was previously implemented using wireless sensor networks at the cost of sensing infrastructure deployment. Motivated by the increasing research attention on location-aware human-robot interaction, this project studies a robot-assisted human indoor localization scheme using acoustic ranging between a self-localized mobile robot and smartphones from human users. Data from the low-cost Kinect vision sensor are fused with smartphone-based acoustic ranging, and an extended Kalman filter based localization algorithm is developed for real-time dynamic position estimation and tracking. Real robot-smartphone experiments are performed, and performances are evaluated in various indoor environments under different environmental noises and with different human walking speed. Compared with existing indoor smartphone localization methods, the proposed system does not rely on wireless sensing infrastructure, and has comparable localization accuracy with increased flexibility and scalability due to the mobility of the robot.

Robot-Assisted Pedestrian Regulation: Learning optimal human-robot interaction (past project)

Overview: This project investigates an emerging application of assistive robots in pedestrian regulation. We propose a novel robot-assisted pedestrian regulation framework that utilizes the dynamic pedestrian-robot interaction during their collective movements. The insights of the effect of pedestrian-robot interaction on the pedestrian movements and the optimal robot motion for desired pedestrian regulation objectives are provided. Adaptive dynamic programming (ADP) algorithm and deep reinforcement learning algorithms are designed to learn optimal control of robot motion. The proposed adaptive learning framework is applied to a merging flow scenarios to reduce the risk of crowd disasters. Furthermore, to address the challenge of feature representation of complex human motion dynamics under the effect of HRI, an end-to-end robot motion planner based on deep neural network is proposed and trained using a deep reinforcement learning algorithm. The new approach avoids hand-crafted feature detection and extraction and thus improves the learning capability for complex dynamic problems.

Multi-Robot Coordinated Planning and Control

Overview: Multi-robot motion planning and control has been investigated for decades and is still an active research area due to the growing demand for both performance optimality and safety assurance. We developed learning-based methods and distributed model predictive control (DMPC) methods: 1) a gradient-based algorithm that leverages the alternating direction method of multipliers (ADMM) to decompose the team-level trajectory optimization into subproblems solved by individual robots. The algorithm also incorporates a discrete-time control barrier functions (CBF) as safety constraints to provide formal guarantee of collision avoidance; and 2) a sampling-based method that formulates multi-robot optimal control as probabilistic inference over graphical models and leverages belief propagation to achieve inference via distributed computation. We developed a distributed sampling-based model predictive control (MPC) algorithm based on the proposed framework, which obtains optimal controls via variational inference (VI).

Adaptive Shared Autonomy for Assistive Robotic Manipulation

Overview: Shared autonomy promotes collaboration between humans and robots by integrating human input with autonomous control, allowing humans to perform tasks without the need for precise manual control. A primary problem in shared autonomy is how to determine the optimal balance of control between the user and robot. This problem is further complicated by the variability in human proficiency and preference when performing manipulation tasks. Addressing this issue requires systems to infer human skill levels, enabling dynamic adjustments to the level of assistance. Such adaptability ensures that assistance is neither overly intrusive for experienced users nor insufficient for novices. To address this challenge, this project investigates human-centric shared autonomy approaches that offers adaptive and personalized level of assistance.

NRI: FND: The Robotic Rehab Gym: Specialized Co-Robot Trainers Working with Multiple Human Trainees for Optimal Learning Outcomes, sponsored by NSF

Overview: A robotic rehabilitation gym is defined as multiple patients training with multiple robots or passive sensorized devices in a group setting. Recent work with such gyms has shown positive rehabilitation outcomes; furthermore, such gyms allow a single therapist to supervise more than one patient, increasing cost-effectiveness. To allow more effective multipatient supervision in future robotic rehabilitation gyms, this project investigates automated systems that can dynamically assign patients to different robots within a session in order to optimize group rehabilitation outcome.

talks

teaching

EE2390 Digital Systems Design

Undergraduate course, University of Wyoming, Department of Electrical Engineering and Computer Science, 2025

Fall 2019, Spring 2020, Spring 2022-2025