DepthWorld: ロボットマニピュレーションのための3Dワールドモデル
DepthWorld: 3D World Model for Robot Manipulation
ロボット操作向けに、多視点RGBと深度を同時予測する3DワールドモデルDepthWorldを提案し、DROIDデータセットをキャリブレーションしたDROID-3Dで学習することで、RGB予測精度の向上と正確なメートル深度推定を実現した。
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著者: Jai Bardhan, Josef Sivic, Vladimir Petrik
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
World models offer a data-driven alternative to traditional simulators for robotics, with applications spanning policy evaluation, improvement, and planning. All of these uses depend on faithful 3D geometry, yet current video-based world models are trained on RGB alone and produce rollouts that look correct frame-by-frame but do not compose into a consistent 3D world. Closing this gap requires progress on two fronts: large-scale 3D supervision for manipulation, and an architecture that can absorb it without disturbing strong pretrained video priors. We introduce a calibration pipeline that combines learned stereo depth with a joint factor graph, pooling all episodes collected from the same physical robot to recover its shared kinematic parameters alongside per-scene extrinsics. Applied to the DROID dataset, this yields DROID-3D, a calibrated 3D dataset providing dense metric depth and recalibrated multi-view extrinsics (achieving <0.7 px reprojection error on 90% of episodes for external cameras). We then train DepthWorld, a Stable Video Diffusion-based world model that jointly predicts multi-view RGB and depth via spatial latent tiling, leaving the pretrained Variational Autoencoder (VAE) unchanged. Depth supervision improves RGB prediction itself by +1.48 dB PSNR over an identical RGB-only baseline at equal training budget, while simultaneously yielding accurate metric depth for downstream geometric reasoning.