build high-fidelity simulator

This work addresses the challenge of constructing high-fidelity actuator dynamics models from real robot data in the absence of torque sensors, current measurements, or internal motor information. The authors formulate actuator identification as a trajectory-matching problem, relying solely on joint position and velocity provided by encoders. By integrating differentiable simulation with gradient-based optimization, their unified framework accommodates a spectrum of model classes—from compact analytical formulations to neural actuator mappings—without requiring specialized test benches or torque sensing. Experimental results demonstrate that the proposed approach reduces position error from 14.20 mrad to 7.54 mrad. Furthermore, when applied to downstream motion policy training, it yields a 46% increase in robot travel distance and a 75% reduction in heading deviation.

Existing CPS co-simulation tools suffer from limited portability, modularity, and automation. To address these limitations, this paper proposes a Python-based programmable co-simulation framework. The framework enables declarative orchestration and runtime dynamic substitution of multi-fidelity heterogeneous components—breaking away from conventional static configuration paradigms. It adopts a componentized architecture, supports distributed communication via ZeroMQ and ROS, and provides standardized adaptation interfaces for third-party platforms (e.g., PX4), thereby enabling cross-platform, reconfigurable co-simulation. Its core innovation is the first-ever declarative component orchestration mechanism, which significantly enhances simulation system reusability, reproducibility, and development efficiency. The framework is validated through co-simulation of unmanned aerial vehicles and autonomous controllers, demonstrating its flexibility and practicality. This work establishes a novel paradigm for CPS benchmark construction and automated evaluation.

This work addresses the sim-to-real transfer failure commonly encountered in reinforcement learning due to mismatches between idealized actuator models used in simulation and the nonlinear, hardware-dependent motor dynamics of real robots. To bridge this gap, the authors propose “actuator reality shaping,” a method that deploys a two-degree-of-freedom feedforward–feedback controller on physical hardware to shape the closed-loop actuator response to closely match an ideal second-order reference model assumed in simulation. Notably, this approach requires no system identification or learned actuator models and enables zero-shot policy deployment through a standardized actuator interface. Experiments across diverse platforms—including single-joint servos, a 7-DoF manipulator, wheeled-legged robots, and humanoids—demonstrate substantial reductions in tracking error and successful zero-shot transfer across multiple tasks and systems, thereby shifting the paradigm from increasing simulation fidelity to unifying real-world actuator behavior to conform to simulation assumptions.

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

本文通过动态反馈线性化方法，使多旋翼无人机模拟固定翼飞行器的动态特性，以解决固定翼飞机训练和仿真中的操作限制问题。

本文提出了一种任务导向的执行器硬件与控制协同设计优化框架，通过代理模型和混合变量优化策略，提高了机器人在特定任务中的性能。

This study addresses the degradation in tracking accuracy and high adaptation costs caused by dynamics mismatch in sim-to-real transfer. To this end, we propose the OSRAM framework, which innovatively models the robot and policy as a closed-loop system. Specifically, it employs meta-learning to train a closed-loop dynamics model that can be rapidly fine-tuned with minimal real-world interactions. Subsequently, reference commands are optimized online via model predictive control, enabling efficient adaptation to real environments without fine-tuning the underlying policy. Experimental results demonstrate that the proposed method significantly improves both prediction and tracking accuracy in bipedal velocity tracking and mobile manipulation tasks, effectively reducing residual errors across diverse hardware configurations.

This study addresses the insufficient understanding of cross-paradigm architectural discrepancies and the absence of design knowledge embedding when AI components replace traditional control laws. Employing a literature-based taxonomy, we conducted static analysis on 62 Simulink models alongside an empirical survey of 13 practitioners. This work quantitatively reveals, for the first time, three major architectural contradictions: subsystem-organization-dominated structures, discrete dynamic dependencies, and the disappearance of constraint-enforcement modules, highlighting traceability ruptures caused by the implicitization of safety mechanisms. Furthermore, it identifies that core logic constitutes a minimal proportion within AI controllers, while user-defined abstractions are absent and explicit safety structures degrade. Collectively, these findings systematically delineate the architectural migration risks inherent in transitioning from conventional control to AI-driven paradigms.

This study presents a high-fidelity digital twin system for agricultural tractors, developed on the Mevea simulation platform, to enhance research efficiency and accessibility. For the first time, the system integrates the ISOBUS protocol with a virtual CAN channel (Kvaser CanKing), enabling ISOBUS-compliant communication simulation implemented in Python. Synchronized straight-line driving and steering tests demonstrate strong agreement between the virtual model and the physical tractor in lateral dynamic behavior, with deviations of only 5–10%, thereby establishing a solid foundation for future extensions to hydraulic systems and implement dynamics. Longitudinal characteristics remain unoptimized due to limited data availability. This work significantly advances the protocol compatibility and control simulation capabilities of tractor digital twins.