Robot Skill Templates for Faster Demonstration Learning
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Solution Overview
Problem
Traditional robotic control methods, such as reinforcement learning, face challenges like high computational costs, difficulty in scaling, brittleness, and the need for extensive manual programming, making them inefficient and error-prone for complex robotic tasks.
Innovation Solution
The development of a skill template distribution system that allows for the distribution of skill templates, enabling robots to be programmed using customized control policies learned from demonstration data, which adapts to specific robot models and environments with high precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is used for robotic control, then robots can learn tasks autonomously, but the computational cost becomes extremely high and the process is time-consuming
Solution Approach 1:
The patent pre-processes demonstration data offline to extract skill templates and transition models before actual robot execution. By performing data processing and model training in advance using recorded human demonstrations, the system avoids time-consuming reinforcement learning during actual robot operation, thus reducing training time while maintaining autonomous task completion capability
Solution Approach 2:
The patent creates simplified copies of complex robotic tasks by extracting skill templates from demonstration data. Instead of using full reinforcement learning models, the system generates condensed skill representations that capture essential task patterns, allowing robots to learn tasks efficiently by copying these pre-processed skill templates rather than learning from scratch through expensive reinforcement learning
2Adaptability or versatility
If reinforcement learning is used for robotic control, then robots can adapt to tasks, but the computational complexity and cost become prohibitively high
Solution Approach 1:
The patent segments complex robotic tasks into smaller, manageable skill templates with distinct transition models. By dividing tasks into discrete skill units (e.g., grasp, move, place, insert), the system reduces computational complexity while maintaining adaptability. Each skill template can be independently processed and combined, avoiding the need for computationally expensive full-task reinforcement learning models
Solution Approach 2:
The patent transforms the problem from continuous reinforcement learning to discrete skill template matching by changing key parameters: using predefined skill categories instead of continuous action spaces, and using transition probability models instead of complex reward functions. This parameter transformation significantly reduces computational complexity while preserving task adaptation capability
3Productivity
If traditional robotic control methods are used, then robots can perform tasks, but extensive manual programming is required which is tedious and error-prone
Solution Approach 1:
The patent enables robots to self-program by automatically learning skill templates from demonstration data without requiring manual programming. The system processes recorded demonstrations, extracts skill patterns, and generates executable task plans autonomously, eliminating the need for tedious manual programming while maintaining reliable task execution
Solution Approach 2:
The patent introduces skill templates as an intermediary layer between demonstration data and robot execution. Instead of direct manual programming or raw reinforcement learning, the system uses skill templates as a middle representation that captures task essence, making the system easier to operate while preserving task execution capability
4Reliability
If reinforcement learning models are trained for specific tasks, then robots can perform those tasks, but the models become brittle and unusable with even tiny changes to the task or environment
Solution Approach 1:
The patent creates universal skill templates that can be applied across multiple tasks and environments. By extracting general skill patterns (grasp, move, place, insert) that are applicable to various objects and contexts, the system achieves both reliability for specific tasks and adaptability to changes. The same skill template can serve multiple functions across different task variations
Solution Approach 2:
The patent makes the skill template system dynamic by allowing flexible combination and sequencing of skill templates based on task requirements. The transition models can adapt to different task contexts while maintaining core skill integrity, enabling the system to remain reliable for known tasks while adapting to new situations through recombination of existing skills rather than requiring complete retraining
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing skill templates for robotic demonstration learning. One of the methods includes receiving from the user device by a skill template distribution system, a selection of an available skill template. The skill template distribution system provides a skill template, wherein the skill template comprises information representing a state machine of one or more tasks, and wherein the skill template specifies which of the one or more tasks are demonstration subtasks requiring local demonstration data. The skill template distribution system trains a machine learning model for the demonstration subtask using a local demonstration data to generate learned parameter values.