Robotic Task Sequencing With Demonstration-Guided Primitive Planning

The patent segments long-horizon tasks into smaller sub-tasks or sequences. Instead of learning entire long sequences at once, the system breaks down complex tasks into manageable chunks that can be learned and reused. This segmentation allows the robot to learn from demonstrations more efficiently without requiring enormous amounts of data, as each segment can be independently learned and then composed to solve novel tasks.

The patent employs preliminary action by pre-segmenting demonstrated tasks into reusable sequences before encountering novel tasks. The system learns from demonstrations in advance, creating a library of sequences that can be later composed to solve new tasks. This preliminary learning phase reduces the data requirements when facing novel tasks, as the system already has pre-learned sequences to work with.

The patent applies segmentation by dividing long-horizon tasks into multiple sequences or sub-tasks. Instead of treating each task as a single monolithic skill, the system breaks them down into smaller, reusable sequence segments. This allows the robot to maintain simpler learning models for each segment while still handling complex sequential tasks through composition of these segments.

The patent introduces dynamics by making the task representation flexible and composable. The system learns sequences that can be dynamically selected and composed based on the specific task requirements. This dynamic composition capability allows the robot to adapt to different sequential tasks using the same set of learned sequences, maintaining simplicity while achieving versatility.

The patent extracts the reward engineering complexity from the overall system by using demonstration-based learning instead of reinforcement learning. Instead of requiring complex reward functions to guide learning, the system extracts sequences directly from demonstrations, which inherently contain the optimal behavior. This extraction approach achieves sequence optimization without the need for complex reward engineering.

The patent uses copying by replicating sequences from demonstrations rather than learning them from scratch through reinforcement learning. The system copies successful sequences from demonstrated tasks and reuses them for novel tasks, achieving optimization accuracy without requiring the prohibitive data and complex reward structures that reinforcement learning would demand.