Autonomous Robot Mimicking Human Skeleton for Adaptive Behavior
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Solution Overview
Problem
Current methods for programming autonomous robots struggle to replicate sophisticated animal-like or human-like behaviors, as they require explicit programming or algorithmic building blocks, making it difficult to achieve adaptive and spontaneous actions.
Innovation Solution
An autonomous robot system that uses data collection from a living subject, featuring a robotic skeleton designed to mimic human movements, coupled with a processing system that includes sensors and machine learning algorithms to generate control signals for effectors, enabling spontaneous and adaptive behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If explicit programming or algorithmic building blocks are used to program robot functions and behaviors, then the robot can perform predefined tasks, but it becomes difficult to achieve sophisticated animal-like or human-like behaviors that are spontaneous and adaptive
Solution Approach 1:
The patent captures movement data from a living subject and uses it to train a machine learning algorithm, which then generates control signals for the robot. This copying approach allows the robot to acquire sophisticated behaviors directly from biological sources without complex explicit programming, resolving the contradiction between achieving adaptive behavior and maintaining programming simplicity
Solution Approach 2:
The patent replaces traditional mechanical programming approaches with a machine learning-based system. Instead of manually programming algorithmic building blocks, the system uses sensor data from living subjects to train neural networks that automatically generate adaptive behaviors, substituting mechanical programming with intelligent data-driven control
2Adaptability or versatility
If a robotic skeleton is designed similar to that of a human skeleton to simulate similar movements, then the robot can exhibit human-like behaviors, but the device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent captures the movement patterns and behaviors of a living subject and uses machine learning to translate these into robot control signals. This allows the robot to exhibit human-like behaviors by copying biological movement data rather than requiring perfect anatomical replication, reducing manufacturing complexity while maintaining behavioral fidelity
Solution Approach 2:
The patent uses machine learning algorithms to map sensor data from living subjects to robot effector control signals. By changing the control parameters through learned transformations rather than direct mechanical replication, the system achieves human-like behaviors with simplified robotic anatomy, resolving the contradiction between behavioral capability and manufacturing ease
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 is described comprising a robotic skeleton designed similar to that of a human skeleton to simulate similar movements as performed by living subjects. The movements of the robotic skeleton are resultant due to control signals received by effectors present near or on the robotic skeleton. The robot can be configured to receive sensor data transmitted from a sensor apparatus that periodically gathers the sensor data from a living subject. The robot can then process the sensor data to transmit control signals to the effectors to simulate the actions performed by the living subject and perform a predictive analysis to learn the capability of generating spontaneous and adaptive actions, resulting in an autonomous robot that can adapt to its surroundings.