Robot Imitation Learning With Task Sequencing for Long-Horizon Manipulation
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Solution Overview
Problem
Existing imitation learning methods in robotics struggle with performing complex and long-horizon object manipulation tasks without human intervention, as they require explicit robot states and actions, and fail to accommodate skill learning from video demonstrations effectively.
Innovation Solution
A decoupled architecture for imitation learning is proposed, comprising a high-level Task Sequencing Network (TSNet) that segments primitive actions from video demonstrations and a low-level Task Agnostic DMP Library (TADL) that executes these primitives sequentially on a robotic arm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If traditional imitation learning methods (Learning from Demonstration) are used to map robot states to actions, then the robot can perform short-horizon manipulation tasks, but it fails to handle long-horizon tasks and cannot learn from video demonstrations
Solution Approach 1:
The patent segments long-horizon tasks into multiple short-horizon sub-tasks or primitives. The system breaks down complex tasks into manageable components that can be learned and executed separately, then composed to form the complete task sequence. This allows the robot to handle long-horizon tasks by chaining together multiple short-horizon skill executions.
Solution Approach 2:
The patent introduces an intermediary module that translates video demonstrations into robot-executable task sequences. This intermediary layer processes visual information from video demonstrations and converts it into a format that the robot's motion planning and execution systems can understand and act upon, enabling learning from video without direct state-action mapping.
2Adaptability or versatility
If end-to-end visual imitation learning is used to map image space directly to robot actions, then the robot can learn from video demonstrations, but the efficiency and task customization scope are reduced
Solution Approach 1:
The patent segments the learning process into distinct modules: video processing module, task sequence prediction module, and motion execution module. This segmentation allows each module to be optimized independently and trained more efficiently than a monolithic end-to-end system, while maintaining the ability to learn from video demonstrations.
Solution Approach 2:
The patent employs dynamic task sequencing that adapts to different task requirements. The system can dynamically adjust the sequence and selection of sub-tasks based on the specific task at hand, enabling flexible task customization without requiring complete retraining of the system, thus improving both efficiency and adaptability.
3Adaptability or versatility
If DMPs are used for kinesthetic demonstration learning, then the robot can reproduce trajectories with different start and goal positions, but the approach fails for long-horizon tasks
Solution Approach 1:
The patent segments long-horizon tasks into sequences of short-horizon DMP executions. Each DMP handles a specific sub-task or motion primitive, and multiple DMPs are chained together to accomplish the complete long-horizon task. This maintains the advantages of DMPs for trajectory reproduction while extending capability to long-horizon tasks through composition.
Solution Approach 2:
The patent performs preliminary decomposition of long-horizon tasks into sub-tasks before execution. By pre-segmenting the task sequence and identifying appropriate DMPs for each segment, the system prepares the execution plan in advance, allowing efficient runtime execution of complex tasks through coordinated DMP sequences.
Data Source
AI summary
The embodiments of present disclosure herein address unresolved problem of key capabilities that an intelligent robot must possess to perform complex and long-horizon object manipulation tasks without human intervention. The present disclosure overcomes above mentioned challenges by decoupling imitation learning task into two parts using a decouple neural network architecture comprising two modules. A high-level module that segments out sequence of primitive actions from video of human demonstration of a complex task and a low-level module that executes those primitives sequentially on robot. The high-level module, Task Sequencing Network (TSNet), is a hybrid neural network composed of Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) with Connectionist Temporal Classification (CTC) loss, while low-level controller is built using dynamic movement primitives (DMP). The network is trained in a supervised learning setting. The CNN, RNN with CTC combination helps in efficiently segmenting task primitives in sequence for given input video demonstrations.