Robot Action Sequence Prediction for Changing Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic systems struggle to adapt to changing environments and perform complex tasks with varying sequences of actions, as they lack effective models for predicting and executing dynamic action sequences based on visual data.
Innovation Solution
The implementation of an environment-conditioned action sequence prediction model, which utilizes a convolutional neural network and a sequence-to-sequence model to predict a sequence of actions from captured vision data, allowing the robot to adapt to different environmental states and perform tasks with varying action sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot uses traditional programming to perform tasks, then it can execute predetermined actions, but it cannot adapt to changing environmental conditions or varying object positions
Solution Approach 1:
The patent replaces traditional mechanical control systems with a machine learning-based action sequence prediction model. The CNN processes visual input data to extract features, and the RNN predicts action sequences based on these features and environmental conditions, substituting rigid programming with adaptive intelligent processing.
Solution Approach 2:
The system changes parameters by using a trained prediction model that takes visual data parameters (object positions, environmental states) as input and outputs optimized action sequences. The model adapts its predictions based on varying input parameters representing different environmental conditions.
2Productivity
If a robot uses a simple control system, then it has low computational complexity, but it cannot predict and execute complex sequences of actions required for sophisticated tasks
Solution Approach 1:
The control system is segmented into distinct functional components: a CNN module for visual feature extraction and an RNN module for action sequence prediction. This segmentation allows each component to specialize in specific tasks, improving overall productivity while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary action by using the CNN to pre-process visual data and extract relevant features before passing them to the RNN. This preliminary processing step enables the RNN to focus on sequence prediction, improving task completion efficiency without requiring the entire system to handle all processing simultaneously.
3Adaptability or versatility
If a robot encounters different environmental states (e.g., open/closed door, varying object positions), then it must generate different action sequences, but traditional programming cannot dynamically adjust to these variations
Solution Approach 1:
The prediction model performs self-service by automatically processing visual input data and generating appropriate action sequences without external intervention. The trained model directly maps environmental states to action sequences, enabling rapid adaptation to variations without requiring manual reprogramming or complex decision-making processes.
Solution Approach 2:
The system uses feedback by continuously processing visual data about the current environmental state and adjusting action sequence predictions accordingly. The RNN incorporates historical state information and current visual input to dynamically generate appropriate actions, enabling rapid response to environmental variations.
Data Source
AI summary
Training and/or using a machine learning model for performing robotic tasks is disclosed herein. In many implementations, an environment-conditioned action sequence prediction model is used to determine a set of actions as well as a corresponding particular order for the actions for the robot to perform to complete the task. In many implementations, each action in the set of actions has a corresponding action network used to control the robot in performing the action.