Robot Perception Programming via Imitation Learning for Reusable Skills
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Solution Overview
Problem
Current methods for programming robot perception skills require human experts, leading to high costs and inefficiencies due to the need for custom algorithms and sensor compatibility issues, limiting the reusability and scalability of robot programming.
Innovation Solution
An automated system for robot perception programming through imitation learning, using a probabilistic generative model simulator and demonstrator sensors to generate source code for robots, allowing non-specialized users to demonstrate tasks and create adaptable perception skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts develop custom algorithms for robot perception skills, then the robot can perform specific tasks with high precision, but the cost and time required for programming increases significantly
Solution Approach 1:
The system captures human demonstration data (videos, sensor readings, actions) and creates a digital model that the robot can replicate. Instead of programming the robot with explicit algorithms, the robot learns by copying human behavior patterns from demonstration data, significantly reducing programming time while maintaining task accuracy
Solution Approach 2:
The patent replaces the manual algorithm development process (mechanical programming work) with an automated machine learning system. The system automatically processes demonstration data, extracts features, and generates perception models without human intervention in the actual code writing, eliminating the time-consuming manual programming phase
2Reliability
If human experts create custom perception algorithms for each robot task, then the robot achieves task-specific optimization, but the complexity of the programming system increases
Solution Approach 1:
The system employs a universal perception skill framework that can handle multiple different tasks through the same underlying architecture. The perception skills are designed to be task-agnostic and can be applied across various robot operations, reducing the need for task-specific custom algorithms and simplifying the overall programming system
Solution Approach 2:
The system enables robots to automatically acquire perception skills through self-supervised learning from demonstration data without requiring expert programmers. The automated pipeline processes raw demonstration data, extracts relevant features, and generates ready-to-use perception models, making the system self-sufficient and reducing programming complexity
3Manufacturing precision
If specialized programming knowledge is required for robot perception skills, then the system maintains high control and precision, but the ease of operation decreases for non-specialized users
Solution Approach 1:
The system allows non-specialized users to program robot perception skills simply by demonstrating the desired task behavior. The user's natural actions and observations are captured and automatically converted into executable perception skills, eliminating the need for programming knowledge while maintaining high skill quality through automated feature extraction and model generation
4Measurement precision
If custom algorithms are developed for each robot perception task, then the task accuracy is maximized, but the scalability and reusability of the programming decreases
Solution Approach 1:
The perception skills are designed with a universal architecture that can be applied across multiple tasks and robot configurations. The same perception skill framework can handle different object recognition, navigation, and manipulation tasks, enabling reusability and scalability without sacrificing task-specific accuracy through its flexible adaptation capabilities
Solution Approach 2:
The system employs dynamic and adaptable perception skills that can adjust to different tasks and environments. The perception models are designed to be flexible and can be fine-tuned for specific tasks while maintaining a core reusable framework, enabling both high task accuracy and broad applicability across different scenarios
Data Source
AI summary
Apparatus, systems, methods, and articles of manufacture for automatic robot perception programming by imitation learning are disclosed. An example apparatus includes a percept mapper to identify a first percept and a second percept from data gathered from a demonstration of a task and an entropy encoder to calculate a first saliency of the first percept and a second saliency of the second percept. The example apparatus also includes a trajectory mapper to map a trajectory based on the first percept and the second percept, the first percept skewed based on the first saliency, the second percept skewed based on the second saliency. In addition, the example apparatus includes a probabilistic encoder to determine a plurality of variations of the trajectory and create a collection of trajectories including the trajectory and the variations of the trajectory. The example apparatus also includes an assemble network to imitate an action based on a first simulated signal from a first neural network of a first modality and a second simulated signal from a second neural network of a second modality, the action representative of a perceptual skill.