Task-conditioned adaptation of visual features in multi-task policy learning (2402.07739v4)

Abstract: Successfully addressing a wide variety of tasks is a core ability of autonomous agents, requiring flexibly adapting the underlying decision-making strategies and, as we argue in this work, also adapting the perception modules. An analogical argument would be the human visual system, which uses top-down signals to focus attention determined by the current task. Similarly, we adapt pre-trained large vision models conditioned on specific downstream tasks in the context of multi-task policy learning. We introduce task-conditioned adapters that do not require finetuning any pre-trained weights, combined with a single policy trained with behavior cloning and capable of addressing multiple tasks. 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. To this end, we propose a new optimization-based estimator. We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that, compared to existing work, it can be addressed with a single policy. In particular, we demonstrate that adapting visual features is a key design choice and that the method generalizes to unseen tasks given a few demonstrations.

• The paper introduces task-conditioned adapters that modulate visual features to improve multi-task policy learning without fine-tuning core model weights.
• It employs a single policy via behavior cloning, effectively managing diverse tasks such as manipulation and legged motion.
• Empirical results on CortexBench demonstrate enhanced performance and generalization to unseen tasks through few-shot visual demonstration methods.

Overview of Task-Conditioned Adaptation in Multi-Task Policy Learning

The paper "Task-conditioned adaptation of visual features in multi-task policy learning" by Pierre Marza, Laetitia Matignon, Olivier Simonin, and Christian Wolf presents a method for enhancing the adaptability of visual features within multi-task learning frameworks, specifically in the context of autonomous agents. The core premise of the research revolves around the notion that successful multi-task policy learning requires the flexible adaptation not only of decision-making strategies but also of perception modules, analogous to the task-driven focal mechanisms in human visual systems. The authors propose a novel approach involving task-conditioned adapters integrated into pre-trained vision models, aimed at improving task-specific adaptability without fine-tuning existing model weights.

Methodology

The methodology introduced in the paper builds on an innovative use of task-conditioned adapters within vision models pre-trained using large-scale datasets. This approach extends the utility of such models beyond their general-purpose capabilities, specifically tailoring them to accommodate a diverse set of tasks. Key elements of the proposed system include:

• Task-conditioned Adapters: The introduction of adapters conditioned on specific task embeddings. These adapters facilitate the modulation of visual features, ensuring more precise and task-relevant extraction of information.
• Single Multi-task Policy: Contrary to conventional practices where separate policies are trained for each task, this approach employs a single policy trained via behavior cloning. The policy leverages the information provided by task-conditioned adapters to handle multiple heterogeneous tasks.
• Task Embeddings: Tasks are encapsulated within a learned embedding space. These embeddings, critical to adapting the model to specific tasks, are either selected based on known task information or inferred from demonstrations in a few-shot manner for unseen tasks.

The approach relies on a ViT-based (Vision Transformer) visual encoder, enriched with middle and top adapter layers. This architecture enables the model to maintain its foundational pre-trained capabilities while being adaptable to task-specific nuances. The adapters are critical to adjusting the model's attention and response to different tasks, driving improved task performance.

Key Results

The authors evaluate their method on the CortexBench benchmark, demonstrating that their approach, particularly the task-conditioned adaptation of visual features, results in significant improvements over existing methods. Notable findings include:

• The proposed single policy effectively manages a variety of tasks, such as manipulation and legged motion, indicating the robustness and flexibility of the method.
• The use of task-conditioned visual adapters significantly boosts the performance of multi-task policies compared to those with non-adapted pre-trained visual features.
• The technique shows promising generalization capabilities to unseen tasks through few-shot learning, using visual demonstrations to estimate unseen task embeddings.

The numerical outcomes reveal that the integration of task-conditioned adapters is a pivotal design element, improving the performance across diverse settings and showing resilience even when confronted with novel task configurations.

Theoretical and Practical Implications

The article underscores the importance of adaptive perception in intelligent systems, expanding the landscape of multi-task robotics and artificial intelligence applications. The ability to generalize across tasks without extensive re-training or fine-tuning positions this technique as a valuable asset for future AI systems, particularly in domains requiring a high degree of task variability and adaptability.

From a theoretical standpoint, the work contributes to our understanding of how task-specific adaptations in perception models can influence policy effectiveness. It offers a framework for exploring task regularities and embedding spaces that capture shared task characteristics, paving the way for more extensive manipulation of neural representations conditioned on varying objectives.

Future Directions

There exists untapped potential in further optimizing task conditioning within broader AI systems. Future work could explore:

• Extending the embedding space to accommodate a wider array of tasks and modalities, providing a richer foundation for inference.
• Investigating the integration of these techniques with real-time dynamic environments, improving adaptability and resilience in deployed systems.
• Enhancing the efficiency of task embedding estimation, potentially leveraging few-shot learning techniques to refine and accelerate the adaptation process even further.

In conclusion, this paper provides a compelling approach to enhancing the flexibility and generality of multi-task learning systems through task-conditioned visual adaptation, serving as a foundational step towards more intelligent and adaptable AI agents.