Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers (2409.20537v1)

Abstract: One of the roadblocks for training generalist robotic models today is heterogeneity. Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting. This work studies the problem of learning policy representations through heterogeneous pre-training on robot data across different embodiments and tasks at scale. We propose Heterogeneous Pre-trained Transformers (HPT), which pre-train a large, shareable trunk of a policy neural network to learn a task and embodiment agnostic shared representation. This general architecture aligns the specific proprioception and vision inputs from distinct embodiments to a short sequence of tokens and then processes such tokens to map to control robots for different tasks. Leveraging the recent large-scale multi-embodiment real-world robotic datasets as well as simulation, deployed robots, and human video datasets, we investigate pre-training policies across heterogeneity. We conduct experiments to investigate the scaling behaviors of training objectives, to the extent of 52 datasets. HPTs outperform several baselines and enhance the fine-tuned policy performance by over 20% on unseen tasks in multiple simulator benchmarks and real-world settings. See the project website (https://liruiw.github.io/hpt/) for code and videos.

Essay: Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-Trained Transformers

In recent years, the field of robotics has been grappling with the challenge of heterogeneity inherent in robotic learning. The paper "Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers" introduces Heterogeneous Pre-trained Transformers (HPT), a novel architecture that addresses this challenge. The research investigates the efficacy of pre-training policy representations on diverse, large-scale robot data encompassing various embodiments and tasks.

Introduction to the Problem

Training robotic policies has traditionally been a task-specific endeavor, necessitating the collection of specialized data for each unique robot, task, and environment setup. This approach not only proves to be cost-intensive but also tends to overfit the models to specific settings, thereby limiting their generalization capability. The fundamental hypothesis driving this research is the potential for general models, pre-trained on diverse, high-quality datasets, to outperform specialized models due to enhanced generalization.

The recent surge in large-scale, open-source datasets for robotics, including simulation data, deployed robots, and human videos, enables this direction of research. However, the inherent heterogeneity in robot data — differences in robot hardware, sensor configurations, environments, and tasks — presents a significant barrier to leveraging these datasets effectively for training foundational robotic models.

Architectural Overview: Heterogeneous Pre-Trained Transformers (HPT)

HPT proposes an architecture that modularizes the policy network into three distinct components:

1. Embodiment-Specific Stems: These are responsible for tokenizing proprioception and vision inputs from different robot embodiments.
2. Shared Trunk: A transformer-based model that processes the tokens to generate a shared representation.
3. Task-Specific Heads: These decode the shared representation into actions pertinent to different tasks.

The key innovation is the decoupling of embodiment-specific processing (stems) from the shared processing (trunk). This allows HPT to align heterogeneous sensor inputs into a common tokenized space, enabling the shared transformer trunk to learn a generalized, task and embodiment-agnostic representation.

Methodology and Training

HPT is pre-trained on a comprehensive dataset comprising multi-embodiment real-world robotic datasets, simulations, and human video datasets. The paper involved experiments to understand the scaling behavior of the proposed architecture with respect to various training objectives. Specifically, the focus was on:

• Evaluating the impact of dataset quantity and diversity.
• Exploring the effect of model size and computational resources.

The pre-training process utilizes behavior cloning as the primary learning objective, optimizing a Huber loss between the predicted and actual actions across diverse datasets. This method ensures that the trunk can handle a wide range of inputs while minimizing overfitting to any specific embodiment or task.

Key Findings and Results

The results demonstrate that HPT significantly enhances the fine-tuned policy performance by over 20% on unseen tasks, compared to several baseline models. Critical observations from the experiments include:

• Data Scaling: The performance improves with the increase in the number of trajectories and the diversity of datasets, affirming the hypothesis that diverse pre-training data leads to more generalized models.
• Model Scaling: Larger models with increased computational resources yield better validation losses up to a certain extent, suggesting that computational scalability is integral for improving model performance.
• Generalization to Simulation and Real-World Tasks: HPT shows robust performance across multiple simulation benchmarks and real-world settings. It outperforms baseline models in environments and tasks that were not part of the pre-training dataset.

Practical and Theoretical Implications

HPT's ability to align heterogeneous robotic data into a unified representation has profound implications for both practical applications and theoretical advancements in robotics and AI:

• Practical Implications: By leveraging heterogeneous datasets, HPT reduces the amount of task-specific data needed for training new policies, thereby lowering the overall costs associated with data collection and enabling faster deployment of robotic solutions across various applications.
• Theoretical Implications: The modular approach of HPT encourages further exploration into disentangling various aspects of robot learning, such as separating embodiment-specific knowledge from task-specific generalizations. This could lead to more robust and scalable models in other domains characterized by high heterogeneity.

Future Directions

The research opens several avenues for future work:

1. Enhanced Data Curation: Developing techniques for better data filtering and cleaning to improve the quality of datasets used for pre-training.
2. Advanced Training Objectives: Exploring alternative pre-training objectives beyond supervised learning, such as reinforcement learning or self-supervised learning, to further improve model performance.
3. Simulation and Real-World Integration: Creating unified simulation benchmarks with varying degrees of complexity and generalization challenges to systematically evaluate the models.
4. Extended Modalities: Incorporating additional sensor modalities like tactile data, 3D point clouds, and more diverse simulation environments.
5. Long-Horizon Tasks: Extending the architecture to handle longer-horizon tasks and complex manipulation scenarios.

Conclusion

Heterogeneous Pre-trained Transformers (HPT) represent a significant step towards addressing the heterogeneity challenge in robotic learning. The architecture's ability to leverage diverse datasets and generalize across different tasks and environments underscores its potential in advancing the field of robotics. The insights gained from this paper pave the way for building more generalized, robust, and scalable robotic policies, heralding a new era of data-driven robotic systems.