Multimodal Robotic Instruction Generation From Video and Text
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Solution Overview
Problem
Existing robotics methods require intricate manual programming, making it difficult to adapt to changing environments or task variations, and are time-consuming, limiting their deployment to repetitive tasks.
Innovation Solution
Utilizing generative artificial intelligence (Gen AI) techniques to generate robotic instructions from video demonstrations, translating them into precise robotic actions through multimodal learning models that integrate video, text, and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual programming is used for robotic tasks, then precision and control are improved, but time consumption and complexity increase
Solution Approach 1:
The system copies human demonstration data (video, text, environmental information) to generate robotic instructions automatically, eliminating the need for manual programming while preserving task accuracy. The generative AI model learns from copied human behavior patterns to produce precise robotic action sequences.
Solution Approach 2:
The patent replaces manual mechanical programming with an automated AI-based system that processes multimedia inputs and generates instructions. This substitution eliminates the time-consuming manual coding process while maintaining instruction precision through advanced pattern recognition and generation capabilities.
2Measurement precision
If manual programming is used for robotic tasks, then task accuracy is improved, but adaptability to environment changes deteriorates
Solution Approach 1:
The system dynamically adapts to environmental changes by processing new video demonstrations and environmental data in real-time. The generative AI model updates its understanding of task requirements and environmental conditions, allowing robotic instructions to adapt to changing conditions while maintaining accuracy through continuous learning from multimedia inputs.
Solution Approach 2:
The system incorporates environmental feedback by analyzing video data and environmental information to adjust generated instructions. This feedback mechanism allows the robotic system to adapt to environmental changes while maintaining task accuracy through iterative refinement based on observed conditions.
3Adaptability or versatility
If complex programming is used for diverse tasks, then task capability is improved, but ease of operation deteriorates
Solution Approach 1:
The system copies human demonstration data (video recordings, text descriptions, environmental observations) to generate robotic instructions, making the system accessible to non-technical users. Instead of requiring users to learn complex programming, they simply need to provide natural demonstrations that the AI model processes and converts into executable robotic instructions.
Solution Approach 2:
The patent replaces complex manual programming operations with an automated AI system that handles task analysis and instruction generation. This substitution maintains diverse task capability while dramatically improving ease of operation, as users interact through simple video recording and text input rather than complex code writing.
4Reliability
If traditional robotics methods are used, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary learning from video demonstrations and environmental data to pre-generate optimized action sequences. This preliminary action allows the robotic system to execute tasks more quickly while maintaining reliability, as the AI model has already processed and validated the instruction sequences before execution.
Solution Approach 2:
The generative AI model enables continuous generation and refinement of robotic instructions based on ongoing video input and environmental feedback. This continuous process maintains execution reliability through constant validation while improving productivity by eliminating idle programming time and enabling faster task deployment.
Data Source
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AI summary
A computer-implemented method to generate robotic instructions is disclosed. The method may include receiving video data demonstrating one or more tasks and text data related to the one or more tasks. Further, the method may include encoding the video data and the text data, wherein the encoding is generated using at least one cross-attentional transformer. The method also includes receiving image data providing environmental data for at least one robotic task. Furthermore, the method may include encoding vision data corresponding to the image data. Consequently, the method may include generating robotic instructions based upon the video data, the text data and the vision data that was encoded.