Yongyao Li

Humans could sensitively perceive and identify objects through dense mechanoreceptors distributed on the skin of curved fingertips. Inspired by this biological structure, this study presents a general conformal integration method for flexible tactile sensors on curved fingertip surfaces. By adopting a spherical partition design and an inverse mode auxiliary layering process, it ensures the uniform distribution of stress at different curvatures. The sensor adopts a 3 × 3 tactile array configuration, replicating the 3D curved surface distribution of human mechanoreceptors. By analyzing multi-point outputs, the sensor reconstructs contact pressure gradients and infers the softness or stiffness of touched objects, thereby realizing both structural and functional bionics. These sensors exhibit excellent linearity within 0–100 kPa (sensitivity ≈ 36.86 kPa⁻¹), fast response (2 ms), and outstanding durability (signal decay of only 1.94% after 30,000 cycles). It is worth noting that this conformal tactile fingertip integration method not only exhibits uniform responses at each unit, but also has the preload-free advantage, and then performs well in pulse detection and hardness discrimination. This work provides a novel bioinspired pathway for conformal integration of tactile sensors, enabling artificial skins and robotic fingertips with human-like tactile perception.

In recent years, humanoid robots have made substantial advances in motion control and multimodal interaction. However, full-size humanoid robots face significant technical challenges due to their inherent geometric and physical properties, leading to large inertia of humanoid robots and substantial driving forces. These characteristics result in issues such as limited biomimetic capabilities, low control efficiency, and complex system integration, thereby restricting practical applications of full-size humanoid robots in real-world settings. To address these limitations, this paper incorporates a biomimetic design approach that draws inspiration from biological structures and movement mechanisms to enhance the robot’s human-like movements and overall efficiency. The platform introduced in this paper, Loong, is designed to overcome these challenges, offering a practically viable solution for full-size humanoid robots. The research team has innovatively used highly biomimetic joint designs to enhance Loong’s capacity for human-like movements and developed a multi-level control architecture along with a multi-master high-speed real-time communication mechanism that significantly improves its control efficiency. In addition, Loong incorporates a modular system integration strategy, which offers substantial advantages in mass production and maintenance, which improves its adaptability and practical utility for diverse operational environments. The biomimetic approach not only enhances Loong’s functionality but also enables it to perform better in complex and dynamic environments. To validate Loong’s design performance, extensive experimental tests were performed, which demonstrated the robot’s ability to traverse complex terrains such as 13 cm steps and 20° slopes and its competence in object manipulation and transportation. These innovations provide a new design paradigm for the development of full-size humanoid robots while laying a more compatible foundation for the development of hardware platforms for medium- and small-sized humanoid robots. This work makes a significant contribution to the practical deployment of humanoid robots.

Recent advances in bionic intelligence are reshaping humanoid-robot design, demonstrating unprecedented agility, dexterity and task versatility. These breakthroughs drive an increasing need for large scale and high-quality data. Current data generation methods, however, are often expensive and time-consuming. To address this, we introduce Digital Twin Loong (DT-Loong), a digital twin system that combines a high-fidelity simulation environment with a full-scale virtual replica of the humanoid robot Loong, a bionic robot encompassing biomimetic joint design and movement mechanism. By integrating optical motion capture and human-to-humanoid motion re-targeting technologies, DT-Loong generates data for training and refining embodied AI models. We showcase the data collected from the system is of high quality. DT-Loong also proposes a Priority-Guided Quadratic Optimization algorithm for action retargeting, which achieves lower time delay and enhanced mapping accuracy. This approach enables real-time environmental feedback and anomaly detection, making it well-suited for monitoring and patrol applications. Our comprehensive framework establishes a foundation for humanoid robot training and further digital twin applications in humanoid robots to enhance their human-like behaviors through the emulation of biological systems and learning processes.