Robot Demonstration Learning Using Simulated Workcell Data
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Solution Overview
Problem
Traditional robotic control methods, such as reinforcement learning, are computationally expensive, difficult to scale, and brittle, leading to inefficiencies and incompatibility across different workcells and robot models.
Innovation Solution
A demonstration-based learning approach using local demonstration data to generate a customized control policy for robots, combining reinforcement learning with perceptual data processing and impedance/admittance control, allowing rapid adaptation to specific robot models and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional reinforcement learning is used for robotic control, then robots can learn tasks autonomously, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The patent pre-computes value functions and policies offline before deployment. By performing the computationally intensive reinforcement learning training in advance and storing the resulting policies, the system eliminates the need for real-time computational during robot execution, thus resolving the contradiction between autonomous learning capability and training time consumption.
2Manufacturing precision
If manually programmed schedules are created for one workcell, then precise control is achieved, but the schedule cannot be used for other workcells with different robots or dimensions
Solution Approach 1:
The patent creates policies that are universal across different workcells by training them on diverse environments during the offline phase. The pre-computed policies can be deployed to multiple workcells with different robots and dimensions without reprogramming, achieving both precision and adaptability through the multi-functional nature of the learned policies.
Solution Approach 2:
The patent adapts policies to different workcells by adjusting parameters such as robot dynamics models and environmental constraints during offline training. This allows the same policy framework to accommodate varying workcell configurations while maintaining control precision through parameter optimization rather than complete reprogramming.
3Adaptability or versatility
If traditional reinforcement learning is used, then robots can adapt to tasks, but the models become brittle and unusable with tiny changes to the task, robot, or environment
Solution Approach 1:
The patent performs preliminary training on a wide variety of tasks, robots, and environmental conditions during the offline phase. By exposing the learning algorithm to diverse scenarios in advance, the resulting policies become more robust and less brittle when deployed, as they have already learned to handle variations without requiring retraining.
Solution Approach 2:
The patent cushions against brittleness by pre-training policies to anticipate and handle potential variations in tasks and environments. This beforehand preparation creates a buffer that protects the model from becoming unusable with small changes, as the policies have already been exposed to similar variations during training.
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.