Differentiable Trajectory

Differentiable trajectory optimization focuses on developing methods to efficiently compute gradients of trajectories with respect to various parameters, enabling end-to-end learning and optimization of robot control policies. Current research emphasizes integrating differentiable dynamics simulators and trajectory optimization algorithms within neural networks, often employing techniques like differential dynamic programming or implicit function theorem-based approaches. This allows for learning optimal control strategies, handling constraints, and improving robustness in tasks ranging from robotic manipulation and autonomous driving to soft robotics control. The resulting advancements promise more efficient and adaptable control systems for a wide range of applications.