Rearrange Indoor Scenes for Human-Robot Co-Activity

Weiqi Wang1*, Zihang Zhao2,3*, Ziyuan Jiao1,2*, Yixin Zhu4, Song-Chun Zhu2,4, Hangxin Liu2

1UCLA Center for Vision, Cognition, Learning, and Autonomy

2National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence (BIGAI)

3College of Engineering, Peking University

4Institute for Artificial Intelligence, Peking University

*Equal contributors

[paper] [code]


We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot’s accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide an average of 14% more accessible space and 30% more objects to interact with. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.


Original                                   Rearranged

External Links

Z. Jiao, Y. Niu, Z. Zhang, S. -C. Zhu, Y. Zhu and H. Liu, "Sequential Manipulation Planning on Scene Graph," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 8203-8210, doi: 10.1109/IROS47612.2022.9981735.

Z. Zhang, Z. Jiao, W. Wang, Y. Zhu, S. -C. Zhu and H. Liu, "Understanding Physical Effects for Effective Tool-Use," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9469-9476, Oct. 2022, doi: 10.1109/LRA.2022.3191793.

Z. Jiao et al., "Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 979-985, doi: 10.1109/IROS51168.2021.9636351.

Z. Jiao et al., "Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective," 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic, 2021, pp. 8288-8294, doi: 10.1109/IROS51168.2021.9636554.

M. Han et al., "Reconstructing Interactive 3D Scenes by Panoptic Mapping and CAD Model Alignments," 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021, pp. 12199-12206, doi: 10.1109/ICRA48506.2021.9561546.