Skill Template Distribution for Scalable Robotic Demonstration Learning
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Solution Overview
Problem
Traditional robotic control methods, such as reinforcement learning, face challenges with complex, high-dimensional action spaces, sparse rewards, and brittleness, making them computationally expensive and difficult to scale and generalize across different environments and robots.
Innovation Solution
The introduction of a skill template distribution system that uses demonstration-based learning, allowing robots to be programmed with customized control policies learned from demonstration data, incorporating visual, proprioceptive, and haptic data, to adapt rapidly to various robot models and environments, and enabling efficient training by non-experts in a short time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional reinforcement learning is used for robotic control, then robots can learn tasks through trial and error, but the computational cost becomes extremely expensive due to complex high-dimensional action spaces
Solution Approach 1:
The patent segments the complex high-dimensional action space into multiple low-dimensional subspaces, each corresponding to a specific skill or task component. This segmentation allows the robot to learn and plan in simpler, more manageable dimensions, dramatically reducing computational cost while maintaining automation capability.
Solution Approach 2:
The patent transforms the problem from learning in high-dimensional continuous action space to learning in low-dimensional discrete skill spaces. By changing the dimensional representation and using hierarchical decomposition, the system achieves automated learning with significantly reduced computational requirements.
2Adaptability or versatility
If traditional reinforcement learning is used for robotic control, then robots can adapt to tasks, but the process is extremely time-consuming due to sparse rewards and complex evaluation
Solution Approach 1:
The patent pre-defines a library of skills and templates that encapsulate common robotic operations. Instead of learning from scratch through time-consuming trial and error, the system prepares these skill templates in advance, allowing rapid adaptation to new tasks by composing existing skills rather than learning basic movements repeatedly.
Solution Approach 2:
The patent uses demonstration data as copies of expert behavior to guide learning. By copying demonstrated trajectories and using them as priors for skill learning, the system avoids the extremely time-consuming process of learning through sparse rewards, achieving fast adaptation by leveraging existing knowledge copies.
3Manufacturing precision
If traditional reinforcement learning is used for robotic control, then robots can learn optimal policies, but the models become extremely brittle and unusable with tiny changes to task or environment
Solution Approach 1:
The patent creates universal skill templates that can be applied across multiple tasks and environments. These templates are designed to be task-agnostic and environment-independent, allowing the same skill library to serve multiple functions and maintain robustness when faced with variations in task parameters or environmental conditions.
Solution Approach 2:
The patent achieves robustness by learning skills in parameterized form rather than fixed trajectories. The skill templates can adapt to parameter changes in tasks and environments, maintaining control precision through parameter adjustment rather than requiring complete relearning, thus preventing brittleness.
4Manufacturing precision
If manual programming is used for robotic control, then precise control schedules can be generated, but the process is tedious and cannot be reused across different workcells
Solution Approach 1:
The patent creates universal skill templates that can be applied across multiple workcells and tasks. Instead of manually programming each workcell separately, the same template library serves multiple functions and environments, making the system easy to deploy while maintaining precise control through the structured template composition.
5Productivity
If reward shaping is used to mitigate sparse rewards in reinforcement learning, then learning can proceed more effectively, but hand-designed reward functions do not scale well
Solution Approach 1:
The patent extracts the reward design problem from the learning process by using demonstration data to implicitly define successful behavior. Instead of requiring complex hand-designed reward functions, the system extracts skill patterns directly from demonstrations, eliminating the need for intricate reward shaping while maintaining learning efficiency.
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.