Robotic Skill Learning With Imitation Pre-Training and Stable RL
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Solution Overview
Problem
Reinforcement learning (RL) methods for robotic control face challenges with exploration and stable learning, especially in large and continuous action spaces, requiring significant computational resources and time to achieve effectiveness, while imitation learning (IL) suffers from distributional shifts and limited robustness. Techniques combining RL and IL, such as QT-Opt and AWAC, can lead to over-optimistic initializations and catastrophic forgetting.
Innovation Solution
The proposed techniques pre-train an actor network and a critic network using offline positive-only demonstration data, followed by further training with online robotic episodes, employing advantage-weighted regression and Q-learning methods to mitigate forgetting and reduce training steps, allowing the actor network to be used independently for low-latency control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning methods are used for robotic control in large and continuous action spaces, then the robot can learn optimal control policies through autonomous trial-and-error, but the training requires hundreds of thousands to millions of steps, consuming significant computational resources and time
Solution Approach 1:
The patent applies preliminary action by pre-training the actor network using imitation learning from demonstration data before performing reinforcement learning. This preliminary pre-training phase initializes the policy with useful prior knowledge, allowing the subsequent RL training to start from a better baseline and converge faster, thereby reducing the total training time and computational resources required.
2Reliability
If reinforcement learning methods are used for robotic control in large and continuous action spaces, then the robot can learn optimal control policies, but the exploration process introduces significant challenges and requires significant computational resources
Solution Approach 1:
The patent applies preliminary action by pre-training the actor network using imitation learning from demonstration data before performing reinforcement learning. This preliminary pre-training phase initializes the policy with useful prior knowledge, allowing the subsequent RL training to start from a better baseline and converge faster, thereby reducing the total training time and computational resources required.
Solution Approach 2:
The patent uses an advantage function as an intermediary component that guides the exploration process during RL training. The advantage function computes the difference between actual rewards and expected rewards, providing a more informative signal that directs the policy optimization more efficiently, thereby reducing the computational resources needed for exploration compared to standard RL methods.
3Stability of the object's composition
If imitation learning is used for robotic control, then the learning process is more stable compared to reinforcement learning, but the policy suffers from distributional shifts and performs poorly when deployed in the real world
Solution Approach 1:
The patent merges imitation learning and reinforcement learning into a unified two-stage training framework. The first stage uses IL for stable pre-training with demonstration data, and the second stage uses RL for fine-tuning with real-world interaction data. This combination allows the system to benefit from both the stability of IL during pre-training and the adaptability of RL for real-world deployment, resolving the distributional shift problem.
Data Source
AI summary
Utilizing an initial set of offline positive-only robotic demonstration data for pre-training an actor network and a critic network for robotic control, followed by further training of the networks based on online robotic episodes that utilize the network(s). Implementations enable the actor network to be effectively pre-trained, while mitigating occurrences of and/or the extent of forgetting when further trained based on episode data. Implementations additionally or alternatively enable the actor network to be trained to a given degree of effectiveness in fewer training steps. In various implementations, one or more adaptation techniques are utilized in performing the robotic episodes and/or in performing the robotic training. The adaptation techniques can each, individually, result in one or more corresponding advantages and, when used in any combination, the corresponding advantages can accumulate. The adaptation techniques include Positive Sample Filtering, Adaptive Exploration, Using Max Q Values, and Using the Actor in CEM.