Action-conditioned video data improves predictability
This work addresses video prediction for robotics in dynamic environments, representing an incremental improvement by integrating action conditioning into existing frameworks.
The paper tackles the challenge of long-term video generation in partially observable scenarios by introducing the Action-Conditioned Video Generation (ACVG) framework, which uses a dual Generator-Actor architecture to generate video sequences conditioned on robot actions, and evaluates it on an indoor robot motion dataset with comparisons to state-of-the-art methods.
Long-term video generation and prediction remain challenging tasks in computer vision, particularly in partially observable scenarios where cameras are mounted on moving platforms. The interaction between observed image frames and the motion of the recording agent introduces additional complexities. To address these issues, we introduce the Action-Conditioned Video Generation (ACVG) framework, a novel approach that investigates the relationship between actions and generated image frames through a deep dual Generator-Actor architecture. ACVG generates video sequences conditioned on the actions of robots, enabling exploration and analysis of how vision and action mutually influence one another in dynamic environments. We evaluate the framework's effectiveness on an indoor robot motion dataset which consists of sequences of image frames along with the sequences of actions taken by the robotic agent, conducting a comprehensive empirical study comparing ACVG to other state-of-the-art frameworks along with a detailed ablation study.