Research
(*: equal contribution)
Please feel free to contact me if you are interested in any of the following works.
Journal Publications
Optimal gait design for a soft quadruped robot via multi-fidelity Bayesian optimization
Authors: Kaige Tan, Xuezhi Niu, Qinglei Ji, Lei Feng & Martin Törngren
Applied Soft Computing, 2025
[Paper] [Code] [Abstract] [BibTeX]
This study focuses on the locomotion capability improvement in a tendon-driven soft quadruped robot through an online adaptive learning approach. Leveraging the inverse kinematics model of the soft quadruped robot, we employ a central pattern generator to design a parametric gait pattern, and use Bayesian optimization (BO) to find the optimal parameters. Further, to address the challenges of modeling discrepancies, we implement a multi-fidelity BO approach, combining data from both simulation and physical experiments throughout training and optimization. This strategy enables the adaptive refinement of the gait pattern and ensures a smooth transition from simulation to real-world deployment for the controller. Compared to previous result using a fixed gait pattern, the multi-fidelity BO approach improves the robot’s average walking speed from 0.14 m/s to 0.214 m/s, an increase of 52.7%. Moreover, we integrate a computational task off-loading architecture by edge computing, which reduces the onboard computational and memory overhead, to improve real-time control performance and facilitate an effective online learning process. The proposed approach successfully achieves optimal walking gait design for physical deployment with high efficiency, effectively addressing challenges related to the reality gap in soft robotics.
@article{TAN2025112568,
title = {Optimal gait design for a soft quadruped robot via multi-fidelity Bayesian optimization},
journal = {Applied Soft Computing},
volume = {169},
pages = {112568},
year = {2025},
issn = {1568-4946},
doi = {https://doi.org/10.1016/j.asoc.2024.112568},
url = {https://www.sciencedirect.com/science/article/pii/S1568494624013425},
author = {Kaige Tan and Xuezhi Niu and Qinglei Ji and Lei Feng and Martin Törngren},
}Conference Publications
Electronic-free Pneumatic Interface for Sensorimotor Human-Robot Interaction
Authors: Jing Xu, Xuezhi Niu , Didem Gürdür Broo & Klas Hjort
Submitted to IEEE International Conference on Robotics and Automation (ICRA2026)
[Paper coming soon] [Abstract]
Most existing haptic interfaces for human-robot interaction rely on electronic components, which pose challenges in harsh environments and require complex control architectures. This paper presents a novel pneumatic interface that provides electronic-free sensorimotor functions, integrating both sensing and actuation using purely pneumatic principles. The key technical innovation is a pneumatic valve architecture that achieves direct mechanical-fluidic coupling between contact sensing and haptic actuation, eliminating the need for electronic sensors, digital processing, or continuous power supplies. The system operates through a closed-loop mechanism where pressure variations generated by contact forces directly regulate pneumatic valve states, which in turn actuate wearable soft components. System characterization experiments demonstrate consistent force activation thresholds and reliable valve switching behavior across varying contact conditions. The technical approach is validated through two proof-of-concept applications: a perceptive haptic glove and an assistive grasping system with human feedback. Preliminary user trials confirm functional performance and provide initial usability feedback, demonstrating the system's ability to convey tactile information for object discrimination and grasp control. This work establishes the technical feasibility of electronics-free sensorimotor coupling and opens new possibilities for potential advantages for haptic interfaces in challenging environments where conventional electronic systems cannot operate reliably.
"What am I supposed to do now?": Exploring Unscripted Human Responses During Robot Malfunction in a Collaborative Quality Control and Repair Task
Authors: Alexandros Rouchitsas, Xuezhi Niu , Ginevra Castellano & Didem Gürdür Broo
Submitted to The ACM Conference on Human Factors in Computing Systems (CHI2026)
[Paper coming soon] [Abstract]
Robot malfunctions are unavoidable in human–robot collaboration and oftentimes detrimental. Yet humans are rarely instructed on how to respond in such moments, leaving ample room for spontaneity and unpredictability. We studied 65 participants working alongside a collaborative robot under both normal operation and deliberate malfunction conditions. We analyzed unscripted vocal and action responses regarding situational awareness (SA)—whether malfunctions were noticed—and task-oriented response appropriateness—whether responses advanced or undermined the collaboration. During malfunctions, SA was nearly universal, as was frustration and confusion, yet appropriateness diverged sharply: 22 participants responded only productively, 7 only unproductively or counterproductively, 15 did both, while 20 attempted nothing whatsoever. Unscripted responses ranged from clarifying questions and corrective actions to sarcasm, comedic gestures, and erroneous markings. Our findings reveal a fragile link between SA and collaboration quality, highlighting the need for robot transparency, explainability and adaptability, so collaborators are actively supported when things fail.
