Behavior Transformers: Cloning $k$ modes with one stone (2206.11251v2)

Abstract: While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-generated datasets. Human behaviors have wide variance, multiple modes, and human demonstrations typically do not come with reward labels. These properties limit the applicability of current methods in Offline RL and Behavioral Cloning to learn from large, pre-collected datasets. In this work, we present Behavior Transformer (BeT), a new technique to model unlabeled demonstration data with multiple modes. BeT retrofits standard transformer architectures with action discretization coupled with a multi-task action correction inspired by offset prediction in object detection. This allows us to leverage the multi-modal modeling ability of modern transformers to predict multi-modal continuous actions. We experimentally evaluate BeT on a variety of robotic manipulation and self-driving behavior datasets. We show that BeT significantly improves over prior state-of-the-art work on solving demonstrated tasks while capturing the major modes present in the pre-collected datasets. Finally, through an extensive ablation study, we analyze the importance of every crucial component in BeT. Videos of behavior generated by BeT are available at https://notmahi.github.io/bet

An Overview of Behavior Transformers for Learning Multi-Modal Human Behaviors

The paper "Behavior Transformers: Cloning $k$ modes with one stone" by Nur Muhammad Shafiullah and collaborators from New York University introduces a novel approach to behavior learning from unlabeled, multi-modal human demonstrations. It highlights the limitations of current methodologies in offline Reinforcement Learning (RL) and Behavioral Cloning (BC) when dealing with large pre-collected datasets, which often encapsulate diverse and unlabeled human behaviors.

Key Contributions

1. Introduction of the Behavior Transformer (BeT): The paper proposes the Behavior Transformer, an innovative technique that modifies standard transformer architectures to handle unlabeled demonstration data exhibiting multiple behavioral modes. This method retrofits transformers with discrete action representation and a multi-task action corrector scheme that draws inspiration from object detection in computer vision.
2. Action Discretization: Instead of learning generative models for continuous actions, BeT clusters continuous actions into discrete bins using k-means, creating a categorical representation that simplifies the prediction of multi-modal action distributions. This approach transforms the challenge of modeling continuous spaces into a more tractable categorical problem.
3. Residual Action Correction: To address the limitations resulting from action discretization, BeT employs a residual action head that fine-tunes discrete actions by learning a residual vector. This component ensures sampled actions' fidelity for subsequent rollouts in real or simulated environments.
4. Experimental Validation: The paper empirically demonstrates the effectiveness of BeT across different robotics and self-driving datasets. Notably, BeT outperforms previous state-of-the-art methods by capturing major behavioral modes within the data. Each environment tested, including CARLA, Block push, and Kitchen simulations, presents distinct challenges, highlighting BeT’s versatility.

Findings and Implications

The experimental evaluations present compelling evidence of the Behavior Transformer's capabilities, as BeT consistently enhances performance over baselines, especially in tasks characterized by significant long-term dependencies and randomness. Its ability to capture multiple behavioral modes is showcased prominently with high entropy measures in task sequences compared to other models. These results suggest BeT's potential in domains requiring robust multi-modal behavior modeling without the explicit need for reward labels or online interactions.

Theoretical and Practical Implications

From a theoretical perspective, BeT extends the transformer model utility by exploring behavioral and RL contexts without remunerative rewards. Practically, its ability to learn from existing datasets without further data collection minimizes deployment costs, potentially revolutionizing applications in self-driving cars, human-robot interaction, and autonomous navigation systems.

Speculative Future Directions

Future endeavors might focus on integrating BeT with online RL frameworks to facilitate real-time adaptation to dynamic environments. Another exciting prospect lies in leveraging BeT's architecture for other multi-modal, non-deterministic domains beyond robotics, such as interactive AI within entertainment and healthcare. Additionally, exploring richer temporal contexts or hierarchical action representations might considerably broaden BeT's applicability and performance.

In summary, the paper delivers a significant methodological advancement for handling unlabeled, multi-modal datasets, positioning BeT as a promising tool for driving forward both theoretical inquiry and practical implementations in AI and machine learning.