Multi-Purpose Robot Control With Reusable Work Primitives
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Solution Overview
Problem
Current robots require complex and elaborate tele-operation systems that limit accessibility and cause significant wear on components during training, making them inefficient for semi-autonomous or autonomous operation across multiple work objectives.
Innovation Solution
A robot system equipped with a library of reusable work primitives and processor-executable instructions that allows for autonomous execution of various work objectives by selecting and executing permutations of these primitives, enabling semi-autonomous or autonomous operation across multiple tasks without the need for extensive training on specific workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots are trained by repeatedly performing physical tasks in the real world, then the robot can learn to complete work objectives autonomously, but significant wear and tear occurs on the robot's components
Solution Approach 1:
The patent creates virtual copies of the robot and its environment in a simulated training environment. The robot's sensory inputs, motor commands, and physical interactions are replicated digitally, allowing extensive training to occur on these virtual copies without affecting the physical robot's components. This copying approach enables autonomous operation learning while preserving the physical robot from wear and tear.
2Ease of operation
If tele-operation systems are made elaborate and complicated with sophisticated sensors and equipment, then the robot can be controlled precisely, but accessibility is limited and the pilot must devote full attention
Solution Approach 1:
The patent segments the tele-operation system into modular components: virtual reality interfaces, haptic feedback devices, and workflow management systems. Each component handles specific aspects of control, allowing the pilot to interact with the robot through simplified, intuitive interfaces rather than complex centralized systems. This segmentation maintains control precision while reducing overall system complexity and improving accessibility.
Solution Approach 2:
The system incorporates autonomous workflow capabilities that allow the robot to perform routine decision-making and task execution independently. The pilot only needs to intervene when necessary, rather than maintaining constant attention. The robot serves itself by autonomously completing workflows based on programmed criteria, reducing the cognitive load and complexity of the tele-operation system.
3Ease of operation
If a robot is programmed to operate semi-autonomously or fully autonomously, then accessibility and ease of operation improve, but extensive training is required which causes component wear
Solution Approach 1:
The patent uses virtual copies of the robot in simulated environments to perform extensive training for semi-autonomous operation. These digital replicas undergo repeated training iterations, learning workflows and decision-making processes without any physical wear. Once trained, the knowledge is transferred to the physical robot, enabling semi-autonomous operation without the component degradation that would result from real-world training.
Data Source
AI summary
Systems, devices, and methods for training and operating (semi-)autonomous robots to complete multiple different work objectives are described. A robot control system stores a library of reusable work primitives each corresponding to a respective basic sub-task or sub-action that the robot is operative to autonomously perform. A work objective is analyzed to determine a sequence (i.e., a combination and/or permutation) of reusable work primitives that, when executed by the robot, will complete the work objective. The robot executes the sequence of reusable work primitives to complete the work objective. The reusable work primitives may include one or more reusable grasp primitives that enable(s) a robot's end effector to grasp objects. Simulated instances of real physical robots may be trained in simulated environments to develop control instructions that, once uploaded to the real physical robots, enable such real physical robots to autonomously perform reusable work primitives.