Learning 3D Dynamic Scene Representations for Robot Manipulation

• URL: http://arxiv.org/abs/2011.01968v2
• Date: Thu, 10 Dec 2020 16:53:29 GMT
• Title: Learning 3D Dynamic Scene Representations for Robot Manipulation
• Authors: Zhenjia Xu, Zhanpeng He, Jiajun Wu, Shuran Song
• Abstract summary: 3D scene representation for robot manipulation should capture three key object properties: permanency, completeness, and continuity. We introduce 3D Dynamic Representation (DSR), a 3D scene representation that simultaneously discovers, tracks, reconstructs objects, and predicts their dynamics. We propose DSR-Net, which learns to aggregate visual observations over multiple interactions to gradually build and refine DSR.
• Score: 21.6131570689398
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D scene representation for robot manipulation should capture three key object properties: permanency -- objects that become occluded over time continue to exist; amodal completeness -- objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity -- the movement of each object is continuous over space and time. In this paper, we introduce 3D Dynamic Scene Representation (DSR), a 3D volumetric scene representation that simultaneously discovers, tracks, reconstructs objects, and predicts their dynamics while capturing all three properties. We further propose DSR-Net, which learns to aggregate visual observations over multiple interactions to gradually build and refine DSR. Our model achieves state-of-the-art performance in modeling 3D scene dynamics with DSR on both simulated and real data. Combined with model predictive control, DSR-Net enables accurate planning in downstream robotic manipulation tasks such as planar pushing. Video is available at https://youtu.be/GQjYG3nQJ80.

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