Successful human–robot interaction calls for fast generation of collision-free and optimized motions. To this end, sampling-based motion planning algorithms have been widely used. However, they often require long planning times to achieve optimized motions. While not being a critical issue in traditional industrial applications, planning time delays or poorly optimized motions have very negative effects on human–robot cooperation. Including artificial potential fields in the sampling algorithm can drastically improve the quality and planning time of such methods. Previous works in this direction are often tailored towards minimizing distance costs such as path length. In this work, we propose a heuristic based on potential fields that can also be used with a variety of state cost functions. We demonstrate the effectiveness of our approach using two cost functions related to human–robot interaction. We achieve drastically improved results in both scenarios. This allows for reducing total planning time and achieving a smoother interaction between human and robot.