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Collaborateurs humains et robots humanoides: contagions motrices et prise en charge de l'ensemble du corps

Authors: Vasalya, Ashesh;

Collaborateurs humains et robots humanoides: contagions motrices et prise en charge de l'ensemble du corps

Abstract

The work done in this thesis is about the interactions between human and humanoid robot HRP-2Kai as co-workers in the industrial scenarios. By interactions, we started with the non-physical human-robot interaction scenario based on an industrially inspired Pick-n-Place task example and then advanced towards the physical human-robot interactions with an example of human, humanoid robot dual-arm bi-directional object handover. The research topics in the thesis are divided into two categories. In the context of non-physical human-robot interactions, the studies conducted in the 1st part of this thesis are mostly motivated by social interactions between human and humanoid robot co-workers, which deal with the implicit behavioural and cognitive aspects of interactions. While in the context of physical human-robot interactions, the 2nd part of this thesis is motivated by the physical manipulations during object handover between human and humanoid robot co-workers in close proximity using humanoid robot whole-body control framework and locomotion. When an individual (human and robot) performs an action followed by the observation of someone’s action, implicit behavioural effects such as motor contagions causes certain features (kinematics parameters, goal or outcome) of that action to become similar to the observed action. However, previous studies have examined the effects of motor contagions induced either during the observation of action or after but never together; therefore, it remains unclear whether and how these effects are distinct from each other. We designed a paradigm and a repetitive task inspired by the industrial Pick-n- Place movement task, in first HRI study, we examine the effect of motor contagions induced in participants during (we call it on-line contagions) and after (off-line contagions) the observation of the same movements performed by a human, or a humanoid robot co-worker. The results from this study have suggested that off-line contagions affects participant’s movement velocity while on-line contagions affect their movement frequency. Interestingly, our findings suggest that the nature of the co-worker, (human or a robot), tend to influence the off-line contagions significantly more than the on-line contagions. Moreover, while the past studies have examined how the effects of induced motor contagions due to the observation of human and robot movements have affected either human co-worker’s movement velocity or how it affected movement variance but never both together. Therefore we argue that since precision in movements along with speed is the key in most industrial tasks, hence it is necessary to consider both task accuracy and task speed to measure the performance in a task accurately. Therefore in second HRI study, under the same paradigm and repetitive industrial task, we systematically varied the robot behaviour and observed viii whether and how the performance of a human participant is affected by the presence of the humanoid robot. We also investigated the effect of physical form of humanoid robot co-worker where the torso and head were covered, and only the moving arm was visible to the human participants. Later, we compared these behaviours with a human co-worker and examined how the observed behavioural effects scale with experience of robots. Our results show that the human and humanoid robot co-workers have been able to affect the performance frequencies of the participants, while their task accuracy remained undisturbed and unaffected. However, with the robot co-worker, this is true only when the robot head and torso were visible, and a robot made biological movements. Next, in pHRI study, we designed an intuitive bi-directional object handover routine between human and biped humanoid robot co-worker using whole-body control and locomotion, we designed models to predict and estimate the handover position in advance along with estimating the grasp configuration of an object and active human hand during handover trials. We also designed a model to minimize the interaction forces during the handover of an unknown mass object along with the timing of the object handover routine. We mainly focused on three important key features during the human humanoid robot object handover routine —the timing(s) of handover, the pose of handover and the magnitude of the interaction forces between human hand(s) and humanoid robot end-effector(s). Basically we answer the following questions, —when(timing), where (position in space), how(orientation and interaction forces) of the handover. Later, we present a generalized handover controller, where both human and the robot is capable of selecting either of their hand to handover and exchange the object. Furthermore, by utilizing a whole-body control configuration, our handover controller is able to allow the robot to use both hands simultaneously during the object handover. Depending upon the shape and size of the object that needs to be transferred. Finally, we explored the full capabilities of a biped humanoid robot and added a scenario where the robot needs to proactively take few steps in order to handover or exchange the object between its human co-worker. We have tested this scenario on real humanoid robot HRP-2Kai during both when human-robot dyad uses either single or both hands simultaneously.; Human-robot interaction is an emerging field which deals with the study andresearch of interactions between humans and robots Goodrich et al. [2008]. Thework done in this thesis is about the interactions between human and humanoidrobot HRP-2Kai as co-workers in the industrial scenarios. In the context of nonphysical human-robot interactions (HRI), the studies conducted in the 1st part of this thesis investigated the implicit behavioral effects of humanoid robot onthe behavior of human co-workers during an industrially inspired Pick-n-Placetask paradigm. In the context of physical human-robot interactions (pHRI), wedeveloped a novel bi-manual object handover framework using robot whole-bodycontrol and locomotion in the 2nd part of this thesis.The results from this study have suggested that off-line contagions affects participant’s movement velocity while on-line contagions affect their movement frequency. Interestingly, our findings suggest that the nature of the co-worker, (human or a robot), tend to influence the off-line contagions significantly more than the on-line contagions. Moreover, while the past studies have examined how the effects of induced motor contagions due to the observation of human and robot movements have affected either human co-worker’s movement velocity or how it affected movement variance but never both together. Therefore we argue that since precision in movements along with speed is the key in most industrial tasks, hence it is necessary to consider both task accuracy and task speed to measure the performance in a task accurately. Therefore in second HRI study, under the same paradigm and repetitive industrial task, we systematically varied the robot behaviour and observed viii whether and how the performance of a human participant is affected by the presence of the humanoid robot. We also investigated the effect of physical form of humanoid robot co-worker where the torso and head were covered, and only the moving arm was visible to the human participants. Later, we compared these behaviours with a human co-worker and examined how the observed behavioural effects scale with experience of robots. Our results show that the human and humanoid robot co-workers have been able to affect the performance frequencies of the participants, while their task accuracy remained undisturbed and unaffected. However, with the robot co-worker, this is true only when the robot head and torso were visible, and a robot made biological movements. Next, in pHRI study, we designed an intuitive bi-directional object handover routine between human and biped humanoid robot co-worker using whole-body control and locomotion, we designed models to predict and estimate the handover position in advance along with estimating the grasp configuration of an object and active human hand during handover trials. We also designed a model to minimize the interaction forces during the handover of an unknown mass object along with the timing of the object handover routine. We mainly focused on three important key features during the human humanoid robot object handover routine —the timing(s) of handover, the pose of handover and the magnitude of the interaction forces between human hand(s) and humanoid robot end-effector(s). Basically we answer the following questions, —when(timing), where (position in space), how(orientation and interaction forces) of the handover. Later, we present a generalized handover controller, where both human and the robot is capable of selecting either of their hand to handover and exchange the object. Furthermore, by utilizing a whole-body control configuration, our handover controller is able to allow the robot to use both hands simultaneously during the object handover. Depending upon the shape and size of the object that needs to be transferred. Finally, we explored the full capabilities of a biped humanoid robot and added a scenario where the robot needs to proactively take few steps in order to handover or exchange the object between its human co-worker. We have tested this scenario on real humanoid robot HRP-2Kai during both when human-robot dyad uses either single or both hands simultaneously.

