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.

翻译：我们提出了一种基于优化的框架，用于重新排列室内家具以更好地适应人机共居活动。该重排旨在为机器人活动提供足够的可访问空间，同时不影响人类的日常活动。为保留人类活动，我们的算法通过分别整合从SUNCG和ConceptNet中提取的空间与语义共现信息，维持家具间的功能关系。通过将机器人可达空间定义为其可穿行的开放空间量及可接触的物体数量，我们将面向人机共居活动的重排建模为优化问题，并采用自适应模拟退火(ASA)与协方差矩阵自适应进化策略(CMA-ES)进行求解。在SUNCG数据集上的实验定量表明，重排后的场景平均提供了14%更多的可访问空间及30%更多的可交互物体。通过人类研究定性验证了重排场景的质量，证明了所提策略的有效性。