Recently, collaborative robots, such as collaborative delivery robots, have been expected to improve the work efficiency of users. For natural human-robot collaboration, it is necessary to infer the appropriate goal position to transport instruments, where the user’s convenience and the surrounding environment are considered. In conventional research, the goal is inferred by demonstrating the user’s desired positions, but position demonstration requires many trials to obtain the inference model, which is burdensome for the user. Therefore, we focus on the user’s correction of the robot position and generate multiple position samples from the user’s corrective path. In addition, these position samples are weighted based on the implicit intention of the correction to learn both the desired and undesired positions. Consequently, the robot improves goal inference in fewer trials. The effectiveness of the proposed method was evaluated by experiment that simulated human-robot collaborative environments.