System and Method Suitable for Tuning a Pre-Trained Diffusion Policy of a Robot

A method for controlling an operation of a robot to execute a task, comprising:

• collecting a set of trajectories by executing a pre-trained diffusion policy of the robot;
• receiving a feedback label corresponding to each pair of trajectories of the set of trajectories, wherein the feedback label corresponding to each pair of trajectories indicates a preference to at least one trajectory of the corresponding pair of trajectories;
• learning a reward function based on each pair of trajectories and its corresponding feedback label;
• finetuning the pre-trained diffusion policy based on the reward function, via reinforcement learning;
• and
• controlling, based on the finetuned diffusion policy, the operation of the robot to execute the task.

A non-transitory computer-readable storage medium embodied thereon a program executable by a processor for performing a method for controlling an operation of a robot to execute a task, the method comprising:

• collecting a set of trajectories by executing a pre-trained diffusion policy of the robot;
• receiving a feedback label corresponding to each pair of trajectories of the set of trajectories, wherein the feedback label corresponding to each pair of trajectories indicates a preference to at least one trajectory of the corresponding pair of trajectories;
• learning a reward function based on each pair of trajectories and its corresponding feedback label;
• finetuning the pre-trained diffusion policy based on the reward function, via reinforcement learning;
• and
• controlling, based on the finetuned diffusion policy, the operation of the robot to execute the task.