TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models (2310.05905v2)

Abstract: The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challenges associated with training or fine-tuning these large models for such applications. Prior work mainly emphasizes either effective pretraining of large models for decision-making or single-task adaptation. But real-world problems will require data-efficient, continual adaptation for new control tasks. Recognizing these constraints, we introduce TAIL (Task-specific Adapters for Imitation Learning), a framework for efficient adaptation to new control tasks. Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e.g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data. Our extensive experiments in large-scale language-conditioned manipulation tasks comparing prevalent parameter-efficient fine-tuning techniques and adaptation baselines suggest that TAIL with LoRA can achieve the best post-adaptation performance with only 1\% of the trainable parameters of full fine-tuning, while avoiding catastrophic forgetting and preserving adaptation plasticity in continual learning settings.

• The paper introduces TAIL, a novel framework that efficiently adapts large pretrained decision-making models using parameter-efficient techniques such as LoRA, Bottleneck Adapters, and P-Tuning.
• The paper demonstrates that TAIL with LoRA outperforms traditional adaptation methods by using fewer trainable parameters and mitigating overfitting in data-scarce settings.
• The paper highlights practical implications for deploying autonomous agents and outlines future research directions in continuous learning for complex control tasks.

TAIL: Enhancing Adaptation in Pretrained Decision-Making Models

Introduction to TAIL

The adaptation of large pretrained models to novel control tasks in decision-making domains—such as robotics—poses significant challenges due to the scarcity of control-task data and computational constraints. In addressing these challenges, our research introduces Task-specific Adapters for Imitation Learning (TAIL), a framework designed for the efficient adaptation of large pretrained models to a sequence of new control tasks. Inspired by the success of parameter-efficient fine-tuning (PEFT) techniques in natural language processing, TAIL explores the use of similar methods—namely Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA)—to adapt pretrained decision-making models with limited demonstration data. Our comprehensive comparison of these techniques reveals that TAIL with LoRA notably outperforms traditional adaptation methods, achieving superior performance with only a fraction of the trainable parameters.

Efficient Adaptation Techniques

At the core of TAIL are three distinct parameter-efficient adaptation techniques:

1. Bottleneck Adapters involve sequential insertion of adaptable layers within the model to fine-tune for new tasks.
2. Prefix Tuning (P-Tuning) adds trainable prefix tokens to the input sequence, allowing the model to adjust its predictions based on these added contexts.
3. Low-Rank Adaptation (LoRA) employs parallel integration by introducing low-rank matrices to the model's weight matrix, facilitating adaptation with minimal parameters.

Our paper explores the efficacy of these techniques in a continual imitation learning setting. Notably, TAIL equipped with LoRA demonstrated remarkable adaptation performance, attributing its success to the minimal alteration of the model's original pretrained representations, resistance to overfitting in data-sparse environments, and its computational efficiency.

Theoretical and Practical Implications

The introduction of TAIL and our findings from implementing PEFT techniques have substantial theoretical and practical implications. Theoretically, TAIL validates the hypothesis that large, pretrained models can be adapted to new tasks efficiently without necessitating a substantial increase in parameters or computational resources. Practically, TAIL lays the groundwork for deploying autonomous agents capable of adapting to varied tasks with minimal human intervention and computational overhead. Our results also speculate on the future development of AI, where support for continuous learning and adaptation becomes intrinsic to model design, particularly in data-constrained decision-making domains.

Future Research Directions

With TAIL's promising outcomes, future research could explore several avenues:

• Investigating the integration of TAIL with other decision-making frameworks or learning paradigms.
• Extending TAIL's application beyond the field of imitation learning to reinforcement learning or unsupervised learning tasks.
• Experimenting with a combination of PEFT techniques within TAIL to uncover potentially synergistic effects on adaptation efficiency and performance.

Moreover, the insights gained from comparing various PEFT techniques in TAIL underscore the necessity of continuing such explorations to refine our understanding and methodologies for adapting large-scale pretrained models in continually evolving environments.

Conclusion

TAIL represents a significant step towards realizing the full potential of large pretrained models in decision-making domains by enabling efficient, practical, and scalable task-specific adaptation. The success of LoRA within TAIL, in particular, marks a pivotal advancement in adaptation techniques, offering a scalable solution that preserves the model's core knowledge while facilitating precise and rapid adjustments to new tasks. As we advance, TAIL and the insights derived from our research will undeniably contribute to the evolution of autonomous systems, enhancing their adaptability and utility in real-world applications.