Multi-Robot Action Primitives for Scalable Collaborative Control
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Solution Overview
Problem
Traditional control system modeling-based approaches are challenging to use in developing collaborative robotics systems, requiring extensive mathematical and control system modeling, manual programming, and are not adaptable or scalable for complex collaborative tasks.
Innovation Solution
The use of reinforcement learning (RL) techniques with a primitives-based approach, where complex collaborative robotic tasks are broken down into pre-learned action and interaction primitives, stored in a library, and then sequenced by a coordination algorithm to achieve complex tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control system modeling-based approaches are used, then robots can complete complex tasks, but the system requires extensive mathematical and control system modeling, manual programming, and is not adaptable or scalable
Solution Approach 1:
The patent segments complex robotic tasks into discrete action primitives (e.g., move, grasp, release, push) and interaction primitives (e.g., handoff, collision avoidance). This segmentation transforms continuous control problems into discrete, learnable units that can be independently trained and recombined, eliminating the need for extensive manual control system modeling while maintaining task complexity capability
Solution Approach 2:
The patent pre-learns and stores action primitives and interaction primitives in a library before executing complex collaborative tasks. These primitives are trained independently using reinforcement learning and then sequenced by a coordination algorithm, allowing the system to adapt to new tasks without重新进行 extensive modeling or manual programming
2Productivity
If traditional control system modeling-based approaches are used, then robots can complete complex tasks, but the system requires extensive manual programming and is not scalable
Solution Approach 1:
By segmenting tasks into reusable action primitives, the system enables scalability through composition. Once primitives are learned, they can be combined to solve increasingly complex tasks without proportionally increasing programming effort, allowing the system to scale from simple to complex collaborative tasks using the same primitive library
Solution Approach 2:
The patent introduces a coordination algorithm as an intermediary that automatically sequences action primitives and interaction primitives based on task goals. This intermediary eliminates the need for manual programming of coordination logic, allowing the system to scale to multi-robot collaborative tasks without proportional increases in programming complexity
3Adaptability or versatility
If reinforcement learning with primitives-based approach is used, then the system becomes adaptable and scalable, but the state-action space must be reduced to make learning tractable
Solution Approach 1:
Segmenting the state-action space into separate action primitives and interaction primitives reduces the complexity of learning. Each primitive learns from a simplified state space relevant to its specific function, rather than the full joint state space of all robots and tasks, making reinforcement learning tractable while maintaining adaptability through primitive composition
Solution Approach 2:
By pre-learning action primitives and interaction primitives separately before combining them, the system reduces the effective state-action space at each learning stage. This preliminary decomposition makes reinforcement learning feasible by breaking down the intractable full-state learning problem into manageable sub-problems
Data Source
AI summary
Various aspects of methods, systems, and use cases include techniques for training or using a model to control a robot. A method may include identifying a set of action primitives applicable to a set of robots, receiving information corresponding to a task (e.g., a collaborative task), and determining at least one action primitive based on the received information. The method may include training a model to control operations of at least one robot of the set of robots using the received information and the at least one action primitive.