CAD-Based Deep Inverse RL for Robot Control Policy Generation
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Solution Overview
Problem
Defining cost functions for Deep Reinforcement Learning algorithms in automation systems is challenging, especially for complex robotic manipulation tasks, requiring significant human intervention and being time-consuming and error-prone, with human demonstrations lacking scalability and posing risks to human operators.
Innovation Solution
Integration of computer-aided design (CAD) models and human expert demonstrations to automate the parameterization of cost functions for Deep Inverse Reinforcement Learning algorithms, reducing human intervention and leveraging CAD-based tools for generating high-quality training data to optimize cost function parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human demonstrations are used to define cost functions, then the robot can learn from expert knowledge, but the process becomes time-consuming and lacks scalability
Solution Approach 1:
The system performs preliminary action by using CAD software to generate ideal trajectories and cost function parameters before robot training. The CAD-based trajectory generation creates high-quality training data in advance, eliminating the need for time-consuming human demonstrations during the actual robot programming phase.
Solution Approach 2:
The system uses CAD software to create digital copies of the ideal trajectories and cost function parameters. Instead of relying on physical human demonstrations, the CAD-based virtual models serve as templates that are copied and used for robot training, significantly reducing time while maintaining quality.
2Reliability
If human demonstrators guide robots through desired motions, then expert knowledge is transferred, but human operators are exposed to hostile field operation environments
Solution Approach 1:
The system introduces CAD software as an intermediary between the expert knowledge source and the robot. Instead of human demonstrators physically guiding robots in hostile environments, the CAD-based trajectory generation acts as a mediator that creates accurate motion paths without exposing humans to physical risks.
Solution Approach 2:
The system replaces the mechanical system of physical human demonstration with a computational system. CAD-based algorithms generate trajectories through mathematical modeling and simulation, substituting the physical presence of human demonstrators with virtual computation, thereby eliminating exposure to harmful field conditions.
3Productivity
If trial-and-error methods are used to find suitable cost functions, then the robot can be trained, but significant human intervention and time are required
Solution Approach 1:
The system enables self-service by allowing the CAD-based algorithm to automatically generate cost function parameters and trajectories without human intervention. The automated parameterization process eliminates the need for engineers to manually tune cost functions through trial and error, significantly improving productivity while reducing complexity.
Solution Approach 2:
The system automatically changes and optimizes cost function parameters through CAD-based computation. Instead of manual parameter tuning, the system dynamically adjusts trajectory parameters, time scaling, and cost weights through algorithmic processes, reducing human intervention while maintaining high productivity.
Data Source
AI summary
Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.