Robotic Task Sequencing With DMPs for Novel Task Generalization
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Solution Overview
Problem
Existing robotic learning methods struggle with efficiently decomposing long-horizon, sequential tasks due to a large search space and data inefficiencies, particularly in reinforcement learning, and lack effective methods for segmenting and optimizing task sequences from human demonstrations.
Innovation Solution
A robotic controller system that utilizes dynamic movement primitives and graph-search based planning to segment and optimize sequences from human demonstrations, enabling efficient learning and execution of novel tasks by identifying appropriate features and generating feasible paths using a pretrained learning module and interface devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is used to solve long-horizon sequential tasks, then a solution can be found, but enormous amount of data is required and reward engineering becomes very difficult
Solution Approach 1:
The patent segments long-horizon sequential tasks into multiple sub-tasks or stages. Each sub-task is learned independently using demonstration data, and then the segments are composed together to form the complete task solution. This segmentation reduces the data requirement compared to learning the entire long-horizon task as a single sequence through reinforcement learning.
Solution Approach 2:
The patent performs preliminary action by pre-learning individual sub-tasks from demonstrations before composing them into the full task sequence. This allows the system to build a library of learned sub-tasks that can be reused and combined, avoiding the need to learn everything from scratch through reinforcement learning with enormous data requirements.
2Reliability
If reinforcement learning is used to solve long-horizon sequential tasks, then a solution can be found, but prohibitive amount of data and complex reward engineering is required
Solution Approach 1:
By segmenting the task into sub-tasks, the patent simplifies the learning problem for each segment. Instead of designing complex reward functions for the entire long-horizon task, the system learns sub-tasks from demonstrations where the goal is inherently defined by the sub-task completion, significantly reducing reward engineering complexity.
Solution Approach 2:
The system uses demonstration data to automatically learn sub-task representations without requiring manual reward function design. The demonstrations provide implicit guidance on what constitutes successful sub-task completion, allowing the system to self-organize the learning process without complex external reward engineering.
3Quantity of substance
If LfD technique is used to learn sequential tasks, then data efficiency is improved, but automatic decomposition of long demonstrations into meaningful sequences is difficult
Solution Approach 1:
The patent automatically segments long demonstration trajectories into meaningful sub-task sequences by analyzing temporal patterns, state transitions, and task structure. This automatic segmentation enables data-efficient learning by creating manageable learning units from lengthy demonstrations without manual intervention.
Solution Approach 2:
The patent replaces manual or heuristic-based sequence decomposition with an automated learning-based approach. The system uses machine learning models to automatically identify meaningful segment boundaries and structure demonstrations into sub-tasks, substituting complex manual decomposition processes with data-driven automatic segmentation.
4Quantity of substance
If LfD technique is used to learn sequential tasks, then data efficiency is improved, but optimization of skill sequence for novel tasks is challenging
Solution Approach 1:
By learning tasks as composable sub-task segments rather than monolithic sequences, the patent enables flexible recombination and optimization of skills for novel tasks. The segmented structure allows the system to select and arrange appropriate sub-tasks from the learned library to efficiently solve new tasks while maintaining data efficiency from the original demonstrations.
Solution Approach 2:
The patent introduces dynamic composition of learned sub-tasks, where the sequence and selection of skills can be optimized based on the specific requirements of novel tasks. This dynamic approach allows the system to adaptively reorganize learned skills rather than being constrained to fixed demonstration sequences, improving generalization while maintaining data efficiency.
Data Source
AI summary
A robotic controller is provided for generating sequences of movement primitives for sequential tasks of a robot having a manipulator. The controller includes at least one control processor, and a memory circuitry storing a dictionary including the movement primitives, a pretrained learning module, and a graph-search based planning module having instructions stored thereon. The controller to perform steps acquiring a planned task provided by an interface device operated by a user, wherein the planned task is represented by an initial state and a goal state with respect to an object, generating a planning graph by searching a feasible path of the object for the novel task using the graph-search based planning module and selecting movement primitives from the dictionary in the pretrained learning module, wherein the pretrained learning module has been trained based on demonstration tasks, parameterizing the feasible path represented by the movement primitives as dynamic movement primitives (DMPs) using the initial state and goal state, and implementing the parameterized feasible path as a trajectory according to the selected movement primitives using the manipulator of the robot by tracking and following the parameterized for the planned task.