While many animals exhibit impressive hopping capabilities, machines have struggled to match their performance. Current hopping robots face limitations in power density, energy efficiency, and control stability. Here, we present a parallel-elastic actuation mechanism with a reactive latch that optimizes energy transfer, enabling a legged robot to achieve hopping heights and continuity previously unattainable. This mechanism efficiently stores and releases energy, extending the actuation period over the aerial phase while minimizing stance time. Our robot achieves a maximum hopping height of 3.6 meters, surpassing both human and animal records while demonstrating sustained, high-frequency hopping cycles with minimal power requirement. By integrating inertia-based onboard sensorimotor autonomy, we demonstrate stable, controlled hopping in environments without external aid. These results represent a step toward bridging the performance gap between biological and robotic locomotion, with potential to influence the design of future legged systems.

Mobile robots have revolutionized various fields, offering solutions for manipulation, environmental monitoring, and exploration. However, payload capacity remains a limitation. This paper presents a novel thrust-based robotic hopper capable of carrying payloads up to 9 times its own weight while maintaining agile mobility over less structured terrain. The 220 gram robot carries up to 2 kg while hopping—–a capability that bridges the gap between high-payload ground robots and agile aerial platforms. Key advancements that enable this high-payload capacity include the integration of bidirectional thrusters, allowing for both upward and downward thrust generation to enhance energy management while hopping. Additionally, we present a refined model of dynamics that accounts for heavy payload conditions, particularly for large jumps. To address the increased computational demands, we employ a neural network compression technique, ensuring real-time onboard control. The robot's capabilities are demonstrated through a series of experiments, including leaping over a high obstacle, executing sharp turns with large steps, as well as performing simple autonomous navigation while carrying a 730 g LiDAR payload. This showcases the robot's potential for applications such as mobile sensing and mapping in challenging environments.

This paper develops an optimal actuator placement method for direct-drive stages. Control bandwidth of rigid body motion, which is limited by flexible modes, is maximized. According to a reduced-order state-space model of stage dynamics, a dual-loop optimization method is proposed to optimize actuator locations. In the inner loop, a specific control scheme is employed to obtain the maximum control bandwidth, which is used to formulate a cost function. This control scheme is only for optimization, which does not limit the choices of control methods for an optimized stage. In the outer loop, Genetic Algorithm (GA) is employed to minimize the cost function such that the actuator locations are optimized. Compared with existing over-actuation methods, this method has low cost and complexity. Numerical simulations verify the effectiveness of the proposed method.

Tracking and synchronization accuracies are key performance indicators for advanced wafer scanners. In this paper, we propose a new Parallel Inverse-Model-based Iterative Learning Control (PIMILC) method in which the tracking and synchronization accuracies of the master-slave wafer scanners are jointly considered. In PIMILC, a parallel ILC structure is adopted and the tracking error of the wafer stage filtered by a compensation filter is fed into the reticle stage to decouple the learning system. Furthermore, an inverse-model-based learning law with robustness enhancement techniques is used to trade-off among robustness, accuracy and convergence rate. Simulation results confirm that the PIMILC method can significantly reduce the tracking and synchronization errors compared to prior work.

In this paper, we propose a force feedback scheme for Delta device to improve precision and stability in master-slave teleoperation. A simple and exact dynamical equation is created with principle of virtual work, which is easy to calculate in real-time. After analysing three items deep in dynamical equation, a reasonable strategy is designed to identify the mass of Delta mechanism. The identified parameters and dynamical equation are verified correct in ADAMS and MATLAB softwares. Simultaneously, following previous work on haptic interface, a suitable Kalman Filter algorithm is proposed to attain smooth contact force in real-time based on the impedance of environment, and simplified due to the short period of a cycle. Finally, a whole master-slave system is set up with CHAI3D toolkit, which consists of Phantom Omni, Computer and Delta device. With a force sensor mounted on the end, the contact force is measured in practice and then filtered with proposed algorithm. The final result shows that the estimated curve followed measured data well and lied at the center of original curve.