Zewen Yang

Zhenxiao Yin, Xiaobing Dai, Zewen Yang et al.

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.

Xiaobing Dai, Zewen Yang

Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced on platforms like GitHub, their deployment often requires writing scripts in specific programming languages, such as Python, C++, or MATLAB. This dependency on particular languages creates a barrier for professionals outside the field of machine learning, making it challenging to integrate these algorithms into their workflows. To address this limitation, we propose GPgym, a remote service node based on Gaussian process regression. GPgym enables experts from diverse fields to seamlessly and flexibly incorporate machine learning techniques into their existing specialized software, without needing to write or manage complex script code.

Zewen Yang, Xiaobing Dai, Dongfa Zhang et al.

Learning from demonstration allows robots to acquire complex skills from human demonstrations, but conventional approaches often require large datasets and fail to generalize across coordinate transformations. In this paper, we propose Prompt2Auto, a geometry-invariant one-shot Gaussian process (GeoGP) learning framework that enables robots to perform human-guided automated control from a single motion prompt. A dataset-construction strategy based on coordinate transformations is introduced that enforces invariance to translation, rotation, and scaling, while supporting multi-step predictions. Moreover, GeoGP is robust to variations in the user's motion prompt and supports multi-skill autonomy. We validate the proposed approach through numerical simulations with the designed user graphical interface and two real-world robotic experiments, which demonstrate that the proposed method is effective, generalizes across tasks, and significantly reduces the demonstration burden. Project page is available at: https://prompt2auto.github.io

Zewen Yang, Xiaobing Dai, Dian Yu et al.

Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existing approaches remain limited in handling multiple types of constraints, such as collision avoidance and dynamic consistency, which are often treated separately or only partially considered. This paper proposes UniConFlow, a unified flow matching (FM) based framework for trajectory generation that systematically incorporates both equality and inequality constraints. UniConFlow introduces a novel prescribed-time zeroing function to enhance flexibility during the inference process, allowing the model to adapt to varying task requirements. To ensure constraint satisfaction, particularly with respect to obstacle avoidance, admissible action range, and kinodynamic consistency, the guidance inputs to the FM model are derived through a quadratic programming formulation, which enables constraint-aware generation without requiring retraining or auxiliary controllers. We conduct mobile navigation and high-dimensional manipulation tasks, demonstrating improved safety and feasibility compared to state-of-the-art constrained generative planners. Project page is available at https://uniconflow.github.io.

Akshat Dubey, Zewen Yang, Georges Hattab

The growing AI field faces trust, transparency, fairness, and discrimination challenges. Despite the need for new regulations, there is a mismatch between regulatory science and AI, preventing a consistent framework. A five-layer nested model for AI design and validation aims to address these issues and streamline AI application design and validation, improving fairness, trust, and AI adoption. This model aligns with regulations, addresses AI practitioner's daily challenges, and offers prescriptive guidance for determining appropriate evaluation approaches by identifying unique validity threats. We have three recommendations motivated by this model: authors should distinguish between layers when claiming contributions to clarify the specific areas in which the contribution is made and to avoid confusion, authors should explicitly state upstream assumptions to ensure that the context and limitations of their AI system are clearly understood, AI venues should promote thorough testing and validation of AI systems and their compliance with regulatory requirements.