Планування на основі навчання

Principles of Motion Planning

The core of the system is based on a learned cost/heuristics for search, allowing it to efficiently explore the solution space.

Furthermore, Model Predictive Path Integral (MPPI) is employed to provide a robust and accurate trajectory prediction.

Finally, Diffusion models for trajectories are utilized to generate smooth and realistic motion plans.

Simulated Robot Learning

The system learns to predict the optimal future action for the simulated robot, given its current state and a learned cost function. This is achieved through a combination of reinforcement learning techniques and model predictive control.

A cost network is trained on demonstrations of desired robot behavior. This network estimates the cost associated with different actions, providing a crucial signal for the MPPI algorithm.

The MPPI algorithm then utilizes this cost network to plan future actions, taking into account the robot's constraints and uncertainties. The learned cost function allows the MPPI algorithm to adapt quickly to new situations and optimize its performance over time.

Finally, the planned actions are executed on the simulated robot, and the results are used to further refine the cost network and improve the overall learning process.

Часті запитання

Спробуйте наживо

Усе, що вище, працює прямо у вашому браузері — відкрийте Inverse Kinematics (FABRIK) і змінюйте параметри під час роботи. Нічого не встановлюється, нічого не завантажується на сервер, уся модель живе в одній вкладці.