Kinema4D: Kinematic 4D World Modeling for Spatiotemporal Embodied Simulation

TL;DR

Kinema4D introduces a novel 4D generative robotic simulator that models precise robot controls and environmental reactions, enabling realistic, geometry-consistent, and zero-shot transferable embodied simulations for advancing AI robotics research.

Contribution

The paper presents Kinema4D, a new 4D simulation framework that disentangles robot control and environmental reactions, along with a large-scale dataset Robo4D-200k for training and evaluation.

Findings

Effectively simulates physically-plausible interactions

Demonstrates geometry consistency and embodiment-agnostic behavior

Shows potential for zero-shot transfer in embodied simulation

Abstract

Simulating robot-world interactions is a cornerstone of Embodied AI. Recently, a few works have shown promise in leveraging video generations to transcend the rigid visual/physical constraints of traditional simulators. However, they primarily operate in 2D space or are guided by static environmental cues, ignoring the fundamental reality that robot-world interactions are inherently 4D spatiotemporal events that require precise interactive modeling. To restore this 4D essence while ensuring the precise robot control, we introduce Kinema4D, a new action-conditioned 4D generative robotic simulator that disentangles the robot-world interaction into: i) Precise 4D representation of robot controls: we drive a URDF-based 3D robot via kinematics, producing a precise 4D robot control trajectory. ii) Generative 4D modeling of environmental reactions: we project the 4D robot trajectory into a pointmap as a spatiotemporal visual signal, controlling the generative model to synthesize complex environments' reactive dynamics into synchronized RGB/pointmap sequences. To facilitate training, we curated a large-scale dataset called Robo4D-200k, comprising 201,426 robot interaction episodes with high-quality 4D annotations. Extensive experiments demonstrate that our method effectively simulates physically-plausible, geometry-consistent, and embodiment-agnostic interactions that faithfully mirror diverse real-world dynamics. For the first time, it shows potential zero-shot transfer capability, providing a high-fidelity foundation for advancing next-generation embodied simulation.