inzicht - Robotics - # Fine-tuning Large-Scale Robot Policies with Reinforcement Learning

FLaRe: Achieving State-of-the-Art Performance on Household Robot Tasks through Large-Scale Reinforcement Learning Fine-Tuning

Q: How can FLaRe be extended to handle tasks that require long-term planning and reasoning, such as multi-step household chores or complex manipulation sequences?

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.

Q: What are the potential limitations of FLaRe's reliance on simulation for fine-tuning, and how could it be adapted to handle tasks where robust simulations are unavailable?

One of the primary limitations of FLaRe's reliance on simulation for fine-tuning is the sim-to-real gap, which refers to the discrepancies between simulated environments and real-world conditions. While FLaRe employs techniques like domain randomization and feature extraction to mitigate this gap, there are still challenges when dealing with tasks that involve complex dynamics, such as those involving liquids or soft objects, where accurate simulation is difficult to achieve. To adapt FLaRe for tasks where robust simulations are unavailable, a hybrid approach could be employed. This would involve combining simulated training with real-world data collection. For instance, FLaRe could initially be fine-tuned in simulation, followed by a phase of real-world fine-tuning using a small number of real-world interactions. This would allow the model to adjust its learned policies based on actual environmental feedback, enhancing its adaptability to real-world conditions. Moreover, incorporating online learning techniques could enable FLaRe to continuously improve its performance as it encounters new scenarios in the real world. By allowing the model to learn from its experiences in real-time, it can adapt to unforeseen challenges and refine its policies accordingly. This approach would not only enhance the robustness of FLaRe in real-world applications but also reduce the dependency on high-fidelity simulations.

Q: Could the stabilization techniques introduced in FLaRe be applied to other RL fine-tuning settings, such as fine-tuning language models or vision-language models, to improve their performance and sample efficiency?

Yes, the stabilization techniques introduced in FLaRe could be effectively applied to other reinforcement learning (RL) fine-tuning settings, including fine-tuning language models and vision-language models. The core principles behind these techniques—such as using smaller learning rates, disabling entropy bonuses, and separating actor and critic networks—are broadly applicable across various domains where RL is utilized. For instance, in fine-tuning language models, using a smaller learning rate can help prevent catastrophic forgetting, where the model loses previously learned information during the fine-tuning process. This is particularly important when adapting language models to specific tasks or domains, as it allows the model to retain its general language understanding while learning task-specific nuances. Similarly, the separation of actor and critic networks can enhance stability in vision-language models, where the complexity of the input data can lead to unstable training dynamics. By ensuring that the feature extraction processes for the actor and critic are independent, the model can maintain robust representations while optimizing for specific tasks. Furthermore, the use of on-policy algorithms, as demonstrated in FLaRe, can improve sample efficiency in these settings by ensuring that the model learns from the most relevant and recent experiences. This is crucial in language and vision-language tasks, where the context and relevance of data can change rapidly. Overall, the stabilization techniques from FLaRe offer valuable insights that can enhance the performance and sample efficiency of RL fine-tuning across various applications, including language and vision-language models.

Belangrijkste concepten

FLaRe, a large-scale Reinforcement Learning fine-tuning framework, can effectively align pre-trained robot policies towards task completion, achieving state-of-the-art performance on both previously demonstrated and entirely novel tasks and embodiments.

Samenvatting

The paper introduces FLaRe, a framework for fine-tuning large-scale robot policies using Reinforcement Learning (RL). The key insights are:

Start from a multi-task robotics foundation model trained via Behavior Cloning (BC), which already possesses valuable features and behavior priors.
Perform large-scale RL fine-tuning in simulation, leveraging extensive environments and objects to align the pre-trained policy towards task completion.
Introduce a set of stabilization techniques, including using an on-policy RL algorithm, smaller learning rate, disabling entropy bonus, and separating actor and critic networks, to ensure stable and effective RL fine-tuning.

FLaRe achieves state-of-the-art performance on a set of long-horizon mobile manipulation tasks, outperforming prior methods by a large margin both in simulation (+23.6%) and on real robots (+30.7%). It also demonstrates the ability to generalize to novel tasks and adapt to new embodiments with minimal fine-tuning effort.

