Robotic Demonstration Learning With Progress-Guided User Feedback
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Solution Overview
Problem
Traditional robotic control methods, such as reinforcement learning, are computationally expensive, error-prone, and difficult to scale due to complex high-dimensional action spaces, sparse rewards, and brittleness, making them inefficient for programming robotic movements in diverse workcell environments.
Innovation Solution
The implementation of demonstration-based learning techniques that utilize visual, proprioceptive, and haptic data to generate customized control policies, allowing for intuitive and robust robotic task execution, even by non-expert users, through skill templates that adapt 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 the robot can learn tasks through trial and error, but the computational cost becomes extremely expensive and the training time is very long
Solution Approach 1:
The patent applies preliminary action by pre-training robot policies in simulation environments before deploying them to physical robots. This allows the robot to learn basic task capabilities in advance through simulated demonstrations, significantly reducing the trial-and-error time needed in the real world. The pre-trained policies are then fine-tuned with actual robot data, combining the benefits of simulation efficiency with real-world accuracy.
2Manufacturing precision
If manual programming is used to dictate robotic movements, then precise control can be achieved, but the programming process becomes tedious and time-consuming
Solution Approach 1:
The patent applies copying by using demonstration data from human operators or other sources to create training datasets that replicate expert behavior. Instead of manually programming each movement, the system copies demonstrated actions and uses them to train robot policies through imitation learning, achieving precise control while eliminating tedious manual programming.
3Productivity
If a manually generated schedule is created for one workcell, then the task can be completed, but the schedule cannot be used for other workcells with different configurations
Solution Approach 1:
The patent applies universality by training robot policies on diverse demonstration data from multiple workcells and task variations. The learned policies are designed to be transferable across different workcell configurations, robot types, and task requirements. This allows a single trained policy to adapt to multiple environments without requiring separate manual programming for each workcell, achieving both efficiency and versatility.
4Adaptability or versatility
If traditional reinforcement learning is used, then the robot can adapt to new tasks, but the approach becomes extremely brittle and fails with tiny changes to the task or environment
Solution Approach 1:
The patent applies feedback by continuously monitoring robot performance and using real-world outcomes to refine and update policies. The system incorporates feedback loops where actual task results are used to adjust and improve robot behavior, making the system more robust to variations. This ongoing feedback mechanism allows the robot to adapt reliably to new tasks while maintaining stability through continuous learning from actual performance data.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing user feedback for robotic demonstration learning. One of the methods includes initiating a local demonstration learning process to collect respective local demonstration data for each of one or more demonstration subtasks defined by a skill template to be executed by a robot. Local demonstration data is repeatedly collected for each of the one or more demonstration subtasks of the skill template while a user manipulates a robot to perform each of the one or more demonstration subtasks defined by the skill template. A respective progress value for each of the one or more demonstration subtasks defined by the skill template is maintained. A user interface presentation is generated that presents a suggested demonstration to be performed by the user based on a respective progress value for each demonstration subtask.