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It is well-known that a deep understanding of co-workers' behavior andpreference is important for collaboration effectiveness. In this work, wepresent a method to accomplish smooth human-robot collaboration in closeproximity by taking into account the human's behavior while planning therobot's trajectory. In particular, we first use an occupancy map to summarizehuman's movement preference over time, and such prior information is thenconsidered in an optimization-based motion planner via two cost items asintroduced in [1]: 1) avoidance of the workspace previously occupied by human,to eliminate the interruption and to increase the task success rate; 2)tendency to keep a safe distance between the human and the robot to improve thesafety. In the experiments, we compare the collaboration performance amongplanners using different combinations of human-aware cost items, including theavoidance factor, both the avoidance and safe distance factor, and a baselinewhere no human-related factors are considered. The trajectories generated aretested in both simulated and real-world environments, and the results show thatour method can significantly increase the collaborative task success rates andis also human-friendly. Our experimental results also show that the costfunctions need to be adjusted in a task specific manner to better reflecthuman's preference.

Considering Human Behavior in Motion Planning for Smooth Human-Robot Collaboration in Close Proximity