Task-Conditioned Adaptation of Visual Features Improves Multi-Task Policy Learning

Основні поняття

Adapting pre-trained visual features conditioned on the current task is a key design choice that enables a single multi-task policy to perform well across a wide variety of robotic manipulation and locomotion tasks.

Анотація

The content presents a method for multi-task policy learning that adapts pre-trained visual features conditioned on the current task. The key elements are:

1. Task-conditioned visual adapters: The authors introduce "middle" and "top" adapters that modulate the output of a pre-trained Vision Transformer (ViT) backbone, conditioned on a learned task embedding. This allows the visual features to be adapted to the specific requirements of each task.

2. Multi-task policy: A single policy is trained using behavior cloning from expert demonstrations, capable of addressing multiple heterogeneous tasks. The policy is conditioned on the task embedding along with the adapted visual features.

3. Few-shot adaptation: The task embedding can be optimized from a few demonstrations of a new, unseen task, enabling the policy to generalize to novel tasks without finetuning.

The experiments show that the task-conditioned visual adapters are crucial, outperforming both single-task policies and a multi-task policy without adapters. The few-shot adaptation to new tasks demonstrates the ability to capture task regularities in the learned embedding space.

Статистика

"We consider a set of K=12 known tasks from 3 benchmarks: Adroit, Deepmind Control, and MetaWorld."
"We evaluate on a set of U=15 unknown tasks from MetaWorld, collecting 5 demonstrations per task to optimize the task embedding."

Цитати

"Adapting pre-trained large vision models conditioned on specific downstream tasks in the context of multi-task policy learning."
"We condition the visual adapters on task embeddings, which can be selected at inference if the task is known, or alternatively inferred from a set of example demonstrations."

Ключові висновки, отримані з

Task-conditioned adaptation of visual features in multi-task policy learning

by Pierre Marza... о arxiv.org 04-04-2024

https://arxiv.org/pdf/2402.07739.pdf

Глибші Запити

How can the task embedding space be further improved to better capture task regularities and enable stronger few-shot generalization?

To enhance the task embedding space for better capturing task regularities and enabling stronger few-shot generalization, several strategies can be considered:

Incorporating Task Hierarchies: Introducing a hierarchical structure to the task embedding space can help capture relationships between tasks at different levels of abstraction. This can enable the model to generalize more effectively by leveraging similarities between related tasks.

Dynamic Embedding Updates: Implementing a mechanism for dynamically updating task embeddings during training can allow the model to adapt to new information and adjust embeddings based on the current training data. This adaptive approach can improve the model's ability to generalize to unseen tasks.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay to the task embedding space can prevent overfitting and encourage the embeddings to capture more generalizable task features. This can lead to improved performance on new tasks during few-shot learning scenarios.

Meta-Learning for Task Embeddings: Leveraging meta-learning techniques to learn task embeddings in a more adaptive and efficient manner can enhance the model's ability to generalize to new tasks. Meta-learning can help the model quickly adapt to new tasks based on a few examples, leading to stronger few-shot generalization capabilities.

By incorporating these strategies, the task embedding space can be further optimized to capture task regularities effectively and enable robust few-shot generalization in multi-task learning scenarios.

How can the task embedding space be further improved to better capture task regularities and enable stronger few-shot generalization?

To enhance the task embedding space for better capturing task regularities and enabling stronger few-shot generalization, several strategies can be considered:

Incorporating Task Hierarchies: Introducing a hierarchical structure to the task embedding space can help capture relationships between tasks at different levels of abstraction. This can enable the model to generalize more effectively by leveraging similarities between related tasks.

Dynamic Embedding Updates: Implementing a mechanism for dynamically updating task embeddings during training can allow the model to adapt to new information and adjust embeddings based on the current training data. This adaptive approach can improve the model's ability to generalize to unseen tasks.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay to the task embedding space can prevent overfitting and encourage the embeddings to capture more generalizable task features. This can lead to improved performance on new tasks during few-shot learning scenarios.

