Researchers have explored human preferences for robot motion on a variety of household tasks. The study aimed to investigate whether preferences were similar between tasks, users, and if robots should behave in a human-like manner. The results found that preferences should be highly individualized, presenting a challenging future for integrating robots into everyday lives.
Robots are becoming increasingly common in the modern world, from industrial to domestic environments. As robots enter these new spaces, they should interact with users in ways that align with human preferences. Traditionally, this has been assumed to be mimicking human-like behaviors and motions, but a team of researchers at the University of Massachusetts Lowell is challenging this notion with a new experiment investigating human preference of robot motion.
This experiment allowed users to physically interact with robots in a series of experiments to determine their preference of motion. Not only did these experiments allow researchers to investigate user preference, but the robots also attempted to adjust their motion to the user preference over. This automatic adjustment allowed for individualized robot motion, which is a key step towards integrating robots into our daily lives.
In this experiment, users first provide demonstrations of a task, such as placing dishware into a dishwasher. Then, through a series of interactions, the robot learns and estimates the user preference, replicating the task according to these preferences. Once the preference is learned, a new task is demonstrated, such as closing the dishwasher. Again, preference is learned through a series of interactions but this time, the robot attempts to re-use the preference learned in a previous task. “What we are looking for is if different tasks can have similar preferences,” says lead researcher Brendan Hertel, “so that we can simplify the ways the robot has to learn to move. If preferences are more individualized, it makes learning harder.”
After performing these experiments, the results offer some key insights into human preferences for robot motion. The first is that preferences vary from task to task, meaning the preferred way of placing a dish in a dishwasher is unlike the preferred way of closing the dishwasher. This means that robots should learn individual motions for each task demonstrated, decreasing the viability of “one-size-fits-all” methods like state-of-the-art motion generation methods. Particularly, there was significant differences in the smoothness preferred by users for various tasks. This means that robot motions should adjust the smoothness of their motions according to the task at hand.
This experiment also investigated if users presented specific preferences, meaning motions should be individualized. As it turns out, different users have different preferences for different tasks. This means that motions should not only be adjusted according to the task at hand, but also to the user observing the task. “There may be many factors pertaining to someone’s preference for motion,” commented Brendan Hertel, “for example, in the U.S. they drive on the right but in the U.K. they drive on the left. What looks correct to someone may be completely wrong for someone else.”
Finally, this study looked at if user preferences aligned with human-like motion, as had been assumed in previous works. Overall, no such preference was found. The results indicated that “users do not perform demonstrations according to their preferences, and reproductions should be adjusted accordingly.” In fact, users wanted robots to move in a much smoother way than themselves.
While this work presents a step forward in understanding human preferences of robot motion and integrating robots with humans, there are some limitations. Notably, this study was limited to short tasks and used only a robotic arm, not the more human-like humanoid robots.
Also, further experiments and research are always needed. Understanding human preferences, especially with respect to robots, is an underdeveloped field that will only become more necessary as the impact of robots increases in our lives.
This paper “Automatic estimation and evaluation of multi-objective human preferences for Learning from Demonstration” was published in Robot Learning .
Hertel B, Nguyen T, Cabrera ME, Azadeh R. Automatic estimation and evaluation of multi-objective human preferences for Learning from Demonstration. Robot Learn. 2026(1):0006, https://doi.org/10.55092/rl20260006.
DOI: 10.55092/rl20260006
Robot Learning
Experimental study
Not applicable
Automatic estimation and evaluation of multi-objective human preferences for Learning from Demonstration
23-Mar-2026