In the context of human-robot collaboration in close proximity, safety and comfort are the two important aspects to achieve joint tasks efficiently. For safety, the robot must be able to avoid dynamic obstacles such as a human arm with high reliability. For comfort, the trajectories and avoidance behavior of the robot need to be predictable to the humans. Moreover, these two aspects might be different from person to person or from one task to another. This work presents a framework to generate predictable motions with dynamic obstacle avoidance for the robot interacting with the human by using policy improvement method. The trajectories are generated using Dynamic Motion Primitives with an additional potential field term that penalizes trajectories that may lead to collisions with obstacles. Furthermore, human movements are predicted using a data-driven approach for proactive avoidance. A cost function is defined which measures different aspects that affect the comfort and predictability of human co-workers (e.g., human response time, joint jerk). This cost function is then minimized during human-robot interaction by the means of policy improvement through black-box optimization to generate robot trajectories that adapt to human preferences and avoid obstacles. User studies are performed to evaluate the trust and comfort of human co-workers when working with the robot. In addition, the studies are also extended to various scenarios and different users to analyze the task transferability. This improves the learning performance when switching to a new task or the robot has to adapt to a different co-worker.

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