Multimodal Action Output Using Event-Vision Prompt Chaining
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Solution Overview
Problem
Existing techniques for processing vision data to generate output for task replication, such as demonstration-based imitation learning, often fail to accurately translate tasks due to limitations in visual modalities, particularly in environments requiring precise force application, leading to ineffective or unreliable task replication.
Innovation Solution
A chain-of-modality approach that processes multiple modalities of data, including vision and non-visual event data like electromyography, to generate comprehensive and accurate output, by iteratively generating and processing subsets of data using a vision-language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a single prompt including both vision data and event data is processed using a generative model, then the model can potentially generate comprehensive output, but the model fails to appropriately take into account all or portions of event data and fails to synchronize timing of events to corresponding vision data
Solution Approach 1:
The patent divides the processing of multiple modalities into separate stages: first processing event data to generate event data output, then processing vision data with event content to generate vision data output. This segmentation allows each modality to be processed independently with appropriate attention, preventing the generative model from losing event data information while maintaining reliable task replication.
2Reliability
If visual modalities alone are used to capture human actions, then the system is simpler to implement, but the translation of tasks to output is ineffective and fails to enable robust replication
Solution Approach 1:
The patent segments the data processing into distinct components: event data processing generates event data output describing non-visual events, which then serves as input content for vision data processing. This segmented approach enables reliable task replication by ensuring event data is properly considered, while managing system complexity through structured multi-stage processing rather than attempting to process all data simultaneously.
3Loss of information
If multiple modalities of data are processed together in a single prompt, then comprehensive information can be captured, but the generative model cannot synchronize the timing of event data events to corresponding vision data
Solution Approach 1:
The patent segments the processing into two distinct stages: first processing event data to capture timing information and generate event data output, then processing vision data with the event content to generate synchronized vision data output. This segmentation preserves timing synchronization information that would be lost in simultaneous processing, while the structured two-stage framework manages complexity by breaking down the synchronization challenge into manageable steps.
Data Source
AI summary
Generating output that is based on a sequence of actions performed by an entity and related to object(s) in an environment—and generating the output using both vision data that visually captures the entity performing the sequence of actions and event data that captures non-visually detected events that occurred in the environment during performance of the sequence of actions. Implementations utilize chain-of-modality techniques to process multiple modalities of data, each capturing corresponding aspects of the entity performing the sequence of actions, to generate output that reflects the sequence of actions. As opposed to incorporating all of multiple modalities of data in a single prompt, implementations of the chain-of-modality techniques generate and process multiple prompts in sequence, where each prompt includes only a subset of the multiple modalities of data and, when preceded by prior processing of a prior prompt, at least some of the output from the prior processing.