VideoAgent: Self-Improving Video Generation (2410.10076v3)

Abstract: Video generation has been used to generate visual plans for controlling robotic systems. Given an image observation and a language instruction, previous work has generated video plans which are then converted to robot controls to be executed. However, a major bottleneck in leveraging video generation for control lies in the quality of the generated videos, which often suffer from hallucinatory content and unrealistic physics, resulting in low task success when control actions are extracted from the generated videos. While scaling up dataset and model size provides a partial solution, integrating external feedback is both natural and essential for grounding video generation in the real world. With this observation, we propose VideoAgent for self-improving generated video plans based on external feedback. Instead of directly executing the generated video plan, VideoAgent first refines the generated video plans using a novel procedure which we call self-conditioning consistency, allowing inference-time compute to be turned into better generated video plans. As the refined video plan is being executed, VideoAgent can collect additional data from the environment to further improve video plan generation. Experiments in simulated robotic manipulation from MetaWorld and iTHOR show that VideoAgent drastically reduces hallucination, thereby boosting success rate of downstream manipulation tasks. We further illustrate that VideoAgent can effectively refine real-robot videos, providing an early indicator that robots can be an effective tool in grounding video generation in the physical world. Video demos and code can be found at https://video-as-agent.github.io.

• The paper introduces the VideoAgent framework which uses self-conditioning consistency to iteratively refine video simulations with vision-language and real-world feedback.
• The approach significantly reduces hallucinations and physics inaccuracies, improving the realism and coherence of generated videos for robotic manipulation.
• Experiments on Meta-World and iTHOR demonstrate substantially higher task success rates compared to existing video generation models.

Overview of "VideoAgent: Self-Improving Video Generation"

The paper "VideoAgent: Self-Improving Video Generation" by Achint Soni et al. presents a novel approach to video generation intended to enhance robotic manipulation control tasks. The authors address significant challenges in existing video generation models, specifically the occurrence of hallucinations and physics inaccuracies that impair control when translated into robotic actions. This research introduces the VideoAgent framework, which incorporates a self-improving mechanism grounded in real-world feedback to mitigate these issues.

Problem Statement

The core limitation observed in current video generation models is their tendency to produce videos with unrealistic and inconsistent content. These shortcomings are particularly detrimental when videos are used as controls for robotic systems, leading to low task success rates in downstream applications. Although increasing dataset size can partially address these issues, it is not a comprehensive solution owing to the complexity of video semantics and physics integrity.

Key Contributions

1. VideoAgent Framework: The authors propose an iterative video refinement process termed self-conditioning consistency, which leverages feedback obtained from a vision-LLM (VLM). This process refines video plans, enhancing their realism and coherence before execution.
2. Incorporation of Feedback: VideoAgent uses two types of feedback: AI feedback from pretrained vision-LLMs and feedback from real-world execution, strengthening the grounding of simulated videos in the physical domain.
3. Demonstrated Efficacy: Experiments demonstrated in environments like Meta-World and iTHOR show that VideoAgent significantly reduces hallucination rates, drastically improving task success compared to existing baselines.

Experimental Evaluation

• Meta-World and iTHOR: The experiments illustrate substantial improvements in manipulation tasks using the proposed method. VideoAgent achieves higher success rates in complex tasks, outperforming previous architectures.
• Online Finetuning: The online component allows VideoAgent to use successful task executions to refine and finetune the video generation model further, showing iterative improvements over multiple refinement cycles.

Implications

The implications of this research stretch across both theoretical and practical dimensions. Theoretically, it paves the way for more robust models in video-based policy learning by suggesting a plausible integration of self-conditioning refinement processes. Practically, applications in autonomous robotic operations receive a direct enhancement from the improved video-to-action translation capabilities, leading to better operational reliability.

Future Prospects

Future advancements may explore integrating alternative action extraction mechanisms, such as inverse dynamics or goal-conditioned diffusion policies, to enhance adaptability across diverse robotic platforms. Another potential avenue is real-world deployment, augmenting model robustness in non-simulated environments.

Conclusion

Overall, the VideoAgent framework offers a significant stride towards resolving key limitations in video generation for control applications. By emphasizing grounded simulation and targeted video refinement, the approach sets a precedent for future exploration into more intricate and interactive simulation environments. The work could be a cornerstone for future developments in AI-driven robotics, promoting greater alignment between virtual simulations and physical realities.