Investigating Symbiosis in Robotic Ecosystems: A Case Study for Multi-Robot Reinforcement Learning Reward Shaping
Authors: Xuezhi Niu & Didem Gürdür Broo
2025 9th International Conference on Robotics and Automation Sciences (ICRAS)
[Paper] [Code] [Abstract] [BibTeX]
This paper presents a bio-inspired reward shaping approach for multi-agent reinforcement learning (MARL) in heterogeneous multi-robot systems, leveraging a formal symbiosis model to enhance cooperation. We categorize interactions based on mutualism, commensalism, and parasitism, introducing constructs such as graph models, state transition systems, and resource flow models to characterize inter-agent dependencies. By incorporating a taxonomy of symbiotic relationships into MARL, we define reward structures that reinforce cooperative behavior in complex tasks. Our experimental results demonstrate that while traditional rewards suffice for simple tasks like CartPendulum, mutualistic rewards provide qualitative benefits in high-dimensional tasks such as ShadowHand Object Passing and Mobile Manipulation, including increased learning stability, smoother convergence, and reduced performance variance. These findings suggest that symbiotic reward shaping provides a structured mechanism for enhancing multi-robot cooperation, with benefits that extend beyond numerical performance metrics. Future work should explore adaptive interaction mechanisms and generalization across diverse robotic applications.
@INPROCEEDINGS{11108729,
author={Niu, Xuezhi and Broo, Didem Gürdür},
booktitle={2025 9th International Conference on Robotics and Automation Sciences (ICRAS)},
title={Investigating Symbiosis in Robotic Ecosystems: A Case Study for Multi-Robot Reinforcement Learning Reward Shaping},
year={2025},
pages={112-117},
doi={10.1109/ICRAS65818.2025.11108729}
}
Enabling Symbiosis in Multi-Robot Systems through Multi-Agent Reinforcement Learning
Authors: Xuezhi Niu, Natalia Calvo Barajas & Didem Gürdür Broo
2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS)
Current cyber-physical systems, including multi-robot systems, often fail to interoperate effectively, resulting in suboptimal performance, inefficient resource utilization, and poor resilience. Inspired by natural symbiotic relationships, such as tree-fungi networks, we propose an architecture that integrates ecological symbiosis principles into multi-robot system specifications. Specifically, we incorporate symbiotic principles into multiagent reinforcement learning (MARL) within a centralized training, decentralized execution framework. Comprehensive scenario-based evaluations in a simulated warehouse environment show that our symbiotic MARL framework improves system performance (10.7%) and resource utilization (13.81%) compared to non-symbiotic baselines. Agents dynamically adjust their behavior in response to environmental changes, ensuring continuous task execution, efficient navigation, and balanced energy use. These findings demonstrate that integrating ecological principles into MARL enhances the system's efficiency and performance. The framework's success in promoting sustainable resource usage while maintaining high task performance suggests broader applications across various cyber-physical domains where adaptive coordination is crucial.
@INPROCEEDINGS{niu2025enabling,
author={Niu, Xuezhi and Barajas, Natalia Calvo and Broo, Didem Gürdür},
booktitle={2025 IEEE 8th International Conference on Industrial Cyber-Physical Systems (ICPS)},
title={Enabling Symbiosis in Multi-Robot Systems Through Multi-Agent Reinforcement Learning},
year={2025},
pages={1-7},
doi={10.1109/ICPS65515.2025.11087893}
}
Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning
Authors: Xuezhi Niu*, Kaige Tan*, Didem Gürdür Broo & Lei Feng
2025 IEEE International Conference on Robotics and Automation (ICRA)
[PDF] [Code] [Abstract] [BibTeX]
This study presents an innovative approach to optimal gait control for a soft quadruped robot enabled by four compressible tendon-driven soft actuators. Soft quadruped robots, compared to their rigid counterparts, are widely recognized for offering enhanced safety, lower weight, and simpler fabrication and control mechanisms. However, their highly deformable structure introduces nonlinear dynamics, making precise gait locomotion control complex. To solve this problem, we propose a novel model-based reinforcement learning (MBRL) method. The study employs a multi-stage approach, including state space restriction, data-driven surrogate model training, and MBRL development. Compared to benchmark methods, the proposed approach significantly improves the efficiency and performance of gait control policies. The developed policy is both robust and adaptable to the robot's deformable morphology. The study concludes by highlighting the practical applicability of these findings in real-world scenarios.
@inproceedings{niu2025optimal,
title = {Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning},
author = {Niu, Xuezhi and Tan, Kaige and G{\"u}rd{\"u}r Broo, Didem and Feng, Lei},
booktitle = {2025 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2025},
pages = {9287-9293},
doi = {10.1109/ICRA55743.2025.11128611},
url = {https://doi.org/10.1109/ICRA55743.2025.11128611}
}Thesis
- Optimal Gait Control of Soft Quadruped Robot by Model-based Reinforcement Learning
Xuezhi Niu
M.Sc. Thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 2023
Technical Report
- Electronically Vacuum Regulated Shut-off Valve for Milking System
Carl Egenäs*, Felix Ekman*, Chenqi Ma*, Tim Naser*, Xuezhi Niu*, Axel Sernelin*, Samuel Stenow*, Benjamin StrÖm*
H.K. Project, KTH Royal Institute of Technology, Stockholm, Sweden, 2023
Reviewer
IEEE International Conference on Robotics and Automation (ICRA), IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), IEEE-RAS International Conference on Humanoid Robots (Humanoids), IEEE International Conference on Robot and Human Interactive Communication (ROMAN).