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:为更好地容纳人类机器人共同活动,我们为室内家具的重新配置提出了一个最优化框架。重新配置的目的是为机器人活动提供足够的无障碍空间,同时不损害人类日常活动。为保持人类活动,我们的算法通过将空间和语义共生关系分别从SONGG和概念网中分离出来,维护家具之间的功能关系。通过以机器人可以穿越的开放空间数量和可以到达的物体数量来界定机器人的无障碍空间,我们将人类机器人共活的重新配置作为一个优化问题,通过适应性模拟Annealing(ASA)和共变矩阵适应进化演变战略(CMA-ES)来解决。我们在SONG数据集的实验从数量上表明,变异场平均提供了14%的无障碍空间和30%的可互动对象。后移场的质量通过人类研究得到定性验证,表明拟议战略的效力。</s>