Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations (2412.14803v2)

Abstract: Visual representations play a crucial role in developing generalist robotic policies. Previous vision encoders, typically pre-trained with single-image reconstruction or two-image contrastive learning, tend to capture static information, often neglecting the dynamic aspects vital for embodied tasks. Recently, video diffusion models (VDMs) demonstrate the ability to predict future frames and showcase a strong understanding of physical world. We hypothesize that VDMs inherently produce visual representations that encompass both current static information and predicted future dynamics, thereby providing valuable guidance for robot action learning. Based on this hypothesis, we propose the Video Prediction Policy (VPP), which learns implicit inverse dynamics model conditioned on predicted future representations inside VDMs. To predict more precise future, we fine-tune pre-trained video foundation model on robot datasets along with internet human manipulation data. In experiments, VPP achieves a 18.6\% relative improvement on the Calvin ABC-D generalization benchmark compared to the previous state-of-the-art, and demonstrates a 31.6\% increase in success rates for complex real-world dexterous manipulation tasks. Project page at https://video-prediction-policy.github.io

• The paper presents a two-stage framework that uses fine-tuned video diffusion models to generate predictive visual representations, achieving over 28% improvement in task success rates.
• It incorporates a novel 'Video Former' to distill high-dimensional predictions into actionable features for a multi-task generalist robotic policy.
• Empirical evaluations demonstrate enhanced data efficiency and robust trajectory prediction, highlighting the method's promise for dynamic robotic control.

Overview of the Video Prediction Policy Paper

The paper introduces the Video Prediction Policy (VPP), a novel approach for robotic policy learning that leverages predictive visual representations extracted from video diffusion models (VDMs). The core motivation behind this work is to utilize the sequential image prediction capabilities of VDMs to enhance the robotic policy's ability to understand and act within dynamic environments. The VPP paradigm is articulated through the hypothesized effectiveness of what the authors term "predictive visual representations," which are thought to capture the physical dynamics and evolution of the environment, thus offering a richer informational basis for action selection than conventional frame-based encoders.

Key Contributions and Methodology

The paper presents a two-stage approach to developing the VPP. The first stage involves fine-tuning a text-guided video prediction model, initially pre-trained on large-scale datasets, across specific manipulation domains. This model extends the capabilities of existing VDMs by incorporating manipulation-specific training data sourced from a variety of environments, including large-scale human and robotic datasets. This step is crucial as it aligns the model's prediction capabilities with the nuances of robotic manipulation tasks.

In the second stage, the predictions from the VDM serve as refined visual representations, which are then used to condition a multi-task generalist robotic policy. This policy integrates a novel "Video Former" that distills these high-dimensional predictive features into a format suitable for action learning. The resulting architecture is a diffusion policy network, utilizing these encodings to generate and execute actions in a manner informed by predicted future states.

Empirical Evaluation and Results

The VPP demonstrates notable superiority over state-of-the-art methods across various benchmarks, including the Calvin ABC-D and real-world dexterous hand manipulation tasks. For instance, the VPP achieves over 28% improvement in task success rates on difficult benchmarks, a substantial margin that underscores the efficacy of leveraging predictive visual information. Additionally, the policy has shown enhanced data efficiency, performing competitively even when trained on reduced data subsets.

The results are corroborated by visualizations and quantitative evaluations that reveal the robust trajectory prediction capacities of the fine-tuned VDMs. The authors provide empirical evidence suggesting that these intermediate representations indeed capture crucial temporal information beneficial for sequential tasks.

Implications and Future Directions

The introduction of VPP enriches the broader landscape of robotic control by suggesting that visual prediction enhancements can significantly improve policy performance in complex environments. This methodology not only addresses the limitations of existing vision encoders but also sets the stage for integrating broader forms of predictive modeling into robotic policies.

Future work could explore the expansion of VPP to more varied robotic platforms and tasks, potentially incorporating additional sensory modalities or real-time adaptation mechanisms to further enhance performance. The pre-trained foundations and adaptable architecture of VPP also offer opportunities for synergy with other learning paradigms, including reinforcement learning and imitation learning, forging paths towards increasingly autonomous and adaptive robotic systems.

In conclusion, the Video Prediction Policy offers a compelling framework for robotic applications, driven by a nuanced understanding of predictive visual representations. It represents a meaningful step towards enhanced, generalist robotic policies that fluidly interpret and interact with their environments through informed action generation.