Hierarchical Robotic Control for Sim-to-Real Skill Selection
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Solution Overview
Problem
Current embodied agent control methodologies struggle to achieve high-latency performance across diverse and dynamic activities due to the limitations of reinforcement learning's sim-to-real gap and the resource-intensive nature of imitation learning, restricting adaptability and robustness in varied operational environments.
Innovation Solution
A hierarchical control framework utilizing high-level control (HLC) machine learning models to process visual representations and select eligible low-level control (LLC) strategies based on skill descriptor metadata, incorporating empirical data and real-time adaptation, enabling precise robotic actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning in simulation is used to train robotic systems, then the agent can explore vast array of behaviors and acquire skills without extensive human-provided examples, but the sim-to-real gap causes policies to not translate effectively to physical robots
Solution Approach 1:
The patent introduces an intermediary adaptation layer that bridges the simulation and real-world domains. This layer includes domain-specific adapters and fine-tuning mechanisms that translate policies from simulation to physical robots, mediating the gap between synthetic training environments and real-world deployment.
Solution Approach 2:
The system dynamically adjusts training parameters, simulation fidelity levels, and domain randomization parameters to optimize policy transfer. By changing parameters such as simulation physics accuracy, sensor noise characteristics, and actuator models, the system adapts policies to bridge the sim-to-real gap effectively.
2Reliability
If imitation learning is used to leverage real-world demonstrations, then the learning process is anchored to proven solutions, but substantial volume of demonstrations (tens of thousands to hundreds of thousands) is required
Solution Approach 1:
The patent segments the demonstration data into task-specific subsets and organizes them in hierarchical structures. By dividing large datasets into manageable, task-relevant segments, the system can efficiently train on only the necessary demonstrations rather than requiring processing of all available data.
Solution Approach 2:
The system uses partial action by selecting only the most relevant demonstrations for each specific task rather than requiring complete datasets. This selective approach allows effective learning with fewer demonstrations by focusing on critical examples that capture essential task characteristics.
3Manufacturing precision
If specialized learning paradigms are used for specific tasks, then high performance can be achieved for those tasks, but adaptability and robustness are restricted in varied operational environments
Solution Approach 1:
The patent implements a universal control framework that can perform multiple functions across diverse tasks and environments. The system uses a unified architecture with task-specific modules that can be dynamically activated, allowing the same base system to adapt to various operational contexts while maintaining specialized performance when needed.
Solution Approach 2:
The system dynamically adjusts its learning paradigm and control strategies based on the operational context. By making the system flexible and adaptive rather than static, it can switch between different learning modes and control approaches to optimize performance for current tasks while maintaining versatility across environments.
Data Source
AI summary
A method for controlling an embodied agent involves retrieving visual representations of an environment in which the agent engages in an activity. The visual representations or data derived therefrom are processed using one or more high-level control (HLC) machine learning models to generate HLC output. Based on this output, a shortlist of two or more eligible low-level control (LLC) strategies is identified from a superset of candidates. Skill descriptor metadata associated with the eligible LLC strategies is analyzed, and one strategy is selected. The visual representations or data derived therefrom are then processed using one or more LLC machine learning models associated with the selected strategy to generate LLC output. A control signal for the embodied agent is generated based on this LLC output. The activity can include racket sports, locomotion, or object manipulation.