Robotic Demonstration Learning Using Simulated Local Workcell Data
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Solution Overview
Problem
Traditional robotic control methods, such as reinforcement learning, are inefficient and brittle, requiring extensive manual programming, being computationally expensive, and failing to generalize to different environments or robots due to sparse rewards and high-dimensional action spaces.
Innovation Solution
Demonstration-based robotic learning using simulated local demonstration data to generate customized control policies, incorporating visual, proprioceptive, and haptic data for rapid adaptation to specific robot models and environments, allowing for efficient training and deployment across various robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional reinforcement learning is used for robotic control, then the robot can learn tasks autonomously, but the computational cost becomes extremely expensive and training time is excessively long
Solution Approach 1:
The patent applies preliminary action by pre-collecting demonstration data from expert demonstrations before actual training. This demonstration data serves as prior knowledge that guides the reinforcement learning process, allowing the robot to start from a pre-prepared foundation rather than learning from scratch, thus dramatically reducing training time while maintaining autonomous learning capabilities
Solution Approach 2:
The patent introduces demonstration data as an intermediary between expert knowledge and robot learning. This intermediary contains pre-processed task execution information that mediates the transfer of skills from expert demonstrations to the robot policy, bridging the gap between manual expertise and autonomous execution without requiring expensive trial-and-error learning
2Adaptability or versatility
If reinforcement learning is used to handle high-dimensional continuous action spaces, then the robot can perform complex tasks, but the computational complexity increases exponentially
Solution Approach 1:
The patent applies local quality by focusing the learning process on local demonstrations rather than global exploration of the entire action space. The demonstration data provides localized expert knowledge for specific task regions, allowing the robot to learn complex tasks by combining multiple local demonstrations rather than searching the entire high-dimensional space, thus reducing computational complexity while maintaining versatility
Solution Approach 2:
The patent segments the complex task into multiple subtasks through demonstration data collection. Each demonstration corresponds to a specific subtask or skill component, allowing the robot to learn complex tasks by composing simpler segmented skills rather than learning the entire complex task as a monolithic problem, thereby reducing computational burden
3Reliability
If traditional reinforcement learning with hand-designed reward functions is used, then the robot can be guided toward task completion, but the reward shaping is not scalable and requires extensive manual tuning
Solution Approach 1:
The patent applies self-service by automatically extracting reward signals from demonstration data rather than requiring hand-designed reward functions. The system learns task objectives and success criteria directly from demonstrated behaviors, enabling automatic reward formulation that scales to new tasks without extensive manual tuning, while still providing reliable task completion guidance through learned reward structures
4Manufacturing precision
If a robot model is trained using traditional learning techniques, then it can perform specific tasks, but even tiny changes to the task, robot, or environment cause the model to become completely unusable
Solution Approach 1:
The patent applies universality by training the robot policy on diverse demonstration data that covers variations in tasks, robots, and environments. The demonstration-based approach creates a universal policy that can handle multiple scenarios and adapt to changes, making the learned model robust to tiny variations while maintaining precise task execution across different conditions
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using simulated local demonstration data for robotic demonstration learning. One of the methods includes receiving perceptual data of a workcell of a robot to be configured to execute a task according to a skill template, wherein the skill template specifies one or more subtasks required to perform the skill, wherein at least one of the subtasks is a demonstration subtask that relies on learning visual characteristics of the workcell. A virtual model is generated of a portion of the workcell. A training system generates simulated local demonstration data from the virtual model of the portion of the workcell and tunes a base control policy for the demonstration subtask using the simulated local demonstration data generated from the virtual model of the portion of the workcell.