Robotic Task Policy Training Using Engineered Guidance
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Solution Overview
Problem
Reinforcement learning (RL) for robotic control faces challenges during early training stages, where policies are minimally trained, leading to random actions and sparse rewards, which hinder effective training. Additionally, domain shift issues arise when policies are trained independently of the deployment environment, resulting in reduced robustness and accuracy.
Innovation Solution
The approach involves utilizing existing manually engineered policies to train RL policy models, which can selectively replace portions of the engineered policies. This is achieved by generating state, action pairs based on data from episodes where the engineered policy is used, and incorporating context information to mitigate domain shift. Rewards are also structured to consider the success of other task portions, further enhancing training efficiency and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is used to train a policy model for robotic control, then the robot can learn optimal policies for task performance, but during early training stages the policy takes random actions resulting in sparse rewards and failed task performance
Solution Approach 1:
The system performs preliminary action by using a manually engineered policy to generate successful task episodes before RL training begins. These pre-generated episodes provide a foundation of successful behaviors that the RL agent can learn from, ensuring that training data reflects successful task completion rather than random failures.
Solution Approach 2:
The manually engineered policy acts as an intermediary between random exploration and optimal RL policy. It mediates the training process by generating realistic successful episodes that bridge the gap between random actions and task-successful behaviors, allowing the RL agent to learn from meaningful examples rather than pure random exploration.
2Ease of manufacture
If the policy is trained independently of the deployment environment, then training can be performed in isolation, but domain shift occurs resulting in reduced robustness and accuracy when deployed
Solution Approach 1:
The system incorporates feedback about the deployment environment into the training process by using the manually engineered policy that is designed for the target environment. This feedback mechanism ensures that the RL agent learns from examples that reflect the actual deployment conditions, improving environmental adaptability while maintaining training convenience.
3Reliability
If manually engineered policy is used for robotic task performance, then task can be completed with reasonable accuracy, but the policy cannot be selectively replaced to improve specific portions
Solution Approach 1:
The system applies segmentation by dividing the policy into distinct segments: the manually engineered policy for portions where it performs well, and the RL policy for portions that benefit from learning. This allows selective replacement of specific policy portions while maintaining the overall task performance, providing both reliability and adaptability.
Solution Approach 2:
The system implements dynamics by making the policy composition flexible and adaptable. The RL policy can dynamically replace or supplement the manually engineered policy in specific situations or task portions, allowing the system to adapt to different scenarios while maintaining the stability of the engineered policy where it performs well.
4Device complexity
If reinforcement learning training uses sparse rewards provided only at the end of episodes, then the reward structure is simple, but the policy receives insufficient guidance for learning in large or continuous state and action spaces
Solution Approach 1:
The manually engineered policy performs preliminary action by demonstrating successful task completion before the RL agent begins training. This preliminary demonstration provides implicit guidance about effective behaviors, compensating for the sparsity of formal rewards and improving training efficiency without complicating the reward structure.
Data Source
AI summary
Implementations disclosed herein relate to utilizing at least one existing manually engineered policy, for a robotic task, in training an RL policy model that can be used to at least selectively replace a portion of the engineered policy. The RL policy model can be trained for replacing a portion of a robotic task and can be trained based on data from episodes of attempting performance of the robotic task, including episodes in which the portion is performed based on the engineered policy and/or other portion(s) are performed based on the engineered policy. Once trained, the RL policy model can be used, at least selectively and in lieu of utilization of the engineered policy, to perform the portion of robotic task, while other portion(s) of the robotic task are performed utilizing the engineered policy and/or other similarly trained (but distinct) RL policy model(s).