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The paper reports the following key metrics:

On the CHORES benchmark, FLaRe achieves an average success rate of 79.5% in unseen environments.
On novel tasks like ObjNavRelAttr, RoomNav, and ObjNavAfford, FLaRe outperforms prior state-of-the-art methods by a large margin.
On the real-world Stretch RE-1 robot, FLaRe achieves an average success rate of 80.7%, outperforming the best prior work by 30.7%.
FLaRe achieves a 15x reduction in training time compared to the previous state-of-the-art method.

Citaten

"FLaRe achieves SoTA performance on household mobile manipulation tasks. In established simulation benchmark [7], it achieves an average 79.5% success rate, +23.6% absolute improvements over the best baseline."
"In the real world, FLaRe achieves excellent results (80.7% SR on average), outperforming the best prior work by +30.7%."
"FLaRe enables efficient training with a 15x reduction in training time compared to the previous SoTA method, using a simple sparse reward without the need for handcrafted reward functions."

Belangrijkste Inzichten Gedestilleerd Uit

FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning

by Jiaheng Hu, ... om arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16578.pdf

FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning

Diepere vragen

How can FLaRe be extended to handle tasks that require long-term planning and reasoning, such as multi-step household chores or complex manipulation sequences?

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.

What are the potential limitations of FLaRe's reliance on simulation for fine-tuning, and how could it be adapted to handle tasks where robust simulations are unavailable?

One of the primary limitations of FLaRe's reliance on simulation for fine-tuning is the sim-to-real gap, which refers to the discrepancies between simulated environments and real-world conditions. While FLaRe employs techniques like domain randomization and feature extraction to mitigate this gap, there are still challenges when dealing with tasks that involve complex dynamics, such as those involving liquids or soft objects, where accurate simulation is difficult to achieve.
To adapt FLaRe for tasks where robust simulations are unavailable, a hybrid approach could be employed. This would involve combining simulated training with real-world data collection. For instance, FLaRe could initially be fine-tuned in simulation, followed by a phase of real-world fine-tuning using a small number of real-world interactions. This would allow the model to adjust its learned policies based on actual environmental feedback, enhancing its adaptability to real-world conditions.
Moreover, incorporating online learning techniques could enable FLaRe to continuously improve its performance as it encounters new scenarios in the real world. By allowing the model to learn from its experiences in real-time, it can adapt to unforeseen challenges and refine its policies accordingly. This approach would not only enhance the robustness of FLaRe in real-world applications but also reduce the dependency on high-fidelity simulations.

Could the stabilization techniques introduced in FLaRe be applied to other RL fine-tuning settings, such as fine-tuning language models or vision-language models, to improve their performance and sample efficiency?

Yes, the stabilization techniques introduced in FLaRe could be effectively applied to other reinforcement learning (RL) fine-tuning settings, including fine-tuning language models and vision-language models. The core principles behind these techniques—such as using smaller learning rates, disabling entropy bonuses, and separating actor and critic networks—are broadly applicable across various domains where RL is utilized.
For instance, in fine-tuning language models, using a smaller learning rate can help prevent catastrophic forgetting, where the model loses previously learned information during the fine-tuning process. This is particularly important when adapting language models to specific tasks or domains, as it allows the model to retain its general language understanding while learning task-specific nuances.
Similarly, the separation of actor and critic networks can enhance stability in vision-language models, where the complexity of the input data can lead to unstable training dynamics. By ensuring that the feature extraction processes for the actor and critic are independent, the model can maintain robust representations while optimizing for specific tasks.
Furthermore, the use of on-policy algorithms, as demonstrated in FLaRe, can improve sample efficiency in these settings by ensuring that the model learns from the most relevant and recent experiences. This is crucial in language and vision-language tasks, where the context and relevance of data can change rapidly.
Overall, the stabilization techniques from FLaRe offer valuable insights that can enhance the performance and sample efficiency of RL fine-tuning across various applications, including language and vision-language models.