Trajectory optimization using neural networks

A system comprising:

• one or more computer-readable storage media;
• a processing system operatively coupled to the one or more computer-readable storage media;
• and
• program instructions, stored on the one or more computer-readable storage media, wherein the program instructions, when read and executed by the processing system, direct the processing system to:
• identify a task for a robotic arm, wherein the task comprises moving an object from a starting point to an end point with the robotic arm and includes a plurality of requirements and information related to the robotic arm, wherein the information related to the robotic arm includes at least geometric information of the robotic arm and a gripper of the robotic arm;
• compute a plurality of trajectories for the robotic arm to move the object from the starting point to the end point based on the task using a non-convex optimizer;
• and
• train a machine learning model using the plurality of trajectories, wherein training the machine learning model comprises refining the machine learning model with imitation loss and trajectory constraint costs until the plurality of requirements are satisfied and the machine learning model predicts an optimal trajectory for moving the object from the starting point to the end point, and wherein training the machine learning model further comprises training the machine learning model using reinforcement learning.

One or more computer-readable storage media having program instructions stored thereon to train a machine learning model to predict optimal trajectories, wherein the program instructions, when read and executed by a processing system, direct the processing system to at least:

• identify a task for a robotic arm, wherein the task comprises moving an object from a starting point to an end point with the robotic arm and includes a plurality of requirements and information related to the robotic arm, wherein the information related to the robotic arm includes at least geometric information of the robotic arm and a gripper of the robotic arm;
• compute a plurality of trajectories for the robotic arm to move the object from the starting point to the end point based on the task using a non-convex optimizer;
• and
• train a machine learning model using the plurality of trajectories, wherein training the machine learning model comprises refining the machine learning model with imitation loss and trajectory constraint costs until the plurality of requirements are satisfied and the machine learning model predicts an optimal trajectory for moving the object from the starting point to the end point, and wherein training the machine learning model further comprises training the machine learning model using reinforcement learning.