Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression (2502.04296v1)

Abstract: We propose Heterogeneous Masked Autoregression (HMA) for modeling action-video dynamics to generate high-quality data and evaluation in scaling robot learning. Building interactive video world models and policies for robotics is difficult due to the challenge of handling diverse settings while maintaining computational efficiency to run in real time. HMA uses heterogeneous pre-training from observations and action sequences across different robotic embodiments, domains, and tasks. HMA uses masked autoregression to generate quantized or soft tokens for video predictions. \ourshort achieves better visual fidelity and controllability than the previous robotic video generation models with 15 times faster speed in the real world. After post-training, this model can be used as a video simulator from low-level action inputs for evaluating policies and generating synthetic data. See this link https://liruiw.github.io/hma for more information.

Overview of "Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression"

The paper "Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression" introduces the Heterogeneous Masked Autoregression (HMA) model, a novel approach for efficiently modeling action-video dynamics in robotic systems. The model aims to address key challenges in scaling robot learning, such as the need for vast amounts of high-quality data and the difficulties in achieving real-time and high-fidelity evaluations. By leveraging heterogeneous pre-training across various robotic embodiments and tasks, HMA presents an innovative framework for generating synthetic data and evaluating policies in the robotics domain.

Key Contributions and Methods

HMA employs masked autoregression, a technique that has shown promise in other domains like LLMing and computer vision, to predict video sequences and action sequences. The approach combines elements of both discrete token generation and continuous diffusion models to capture the dynamics within heterogeneous datasets effectively. These datasets encompass over 3 million trajectories drawn from 40 distinct robotic embodiments, illustrating the model's capability to generalize across diverse settings.

The model builds on the concept of aligning heterogeneous actions into a shared latent space, enabling seamless generation and control of action-video dynamics across different robotic systems. This heterogeneity in action encompasses variations in action spaces, frequencies, and dimensions, a challenge that the model elegantly addresses.

Architecturally, HMA employs a modular design where separate modules handle action inputs and outputs, and a shared spatial-temporal transformer predicts dynamic outcomes. The integration of masked autoregression ensures that the model can efficiently and effectively handle the generation of both video and action sequences required for robotic tasks.

Experimental Findings

The paper's experimental results highlight the effectiveness of HMA in scaling across different dimensions:

1. Fidelity and Controllability: HMA demonstrates superior visual fidelity and controllability compared to previous state-of-the-art models, with significant improvements in metrics like PSNR, SSIM, and $\Delta$PSNR. The model's ability to generate action-conditioned video sequences contributes to its robust performance.
2. Efficiency: HMA shows a remarkable 15x speed improvement in inference latency compared to prior works, facilitating real-time interactions and applications in robotics. This efficiency stems from the model's use of autoregressive techniques that streamline the generation process.
3. Scalability: The model's performance benefits from scaling in the number of datasets, trajectories, and model sizes, with consistent improvements in both fidelity and controllability across these axes.
4. Synthetic Data Generation and Policy Evaluation: HMA is effectively used to generate synthetic data for policy training, leading to improved policy performance. Additionally, it serves as a real-time simulator for evaluating policies, showcasing its versatility in robotic applications.

Implications and Future Directions

The introduction of HMA has significant implications for the field of robotics and beyond:

• Enhanced Simulation Capabilities: By providing a robust framework for generating and simulating diverse robotic interactions, HMA can accelerate the development and evaluation of robotic systems in virtual environments, potentially reducing the need for costly and time-consuming real-world trials.
• Broader Applicability: The model's ability to generalize across heterogeneous datasets indicates its potential applicability to other domains requiring dynamic modeling and simulation.
• Future Research: The paper suggests further investigation into long-horizon planning, model predictive control, and the use of autoregressive policies in real-world robotic systems as promising directions for future work.

In summary, the HMA framework represents a significant step forward in the efficient and scalable modeling of action-video dynamics, providing valuable tools and insights for advancing robotic learning and simulation. As research in this area progresses, HMA may play a crucial role in bridging the gap between virtual simulations and real-world robotic applications.