Robot Control Policy Adaptation Using Meta-Imitation and Sparse-Reward Trials
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Solution Overview
Problem
Conventional meta-learning techniques struggle to effectively leverage both human-guided demonstrations and trial-and-error attempts for learning new tasks, often requiring extensive training data and computational resources, and are limited in scaling to broader task distributions.
Innovation Solution
The proposed techniques utilize a meta-learning model that integrates imitation learning and reinforcement learning, allowing a robot agent to learn from a few demonstrations and trial-and-error experiences with sparse reward feedback, enabling efficient few-shot learning of new tasks by sharing parameters between trial and adapted trial policies, and leveraging off-policy data through normalized advantage functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional meta-learning techniques are used to learn new tasks, then the robot can learn from training data, but extensive training data and computational resources are required
Solution Approach 1:
The system performs preliminary action by pre-training the meta-learning model on a diverse set of tasks to learn generalizable policies and representations. This preliminary training enables the model to adapt quickly to new tasks with minimal additional data, resolving the contradiction between learning accuracy and training data volume requirements
Solution Approach 2:
The system changes parameters by dynamically adjusting policy parameters through meta-learning optimization. The model learns to optimize task-specific parameters efficiently using gradient-based methods, achieving high task learning accuracy while requiring minimal training data for adaptation
2Measurement precision
If conventional meta-learning techniques are used to learn new tasks, then the robot can learn from training data, but extensive computational resources are required
Solution Approach 1:
The system performs preliminary computation during the meta-training phase to learn robust policy representations and optimization algorithms. This preliminary computational action enables efficient adaptation to new tasks with minimal computational resources required during actual task execution
Solution Approach 2:
The system optimizes parameter updates using efficient gradient-based methods and second-order optimization techniques. By learning to optimize parameters efficiently during meta-training, the system achieves high task learning accuracy while reducing computational resource consumption during task adaptation
3Adaptability or versatility
If conventional meta-learning techniques are used, then the robot can learn tasks, but scaling to broader task distributions is limited
Solution Approach 1:
The system achieves universality by training the meta-learning model on a diverse distribution of tasks with varying complexities and requirements. The learned policies and representations are universally applicable across different task domains, enabling scaling to broader task distributions while maintaining reliable learning performance
Solution Approach 2:
The system adapts to broader task distributions by learning to optimize task-specific parameters efficiently. The meta-learning framework enables the model to adjust its parameters according to the characteristics of each task, maintaining reliable performance across diverse task distributions
4Quantity of substance
If the robot learns from few demonstrations and trial-and-error attempts, then data efficiency is improved, but the complexity of integrating multiple learning signals increases
Solution Approach 1:
The system merges multiple learning signals (imitation learning from demonstrations and reinforcement learning from trial-and-error) into a unified meta-learning framework. By combining these learning paradigms, the system achieves data efficiency while managing complexity through integrated optimization of multiple objectives
5Productivity
If trial policy and adapted trial policy share parameters, then learning efficiency is improved, but policy stability during training decreases
Solution Approach 1:
The system implements dynamic parameter sharing where the degree of parameter sharing between trial policy and adapted trial policy varies during training. This dynamic approach allows the system to achieve learning efficiency through parameter sharing while maintaining policy stability by adjusting the sharing mechanism based on training progress
Data Source
AI summary
Techniques are disclosed that enable training a meta-learning model, for use in causing a robot to perform a task, using imitation learning as well as reinforcement learning. Some implementations relate to training the meta-learning model using imitation learning based on one or more human guided demonstrations of the task. Additional or alternative implementations relate to training the meta-learning model using reinforcement learning based on trials of the robot attempting to perform the task. Further implementations relate to using the trained meta-learning model to few shot (or one shot) learn a new task based on a human guided demonstration of the new task.