FlowDreamer: A RGB-D World Model with Flow-based Motion Representations for Robot Manipulation (2505.10075v1)

Abstract: This paper investigates training better visual world models for robot manipulation, i.e., models that can predict future visual observations by conditioning on past frames and robot actions. Specifically, we consider world models that operate on RGB-D frames (RGB-D world models). As opposed to canonical approaches that handle dynamics prediction mostly implicitly and reconcile it with visual rendering in a single model, we introduce FlowDreamer, which adopts 3D scene flow as explicit motion representations. FlowDreamer first predicts 3D scene flow from past frame and action conditions with a U-Net, and then a diffusion model will predict the future frame utilizing the scene flow. FlowDreamer is trained end-to-end despite its modularized nature. We conduct experiments on 4 different benchmarks, covering both video prediction and visual planning tasks. The results demonstrate that FlowDreamer achieves better performance compared to other baseline RGB-D world models by 7% on semantic similarity, 11% on pixel quality, and 6% on success rate in various robot manipulation domains.

FlowDreamer: Enhancing Robot Manipulation with RGB-D World Models

The paper entitled "FlowDreamer: A RGB-D World Model with Flow-based Motion Representations for Robot Manipulation" presents a novel architecture aimed at advancing visual world models for robotic manipulation tasks. This work provides a detailed investigation into the development of RGB-D world models, focusing on improving the prediction of future visual observations by incorporating explicit dynamics modeling.

Overview and Approach

The primary innovation of the FlowDreamer model lies in its modular framework, which separates the processes of dynamics prediction and visual rendering. Traditional methods often conflate these steps into a single model, potentially compromising the accuracy of dynamics prediction in favor of visual fidelity. FlowDreamer circumvents this limitation by employing a two-stage prediction framework that leverages 3D scene flow as motion representations, enhancing the model's capability to predict environmental dynamics accurately.

The architecture of FlowDreamer consists of two main components:

1. Dynamics Prediction Module: This component is tasked with predicting the 3D scene flow based on past visual frames and robotic actions using a U-Net architecture. The explicit modeling of 3D scene dynamics using the scene flow, which describes the motion of objects through a series of frames, provides improved supervision and understanding of physical interactions in a 3D space.
2. Future Generation Module: Utilizing a diffusion model, this module generates future visual observations conditioned on current RGB-D inputs and the predicted scene flow. The diffusion model focuses on rendering high-fidelity visual results, ensuring the next-frame prediction aligns closely with physical realism.

Despite its modular composition, FlowDreamer is trained end-to-end, allowing for cohesive optimization across components, fostering a robust world model capable of detailed and precise future predictions.

Experimental Analysis

FlowDreamer was evaluated across multiple benchmarks, including RT-1, Language Table, RoboDesk, and Robosuite, with the experimental setup encompassing video prediction and visual planning tasks. Compared to baseline models, FlowDreamer demonstrated superior performance, achieving improvements in semantic similarity (7%), pixel quality (11%), and success rate in robotic tasks (6%).

The differentiation from other models was particularly evident when examining semantic and pixel-based assessments. The explicit motion representation appeared instrumental in ensuring the continuity and accuracy of visual predictions, highlighting the potential of introducing explicit dynamics modeling into world model architectures.

Implications and Future Directions

The enhanced performance of FlowDreamer suggests promising implications for robotic manipulation and planning systems, emphasizing the necessity for detailed dynamics modeling in robotics applications. Through better prediction accuracy, robots can achieve more reliable and sophisticated control tasks, paving the way for advancements in autonomous systems.

Looking forward, this work opens avenues for integrating larger contextual inputs and historical data into world model architectures, potentially extending the capabilities of models like FlowDreamer. Additionally, optimizing diffusion models for faster inference without sacrificing quality remains a crucial challenge and opportunity for future research in AI-driven robotics.

In conclusion, FlowDreamer represents a significant step forward in embedding detailed motion understanding within RGB-D world models, offering substantial improvements in prediction accuracy and efficiency, with broad implications for the future of robotics and autonomous systems.