arXiv subjects
Publications and source records attributed to Dongsheng Zhang.
In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pre-training and supervised fine-tuning, RL post-training, and real-world deployment. Each component serves a distinct role in this stack.
This article propose a whole-body impedance coordinative control framework for a wheel-legged humanoid robot to achieve adaptability on complex terrains while maintaining robot upper body stability. The framework contains a bi-level control strategy. The outer level is a variable damping impedance controller, which optimizes the damping parameters to ensure the stability of the upper body while holding an object. The inner level employs Whole-Body Control (WBC) optimization that integrates real-time terrain estimation based on wheel-foot position and force data. It generates motor torques while accounting for dynamic constraints, joint limits,friction cones, real-time terrain updates, and a model-free friction compensation strategy. The proposed whole-body coordinative control method has been tested on a recently developed quadruped humanoid robot. The results demonstrate that the proposed algorithm effectively controls the robot, maintaining upper body stability to successfully complete a water-carrying task while adapting to varying terrains.
The escalating demand for high-speed and high-precision machining in machine tool feed system has brought to the forefront the challenge of its design method. Currently, existing methodologies struggle to ascertain compliance with dynamic performance requirements during the design phase, often resulting in either excessive or insufficient design. Therefore, there is an urgent need for research focused on feed system design methods that directly address time domain dynamic precision. The dynamic precision of the feed system is influenced by the motor, mechanical structure, motion processes, and control system. However, existing studies on the impact mechanisms of electromechanical matching on feed system precision often overlook the roles of control and motion processes. This paper innovatively proposes the need to consider the coupling effects among subsystems, directing the optimization design of CNC machine tool feed systems towards time domain dynamic precision. Furthermore, it introduces acceleration and deceleration capability as a key indicator of electromechanical matching. Following the decoupling of control system parameters, this study elucidates the influence mechanisms of electromechanical matching on the overall dynamic performance of the feed system under various motion processes. This research offers a novel design philosophy and theoretical foundation for the optimization of CNC machine tool feed systems.
With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand. Hence, in this paper, the dynamics matching design methods of existing CNC machine tool feed systems were investigated and analyzed. Further, sub-system coupling mechanisms and optimization design studies were carried out for each sub-system. First, the required kinematic indexes must be achieved when designing the feed system dynamics of high-speed, high-precision CNC machine tools. Second, the CNC machine tool feed systems generally have four sub-systems: motion process, control system, motor, and mechanical structure. The coupling effect between the sub-systems should also be considered in the design. Based on the dynamics design, each sub-system should be optimized to maximize the system dynamic performance with minimum resource allocation. Finally, based on the review, future research directions within the field were detected.
Laser metal additive manufacturing technology is capable of producing components with complex geometries and compositions that cannot be realized by conventional manufacturing methods. However, a large number of pores generated during the additive manufacturing process greatly affect the mechanical properties of the additively manufactured parts, and the mechanism of such pore generation has not been revealed by direct observation clearly. Here, we report the mechanism of pore generation in the laser direct energy deposition process as revealed by {\it in-situ} high-speed high-resolution synchrotron X-ray imaging. We found that dissolution and re-precipitation of external gases and precipitation of metal vapors are the two main mechanisms of pore formation. We further explored the effects of different process parameters on the generation of pores and optimized the process to suppress pore generation. This work provides important insights into the formation of porosity defects during laser metal additive manufacturing, and can provide guidance for related process optimization.
When developing high-speed and high-precision CNC machine tools, subsystem coupling effects must be considered while designing the feed system to maximize its dynamic performance. Currently, the influence of changes in control parameters on the matching characteristics of each subsystem was not yet considered when studying the coupling relationship between subsystems. Therefore, it is difficult to define the law of action between subsystems under the interference of control parameters. Hence, a new method was proposed in this paper aiming to isolate the disturbance of control parameters and to highlight the actions between electromechanical subsystems. This was achieved by optimizing the servo control parameters for each configuration of electromechanical parameters, ensuring that the feed system performance is optimal for the observed configuration. This approach eliminated the influence of the control subsystem on the electromechanical coupling relationship. The approach effectiveness was verified via the integrated model of the feed system. As such, this paper provides a reliable method to further study the coupling mechanism of subsystems, revealing the mechanism behind the dynamic design of feed systems.
To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imitation learning techniques for implementing autonomous robotic pouring have an inherent black-box effect and require a large amount of demonstration data for model training. To address these issues, an Explainable Hierarchical Imitation Learning (EHIL) method is proposed in this paper such that a robot can learn high-level general knowledge and execute low-level actions across multiple drink pouring scenarios. Moreover, with EHIL, a logical graph can be constructed for task execution, through which the decision-making process for action generation can be made explainable to users and the causes of failure can be traced out. Based on the logical graph, the framework is manipulable to achieve different targets while the adaptability to unseen scenarios can be achieved in an explainable manner. A series of experiments have been conducted to verify the effectiveness of the proposed method. Results indicate that EHIL outperforms the traditional behavior cloning method in terms of success rate, adaptability, manipulability and explainability.