Remote Robot Control Using Learned Manipulation Parameters
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Solution Overview
Problem
Existing robotic systems face inefficiencies when tasked with performing a variety of actions on diverse components, requiring significant engineering effort and computational resources for pre-programming, and often result in idle time due to the need for human guidance, limiting productivity and operational efficiency.
Innovation Solution
The implementation of a user interface that allows remote clients to provide object manipulation parameters, such as grasp poses and trajectories, which are used to train machine learning models to predict optimal manipulation parameters, reducing the need for human input and enhancing robotic efficiency and productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-programming is used to enable robots to perform tasks, then the robot can reliably execute constrained actions repeatedly, but it requires significant engineering effort and computational resources, and cannot adapt to diverse components or new tasks
Solution Approach 1:
The robot system performs self-programming by capturing human demonstration data and automatically generating executable task programs through machine learning models. The robot learns manipulation parameters (grasp poses, trajectories, placement positions) by observing human actions and converting them into robotic control commands, eliminating the need for traditional manual pre-programming while maintaining task reliability
Solution Approach 2:
The patent replaces traditional mechanical pre-programming approaches with data-driven machine learning systems. Instead of manually configuring robotic controllers with constrained motion parameters, the system uses neural networks to learn manipulation policies from human demonstration data, substituting computational learning mechanisms for conventional programming methodologies
2Adaptability or versatility
If human guidance is solicited to assist robot performance of tasks, then the robot can adapt to diverse components and new tasks, but the robot becomes idle while awaiting human guidance, reducing operational efficiency
Solution Approach 1:
The system performs preliminary learning by capturing human demonstration data in advance and training machine learning models offline. Once trained, the robot can autonomously execute learned tasks without requiring real-time human guidance, eliminating idle waiting time while maintaining adaptability to diverse components through the pre-acquired knowledge
Solution Approach 2:
The robot maintains continuous productive operation by executing tasks autonomously based on pre-learned policies. The system eliminates interruptions and idle periods by having the robot continuously perform manipulation actions guided by trained machine learning models, rather than pausing to solicit human guidance during operation
3Adaptability or versatility
If human guidance is always solicited for robot tasks, then the robot can handle new component types, but operator productivity is limited and the system cannot operate more efficiently
Solution Approach 1:
The robot system performs self-learning by automatically processing human demonstration data and generating executable task programs without requiring continuous operator intervention. The machine learning models autonomously extract manipulation parameters and generalize to new component types, freeing operators from repetitive guidance tasks and significantly increasing operator productivity
Solution Approach 2:
The system performs preliminary data capture and model training during setup phases, enabling the robot to independently handle new component types using pre-learned policies. This preliminary learning phase consolidates the adaptability function, allowing operators to focus on higher-value activities while the robot autonomously manages task execution for diverse components
Data Source
AI summary
Utilization of user interface inputs, from remote client devices, in controlling robot(s) in an environment. Implementations relate to generating training instances based on object manipulation parameters, defined by instances of user interface input(s), and training machine learning model(s) to predict the object manipulation parameter(s). Those implementations can subsequently utilize the trained machine learning model(s) to reduce a quantity of instances that input(s) from remote client device(s) are solicited in performing a given set of robotic manipulations and/or to reduce the extent of input(s) from remote client device(s) in performing a given set of robotic operations. Implementations are additionally or alternatively related to mitigating idle time of robot(s) through the utilization of vision data that captures object(s), to be manipulated by a robot, prior to the object(s) being transported to a robot workspace within which the robot can reach and manipulate the object.