Robot Arm Motion Planning With Curriculum-Based Deep Reinforcement Learning
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Solution Overview
Problem
Existing robot arm motion planning techniques using deep reinforcement learning suffer from instability and catastrophic forgetting due to the lack of understanding of cause-and-effect relationships between target point difficulties, leading to decreased precision and performance.
Innovation Solution
Implement curriculum-based deep reinforcement learning by generating units of curriculum (UoC) with similar difficulty levels, using features like processing time, distance, and delta in joint angles, and applying experience replay and buffer flushing to maintain learning stability and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If curriculum learning is applied to robot arm motion planning, then learning stability is improved, but catastrophic forgetting occurs where previously learned information is lost
Solution Approach 1:
The patent segments the curriculum learning process into distinct units of curriculum (UoC), where each UoC contains target points with similar difficulty levels. This segmentation allows the system to systematically progress through difficulty levels while maintaining stable learning, as each segment builds upon previous segments without causing catastrophic forgetting of previously learned patterns.
Solution Approach 2:
The patent applies preliminary action by pre-defining multiple units of curriculum with progressively increasing difficulty levels before the learning process begins. Each UoC is prepared in advance with target points that have similar characteristics (distance, processing time, delta), allowing the reinforcement learning agent to systematically advance through predetermined learning stages without losing previously acquired knowledge.
2Adaptability or versatility
If target points with various difficulty levels are randomly generated, then the robot arm can reach multiple target points, but learning instability occurs
Solution Approach 1:
The patent applies local quality by creating heterogeneous target points with different difficulty characteristics (distance from start, processing time, delta) within the workspace. Each target point has specific local properties that define its difficulty level, allowing the robot arm to learn diverse motion patterns while maintaining overall learning stability through the structured curriculum organization.
Solution Approach 2:
The patent segments the diverse target points into distinct units of curriculum based on their difficulty characteristics. By grouping target points with similar properties into separate UoCs and processing them in a defined sequence, the system maintains learning stability while still achieving the ability to reach multiple different target points across all curriculum units.
3Adaptability or versatility
If deep reinforcement learning is performed without curriculum structure, then learning flexibility is maintained, but precision in motion planning decreases
Solution Approach 1:
The patent applies dynamics by making the curriculum structure adaptive rather than rigid. While units of curriculum are predefined with specific difficulty levels, the system dynamically adjusts the learning process by allowing transitions between UoCs based on learning progress and performance metrics. This dynamic approach maintains learning flexibility while the structured progression through difficulty levels ensures improved precision in motion planning.
Data Source
AI summary
A method of simulating robot arm motion planning to which curriculum-based deep reinforcement learning applies may be provided. The method according to an embodiment of the present disclosure may include: generating multiple features for each target point within a robot arm's work area; deciding on a selected feature based on correlations between the multiple features; determining a level of difficulty of the each target point based on the selected feature; determining a unit of curriculum (UoC), which is a group of the targets points, for which deep reinforcement learning is to be performed in the same episode, based on the determined level of difficulty; and performing sequentially the deep reinforcement learning for each of the units of curriculum.