Large Language Models as Generalizable Policies for Embodied Tasks
מושגי ליבה
Large language models can be adapted through reinforcement learning to serve as generalizable policies for embodied visual tasks, outperforming other common baselines in terms of paraphrastic robustness and behavior generalization.
תקציר
The paper introduces a method called Large LAnguage model Reinforcement learning Policy (LLaRP) that adapts pre-trained large language models (LLMs) to operate in embodied multi-modal decision-making settings. LLaRP takes as input text instructions and visual egocentric observations, and outputs actions directly in the environment.
The key highlights are:
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LLaRP is trained using only reinforcement learning, without requiring expert demonstration data. The LLM backbone and visual encoder are frozen during training, while the action output module is trained.
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LLaRP exhibits strong generalization capabilities, outperforming other baselines on both paraphrastic robustness (handling linguistic variations of instructions) and behavior generalization (solving novel task types).
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On a novel benchmark called Language Rearrangement, which consists of 150,000 training and 1,000 testing tasks for language-conditioned rearrangement, LLaRP achieves a 42% success rate, significantly higher than other common baselines.
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LLaRP also shows improved sample efficiency during training and continual learning compared to non-LLM baselines. Additionally, LLaRP trained with RL outperforms LLaRP trained with imitation learning, demonstrating the efficiency of the RL approach.
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Experiments on Atari games further demonstrate that the LLM-based LLaRP can boost performance beyond the Language Rearrangement domain.
Overall, the paper shows that by leveraging the broad knowledge and generalization capabilities of large language models, embodied agents can achieve significantly improved performance and generalization on a wide range of tasks.
Large Language Models as Generalizable Policies for Embodied Tasks
סטטיסטיקה
"LLaRP achieves 42% success rate on 1,000 unseen tasks, 1.7x the success rate of other common baselines."
"LLaRP is 3x more efficient than LSTM-Flan in continual learning on downstream tasks."
ציטוטים
"LLaRP is almost 1.7x better than the next best performing baseline, 42% vs. 25%."
"LLaRP displays superior generalization capabilities across all settings."
שאלות מעמיקות
How can the LLM-based policy be further improved to directly interact with the environment via the language head, without the need for a separate action decoder module?
To enhance the LLM-based policy's ability to directly interact with the environment through the language head, several improvements can be implemented:
End-to-End Training: One approach is to train the LLM in an end-to-end fashion, where the language head directly interfaces with the environment without the need for an explicit action decoder. This would involve designing the LLM to generate action sequences directly from the language input, incorporating both high-level task instructions and low-level action commands.
Multi-Modal Fusion: Integrate multi-modal fusion techniques to combine visual observations with textual instructions within the LLM architecture. This fusion can enable the model to generate action sequences that are informed by both the language input and the visual context, leading to more contextually relevant and effective actions.
Attention Mechanisms: Utilize attention mechanisms within the LLM to dynamically focus on relevant parts of the instruction and the visual input when generating actions. This can help the model attend to critical information for decision-making and action execution.
Reinforcement Learning with Language Rewards: Implement reinforcement learning with language rewards, where the language model is directly optimized based on the success of executing actions derived from the language input. This can reinforce the model's ability to generate action sequences that align with the intended task goals specified in natural language.
Hierarchical Planning: Incorporate hierarchical planning strategies within the LLM to enable the generation of action plans at different levels of abstraction. This can facilitate more efficient and structured decision-making processes, especially in complex and multi-step tasks.
How can the potential limitations of using large language models in embodied settings be addressed?
While large language models (LLMs) offer significant advantages in embodied settings, they also come with potential limitations that need to be addressed:
Computational Efficiency: The computational demands of large LLMs can be a limitation, especially in real-time applications. To address this, model optimization techniques, such as model distillation, quantization, and efficient hardware acceleration, can be employed to reduce the computational overhead.
Sample Efficiency: Large LLMs may require extensive data for training, which can hinder sample efficiency. Techniques like curriculum learning, transfer learning, and data augmentation can be utilized to improve sample efficiency and accelerate the learning process.
Interpretability: Understanding the decision-making process of LLMs in embodied settings can be challenging due to their complex architecture. Incorporating interpretability methods, such as attention visualization, saliency maps, and explanation generation, can enhance the transparency of model decisions.
Generalization: Ensuring that LLMs generalize well to diverse and unseen scenarios is crucial. Techniques like domain adaptation, continual learning, and robust training on diverse datasets can enhance the model's generalization capabilities across varied environments and tasks.
Ethical Considerations: Addressing ethical concerns related to bias, fairness, and accountability in LLMs is essential. Implementing bias mitigation strategies, fairness-aware training, and transparent reporting practices can help mitigate ethical risks associated with large language models.
How can the Language Rearrangement benchmark be extended to capture even more diverse and complex real-world scenarios for embodied AI systems?
To expand the Language Rearrangement benchmark and capture a broader range of real-world scenarios for embodied AI systems, the following strategies can be implemented:
Multi-Modal Tasks: Introduce tasks that require the integration of multiple modalities, such as language, vision, and audio, to simulate more realistic and complex real-world scenarios. This can include tasks that involve auditory instructions, object recognition from visual cues, and spatial reasoning.
Temporal Reasoning: Incorporate tasks that involve temporal reasoning and long-term dependencies, requiring agents to plan and execute actions over extended time horizons. This can include tasks with sequential steps, dynamic environments, and delayed rewards.
Social Interaction: Design tasks that involve social interaction and collaboration between multiple agents, simulating real-world scenarios where agents need to communicate, coordinate, and cooperate to achieve common goals. This can include tasks with shared objectives, communication constraints, and joint decision-making.
Adversarial Scenarios: Introduce adversarial scenarios and challenges that test the robustness and adaptability of embodied AI systems. This can include tasks with deceptive instructions, conflicting goals, and dynamic obstacles that require agents to navigate complex and uncertain environments.
Transfer Learning: Create tasks that facilitate transfer learning across different environments, domains, and task settings. This can include tasks with varying levels of complexity, environmental conditions, and task constraints to evaluate the model's ability to generalize and adapt to new scenarios effectively.
By incorporating these enhancements, the Language Rearrangement benchmark can provide a more comprehensive and diverse evaluation framework for testing the capabilities of embodied AI systems in complex and realistic real-world scenarios.
