Robot Grasp Primitive Library for Autonomous Multi-Task Operation
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Solution Overview
Problem
Current robots require complex tele-operation systems and significant training, leading to wear and tear before they can autonomously perform tasks, limiting their accessibility and efficiency in completing multiple work objectives.
Innovation Solution
A robot system equipped with a library of reusable work primitives and processor-executable instructions that allows it to autonomously initiate and execute workflows for various work objectives, using a combination of physically actuatable components and sensors to perform tasks like grasping objects, without the need for extensive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots are trained to operate autonomously by repeatedly performing physical tasks in the real world, then the robot can learn to complete work objectives, but significant wear and tear occurs on the robot components before deployment
Solution Approach 1:
The patent creates virtual copies of the robot and its environment in a simulated training environment. The robot learns autonomous operation through repeated tasks in this virtual copy, eliminating physical wear and tear while maintaining training effectiveness. The simulation accurately replicates physics, sensor inputs, and actuator responses to provide realistic training without compromising component durability.
2Ease of operation
If tele-operation systems are used to control robots, then the robot can perform physical actions emulating a human operator, but the interface becomes very elaborate and complicated requiring full operator attention
Solution Approach 1:
The robot performs self-service by autonomously completing work objectives without requiring complex tele-operation interfaces. The robot independently processes sensor data, plans actions, executes workflows, and adapts to environmental changes, eliminating the need for elaborate control systems and full operator attention while maintaining operational effectiveness.
3Adaptability or versatility
If a robot is designed to complete multiple different work objectives, then the robot's versatility increases, but the system complexity increases
Solution Approach 1:
The patent segments complex work objectives into reusable atomic actions that can be independently learned and combined. The robot maintains a library of segmented actions (e.g., grasp, move, place, manipulate) that can be recombined through workflow composition to complete diverse work objectives, reducing system complexity while maintaining versatility.
Solution Approach 2:
The patent implements universality by creating a unified autonomous operation system that handles multiple work objectives through common processing pipelines. The robot uses the same sensor processing, workflow planning, and action execution mechanisms across different tasks, eliminating the need for separate specialized systems and reducing overall complexity while maintaining adaptability.
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.