Autonomous Robot Training From Captured Motion Data
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Solution Overview
Problem
Current methods struggle to program autonomous robots to exhibit sophisticated, animal-like or human-like behaviors, as they require explicit programming or algorithmic building blocks, making it difficult to replicate complex movements and interactions.
Innovation Solution
An autonomous robot system that uses data captured from a living subject, processed by a machine learning algorithm and neural networks, to simulate movements and behaviors, allowing the robot to adapt and interact spontaneously with its environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If explicit programming or algorithmic building blocks are used to control robot functions and behaviors, then the robot can perform programmed tasks, but it becomes difficult to program sophisticated animal-like or human-like behaviors
Solution Approach 1:
The patent captures movement data from a living subject using motion capture technology and uses this copied data to train the robot. Instead of programming complex behaviors explicitly, the system copies actual human movements and behaviors, which are then processed through machine learning algorithms to generate robot control signals. This approach transfers sophisticated behaviors directly from the subject to the robot without requiring explicit programming of each behavior pattern.
Solution Approach 2:
The patent replaces traditional mechanical programming approaches with a data-driven machine learning system. Rather than using algorithmic building blocks and explicit control logic, the system uses neural networks and predictive analytics to process captured movement data and generate robot commands. This substitution of mechanical/control systems with intelligent data processing enables sophisticated behaviors to emerge naturally from the learned patterns.
2Extent of automation
If a robot is programmed to perform simplistic tasks using programmable controllers, then the robot can execute defined tasks, but it cannot exhibit spontaneous and adaptive actions
Solution Approach 1:
The system performs preliminary action by capturing and storing movement data from a living subject before the robot needs to execute similar behaviors. This pre-captured data serves as training material that the machine learning algorithm processes in advance, enabling the robot to later perform adaptive and spontaneous actions based on this pre-learned knowledge rather than requiring real-time programming decisions.
Solution Approach 2:
The robot achieves a form of self-service through autonomous learning from captured data. The machine learning system automatically processes the movement data, identifies patterns, and generates control signals without human intervention. Once trained, the robot can independently generate spontaneous and adaptive behaviors based on the learned patterns, freeing operators from the need to explicitly program each behavioral response.
3Manufacturing precision
If sensor data from a living subject is captured and processed to generate robot control signals, then the robot can simulate living subject movements, but the system requires complex processing and training
Solution Approach 1:
The patent segments the complex processing task into distinct components: motion capture sensors record specific body part movements, machine learning algorithms process this segmented data to identify movement patterns, and control signal generators translate learned patterns into robot-specific commands. This segmentation of the processing pipeline allows each component to specialize in a specific function, improving overall accuracy while making the complex system more manageable and trainability.
Data Source
AI summary
Using various embodiments, an autonomous robot using data captured from a living subject are disclosed. In one embodiment, an autonomous robot can be trained using previously captured sensor data by receiving the sensor data, processing the sensor data and transmitting control signals to cause a movement in at least one portion of the autonomous robot. The movement can be related to an interaction of the autonomous robot with its surroundings, wherein the movement generates movement data. Thereafter, a predictive analysis can be performed on the movement data to learn a capability of generating actions that are spontaneous and adaptive to an immediate environment of the autonomous robot.