Robotic Task Policy Training Using Engineered RL 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 deployment environments, 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 done by generating state, action pairs 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 addressing domain shift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is used for robotic control, then the robot can learn optimal policies for task performance, but during early training stages the policy is minimally trained resulting in random actions and sparse rewards that hinder effective training
Solution Approach 1:
The system performs preliminary action by using a manually engineered policy to generate initial training data before the RL policy model is trained. This preliminary data generation phase allows the system to have meaningful training examples from the start, rather than beginning with completely random policies. The engineered policy executes tasks and collects state-action-reward tuples that form the initial training dataset, providing a head start for the RL model.
Solution Approach 2:
The manually engineered policy serves as an intermediary between the desired task performance and the RL training process. It mediates by generating high-quality training data that bridges the gap between random initial policies and optimal performance. The engineered policy translates complex task requirements into structured training examples that the RL model can learn from, acting as a bridge during the transition from manual to autonomous control.
2Ease of manufacture
If the policy is trained independently of the deployment environment, then training can proceed in isolation, but domain shift occurs resulting in limitations in robustness and accuracy when deployed
Solution Approach 1:
The system applies parameter changes by modifying the training data to include context information from the deployment environment. Instead of training on isolated task portions, the RL model receives training examples that incorporate environmental context, task progress state, and information about other task portions. This changes the training parameters to better reflect deployment conditions, reducing domain shift while maintaining training feasibility.
3Extent of automation
If the RL policy model replaces the engineered policy, then autonomous performance improves, but the state space and action space are large and continuous making training difficult with sparse rewards
Solution Approach 1:
The system applies segmentation by breaking down the complex robotic task into distinct task portions or subtasks. Each portion is handled separately in the training process, with the RL model learning to execute specific segments while receiving context about the overall task state. This segmentation reduces the effective state and action space the model must handle at once, making training more manageable despite the overall complexity of the full task.
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).