Although in the literature it is common to find predictors and inference systems that try to predict human intentions, the uncertainty of these models due to the randomness of human behavior has led some authors to start advocating the use of communication systems that explicitly elicit human intention. In this work, it is analyzed the use of four different communication systems with a human-robot collaborative object transportation task as experimental testbed: two intention predictors (one based on force prediction and another with an enhanced velocity prediction algorithm) and two explicit communication methods (a button interface and a voice-command recognition system). These systems were integrated into IVO, a custom mobile social robot equipped with force sensor to detect the force exchange between both agents and LiDAR to detect the environment. The collaborative task required transporting an object over a 5-7 meter distance with obstacles in the middle, demanding rapid decisions and precise physical coordination. 75 volunteers perform a total of 255 executions divided into three groups, testing inference systems in the first round, communication systems in the second, and the combined strategies in the third. The results show that, 1) once sufficient performance is achieved, the human no longer notices and positively assesses technical improvements; 2) the human prefers systems that are more natural to them even though they have higher failure rates; and 3) the preferred option is the right combination of both systems.
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Figure 1: Experiments setup. Top Left - Human-robot pair collaboratively transporting an aluminium bar. Goal marked with a chequered flag. Top Right - Scheme of the designed setup. At least eight routes to the goal. Control desk on the right with researcher managing the experiment and camera recording the point of view on the left. Bottom Left - Handle for better ergonomics with meaning of each button next to it. Only three buttons are used to tell the robot which route the human wants. Bottom Right - RODE Wireless GO microphone used for voice command recognition.
Figure 2: Assessment of objective measurements and election make by volunteers (predictor experiments). Left: Mean force exerted in orange, maximum force exerted in light blue and duration in yellow for the three experiments. Left axis in Newtons (both forces) and right axis in seconds (duration). Bars represent std. dev. Right: Election made by the 22 volunteers instead of valuate aspects numerically. Force predictor in dark red, velocity predictor in light red and draw in yellow.
Figure 3: Assessment of the main aspects involved in the interaction (predictor experiments). Left: Comparison among the baseline experiment (without any predictor) in gray, experiment with force predictor in dark red and with velocity predictor in light red. Valuation from 1 (very low) to 7 (very high). Statistical significance marked with *: p<0.05 , **: p<0.01 , ***: p<0.001 . Bars represent std. dev. Right: Election made by the 22 volunteers with respect to which system they prefer for the task at hand. Force predictor in dark red, velocity predictor in light red.
Figure 4: Assessment of the subjective easiness perceived by the user to indicate their intention. Left: Comparison among the three experiments performed in the first round: baseline, force predictor and velocity predictor. Middle: Comparison among the three experiments performed in the second round: baseline, with buttons and with voice commands recognition. Right: Comparison among the four experiments performed in the third round: baseline, velocity predictor, voice commands recognition and both systems. Valuation from 1 (very low) to 7 (very high). Statistical significance marked with *: p<0.05 , **: p<0.01 , ***: p<0.001 . Bars represent std. dev.
Figure 5: Assessment of objective measurements and election make by volunteers (direct communication experiments). Left: Mean force exerted in orange, maximum force exerted in light blue and duration in yellow for the three experiments. Left axis in Newtons (both forces) and right axis in seconds (duration). Statistical significance marked with *: p<0.05 . Bars represent std. dev. Right: Election made by the 23 volunteers instead of valuate aspects numerically. Buttons in dark blue, voice commands in light blue and draw in yellow.
Figure 6: Assessment of the main aspects involved in the interaction (direct communication experiments). Left: Comparison among the baseline experiment (without any communication system) in gray, experiment with buttons in dark blue and with voice commands in light blue. Valuation from 1 (very low) to 7 (very high). Statistical significance marked with *: p<0.05 , **: p<0.01 , ***: p<0.001 . Bars represent std. dev. Right: Election made by the 23 volunteers with respect to which system they prefer for the task at hand. Buttons in dark blue, voice commands in light blue.
Figure 7: Assessment of objective measurements and election make by volunteers (both systems experiments). Left: Mean force exerted in orange, maximum force exerted in light blue and duration in yellow for the four experiments. Left axis in Newtons (both forces) and right axis in seconds (duration). Statistical significance marked with *: p<0.05 , **: p<0.01 . Bars represent std. dev. Right: Election made by the 30 volunteers instead of valuate aspects numerically. Velocity predictor in light red, voice commands in light blue and both systems in green.
Figure 8: Assessment of the main aspects involved in the interaction (both systems experiments). Left: Comparison among the baseline experiment (without predictor nor voice commands) in gray, experiment with velocity predictor in light red, with voice commands in light blue and with both in green. Valuation from 1 (very low) to 7 (very high). Statistical significance marked with *: p<0.05 , **: p<0.01 , ***: p<0.001 . Bars represent std. dev. Right: Election made by the 30 volunteers with respect to which system they prefer for the task at hand. Velocity predictor in light red, voice commands in light blue and with both in green.
Department of Electrical and Computer Engineering, George Washington University · the Department of Electrical and Computer Engineering, Northeastern University · the Department of Mechanical and Aerospace Engineering, George Washington University
Chair of Perception for Intelligent Systems, Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany · Robotics Institute Germany (RIG) · Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, Sweden