Latent-State Inference Control for Dynamic Flexible-Body Tasks
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Solution Overview
Problem
Controlling a flexible body, such as an arm with octopus-like limbs, is challenging due to the difficulty in dynamically enabling predetermined tasks, as conventional research methods focus on position control rather than dynamic task execution and struggle to apply to non-traditional materials like octopus legs.
Innovation Solution
An inference apparatus comprising neural networks that acquire and update latent states from input data to infer and execute time-series tasks, using a combination of fully connected and convolutional layers, and recurrent neural networks to predict and adjust control data for dynamic task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional position control methods are used for flexible bodies, then control simplicity is maintained, but dynamic task execution capability deteriorates
Solution Approach 1:
The patent transforms the control approach by changing the parameter representation from direct position control to latent state representation. The latent state captures essential dynamic characteristics of flexible bodies in a compressed form, enabling both simple control operations and adaptable dynamic task execution through learned state transformations.
Solution Approach 2:
The patent introduces latent states as an intermediary between input data and control outputs. These latent states serve as a mediating representation that simplifies the control interface while preserving dynamic task execution capabilities through learned mappings from raw sensor data to control-relevant features.
2Device complexity
If flexible bodies are modeled with traditional methods, then model simplicity is maintained, but applicability to non-traditional materials deteriorates
Solution Approach 1:
The patent replaces traditional mechanical modeling approaches with data-driven latent state representations. Instead of relying on physics-based models that are difficult to formulate for non-traditional materials like octopus legs, the system learns material-specific dynamics directly from sensor data, enabling universal applicability across diverse flexible materials.
Solution Approach 2:
The patent changes the modeling paradigm from explicit physical parameters to latent state parameters. This transformation allows the model to capture complex material behaviors without requiring detailed physical models, making it applicable to non-traditional materials where traditional mechanical modeling is intractable.
3Measurement precision
If focus is placed on tip position control, then control precision is improved, but dynamic task execution capability deteriorates
Solution Approach 1:
The patent adds a temporal dimension to control by using recurrent neural networks that process sequences of latent states. This transforms static position control into dynamic task execution by capturing temporal patterns and evolving system behavior over time, enabling tasks that require coordinated motion sequences.
Solution Approach 2:
The patent creates a universal control framework where latent states encode multiple task-relevant features simultaneously. Instead of specialized controllers for different tasks, the latent state representation provides a unified interface that can execute various dynamic tasks through learned policies, maintaining precision while achieving versatility.
Data Source
AI summary
An inference apparatus includes one or more memories; and one or more processors. The one or more processors configured to acquire a latent state from input data regarding a control target; acquire a future latent state from the latent state and control data; infer, from the future latent state, a time series of a task to be executed by the control target to be controlled based on the control data; calculate a loss between the time series of the task and data indicating a target state; and update the control data based on the loss.