Main structure of the octagonal prism. by Hongjun Meng (5031806)

Subjects:

Schematic illustration of the robot’s walking gait. by Hongjun Meng (5031806)

Subjects:

The error of leg-2. by Hongjun Meng (5031806)

Subjects:

The overall structure of the soft quadruped robot. by Hongjun Meng (5031806)

Subjects:

Control error comparison. by Hongjun Meng (5031806)

Subjects:

Data of leg-1. by Hongjun Meng (5031806)

Subjects:

Air pressure versus bending angle. by Hongjun Meng (5031806)

Subjects:

Unilateral bending, elongation, and diagonal bending of the soft leg. by Hongjun Meng (5031806)

Subjects:

The structure of the single soft robotic leg. by Hongjun Meng (5031806)

Subjects:

Control curves of Leg 1 and Leg 3. by Hongjun Meng (5031806)

Subjects:

The error of leg-1. by Hongjun Meng (5031806)

Subjects:

Data collection for developing the control model for the soft leg. by Hongjun Meng (5031806)

Subjects:

Control process flowchart. by Hongjun Meng (5031806)

Subjects:

Soft leg bending angle. by Hongjun Meng (5031806)

Subjects:

Hardware setup.proportional valve. by Hongjun Meng (5031806)

Subjects:

Air pressure versus elongation. by Hongjun Meng (5031806)

Subjects:

The research framework. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Rosenbrock function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Levy function losses for . by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”