FLaRe can be extended to handle tasks requiring long-term planning and reasoning by integrating hierarchical reinforcement learning (HRL) frameworks. In HRL, complex tasks are decomposed into subtasks, allowing the robot to plan and execute multi-step actions more effectively. This approach can be particularly beneficial for household chores, where tasks often involve a sequence of actions that must be coordinated to achieve a final goal.
To implement this, FLaRe could utilize a two-level policy structure: a high-level policy that determines the sequence of subtasks and a low-level policy that executes the specific actions required for each subtask. The high-level policy could be trained using a combination of behavior cloning and reinforcement learning, leveraging the existing multi-task capabilities of the foundational model. The low-level policy would then focus on executing the actions necessary to complete each subtask, benefiting from the robust representations learned during the initial FLaRe training.
Additionally, incorporating a memory mechanism, such as recurrent neural networks (RNNs) or attention-based models, could enhance the robot's ability to maintain context over longer sequences of actions. This would allow the robot to remember previous states and actions, facilitating better decision-making in complex scenarios. By combining these strategies, FLaRe could effectively tackle long-term planning and reasoning tasks, improving its performance in multi-step household chores and intricate manipulation sequences.