Meta-Learning for Task Embeddings: Leveraging meta-learning techniques to learn task embeddings in a more adaptive and efficient manner can enhance the model's ability to generalize to new tasks. Meta-learning can help the model quickly adapt to new tasks based on a few examples, leading to stronger few-shot generalization capabilities.

By incorporating these strategies, the task embedding space can be further optimized to capture task regularities effectively and enable robust few-shot generalization in multi-task learning scenarios.

How can the task embedding space be further improved to better capture task regularities and enable stronger few-shot generalization?

To enhance the task embedding space for better capturing task regularities and enabling stronger few-shot generalization, several strategies can be considered:

Incorporating Task Hierarchies: Introducing a hierarchical structure to the task embedding space can help capture relationships between tasks at different levels of abstraction. This can enable the model to generalize more effectively by leveraging similarities between related tasks.

Dynamic Embedding Updates: Implementing a mechanism for dynamically updating task embeddings during training can allow the model to adapt to new information and adjust embeddings based on the current training data. This adaptive approach can improve the model's ability to generalize to unseen tasks.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay to the task embedding space can prevent overfitting and encourage the embeddings to capture more generalizable task features. This can lead to improved performance on new tasks during few-shot learning scenarios.

Meta-Learning for Task Embeddings: Leveraging meta-learning techniques to learn task embeddings in a more adaptive and efficient manner can enhance the model's ability to generalize to new tasks. Meta-learning can help the model quickly adapt to new tasks based on a few examples, leading to stronger few-shot generalization capabilities.

By incorporating these strategies, the task embedding space can be further optimized to capture task regularities effectively and enable robust few-shot generalization in multi-task learning scenarios.

How can the task embedding space be further improved to better capture task regularities and enable stronger few-shot generalization?

To enhance the task embedding space for better capturing task regularities and enabling stronger few-shot generalization, several strategies can be considered:

Incorporating Task Hierarchies: Introducing a hierarchical structure to the task embedding space can help capture relationships between tasks at different levels of abstraction. This can enable the model to generalize more effectively by leveraging similarities between related tasks.

Dynamic Embedding Updates: Implementing a mechanism for dynamically updating task embeddings during training can allow the model to adapt to new information and adjust embeddings based on the current training data. This adaptive approach can improve the model's ability to generalize to unseen tasks.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay to the task embedding space can prevent overfitting and encourage the embeddings to capture more generalizable task features. This can lead to improved performance on new tasks during few-shot learning scenarios.

Meta-Learning for Task Embeddings: Leveraging meta-learning techniques to learn task embeddings in a more adaptive and efficient manner can enhance the model's ability to generalize to new tasks. Meta-learning can help the model quickly adapt to new tasks based on a few examples, leading to stronger few-shot generalization capabilities.

By incorporating these strategies, the task embedding space can be further optimized to capture task regularities effectively and enable robust few-shot generalization in multi-task learning scenarios.

How can the task embedding space be further improved to better capture task regularities and enable stronger few-shot generalization?

To enhance the task embedding space for better capturing task regularities and enabling stronger few-shot generalization, several strategies can be considered:

Incorporating Task Hierarchies: Introducing a hierarchical structure to the task embedding space can help capture relationships between tasks at different levels of abstraction. This can enable the model to generalize more effectively by leveraging similarities between related tasks.

Dynamic Embedding Updates: Implementing a mechanism for dynamically updating task embeddings during training can allow the model to adapt to new information and adjust embeddings based on the current training data. This adaptive approach can improve the model's ability to generalize to unseen tasks.

Regularization Techniques: Applying regularization techniques such as dropout or weight decay to the task embedding space can prevent overfitting and encourage the embeddings to capture more generalizable task features. This can lead to improved performance on new tasks during few-shot learning scenarios.

Meta-Learning for Task Embeddings: Leveraging meta-learning techniques to learn task embeddings in a more adaptive and efficient manner can enhance the model's ability to generalize to new tasks. Meta-learning can help the model quickly adapt to new tasks based on a few examples, leading to stronger few-shot generalization capabilities.

By incorporating these strategies, the task embedding space can be further optimized to capture task regularities effectively and enable robust few-shot generalization in multi-task learning scenarios.