Autonomous Robot Assembly via Reinforcement Learning Primitives
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Solution Overview
Problem
Conventional methods for deploying autonomous robots in AI-based object handling and assembly require extensive manual programming and customization, are costly, and struggle with adapting to changing environments and complex tasks due to high dimensionality and sparse reward structures in reinforcement learning.
Innovation Solution
The use of reinforcement learning (RL) action primitives allows autonomous robots to learn simple robotic actions, combine them into policies, and decompose complex assembly tasks into sequences of these primitives, enabling efficient and adaptable construction of complex assemblies without extensive manual programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual programming methods are used for robotic assembly, then the robot can perform assembly tasks with high reliability, but the system requires extensive manual programming and customization which increases device complexity and reduces adaptability
Solution Approach 1:
The robot performs self-programming through reinforcement learning, automatically generating assembly policies without extensive manual programming. The system learns optimal assembly strategies through trial and error in simulated environments, then transfers this learned behavior to physical execution, eliminating the need for complex manual programming while maintaining reliable assembly task completion.
2Adaptability or versatility
If reinforcement learning is applied to complex assembly tasks, then adaptability to changing environments improves, but learning failures increase due to high dimensionality and sparse reward structures
Solution Approach 1:
The complex assembly task is segmented into hierarchical levels: high-level assembly policies that determine overall assembly strategy, and low-level action primitives that execute specific robotic movements. This segmentation breaks down the high-dimensional state space into manageable components, allowing the robot to learn each level separately and combine them effectively, thereby improving learning success rate while maintaining environmental adaptability.
Solution Approach 2:
The robot undergoes preliminary training in simulated environments before deploying to physical tasks. The simulation phase allows extensive exploration and learning of assembly policies without real-world consequences, pre-training the robot on a wide variety of scenarios. This preliminary action in simulation reduces learning failures when transferring to physical execution, while the learned policies maintain high adaptability to varying environments.
3Extent of automation
If end-to-end reinforcement learning is used for assembly tasks, then automation extent increases, but the high dimensionality of state-action space causes learning failures
Solution Approach 1:
The end-to-end automation is achieved through segmentation of the learning process into distinct hierarchical levels. The high-level policy network learns assembly planning from demonstrations, while separate low-level networks learn specific manipulation skills. This segmentation reduces the effective dimensionality at each learning stage compared to a monolithic end-to-end approach, enabling successful training while maintaining complete automation from perception to execution.
Data Source
AI summary
Methods and apparatus for complex assembly via autonomous robots using reinforcement learning action primitives are disclosed. An example apparatus includes a construction manager and a movement manager. The construction manager is to determine sequences of reinforcement learning (RL) action primitives based on object location goals and associated assembly goals determined for respective ones of objects depicted in an imaged assembly of objects. The movement manager is to command a robot to construct a physical assembly of objects based on the sequences of RL action primitives. The physical assembly of objects is to correspond to the imaged assembly of objects.