Country
France
Related Organizations
Keywords

Transfert de tout le corps, Humanoid robot co-worker, Collaborateurs robots humains, Robotics, Human performance, Phri, Robotic behavior, Whole-Body handover, Hri, Physical human- robot interaction, Contagion motrice, [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], Motor contagion, [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics, Human-robot interaction

15 references, page 1 of 2

1 State of the art 3 1.1 Human-robot interaction (HRI) . . . . . . . . . . . . . . . . . . . . 3 1.1.1 Motor contagion . . . . . . . . . . . . . . . . . . . . . . . . 4 1.1.2 Motor contagion: a social influencer . . . . . . . . . . . . . . 5 1.2 Physical human-robot interaction (pHRI) . . . . . . . . . . . . . . . 6 1.2.1 Previous handover studies . . . . . . . . . . . . . . . . . . . 8 1.2.2 Proactive handover formulation . . . . . . . . . . . . . . . . 10

2 Distinct motor contagions 13 2.1 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.2 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.1.3 Experimental task and conditions . . . . . . . . . . . . . . . 15 2.1.4 HRP-2Kai movement trajectories . . . . . . . . . . . . . . . 17 2.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 Participant sample size . . . . . . . . . . . . . . . . . . . . . 19 2.2.3 Quantifying the off-line contagions . . . . . . . . . . . . . . 20 2.2.4 Quantifying the on-line contagions . . . . . . . . . . . . . . 21 2.2.5 Statistical correction . . . . . . . . . . . . . . . . . . . . . . 21 2.2.6 Movement congruency analysis . . . . . . . . . . . . . . . . 22 2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.1 Off-line contagions affect mean velocities but not htps . . . 22 2.3.2 On-line contagions affect htps and not mean velocities . . . 24 2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

n n [1] D. J. Agravante, A. Cherubini, A. Sherikov, P. Wieber, and A. Kheddar. Human-humanoid collaborative carrying. IEEE Transactions on Robotics, 35(4):833-846, Aug 2019. doi: 10.1109/TRO.2019.2914350.

[20] Etienne Burdet, Rieko Osu, David W Franklin, Theodore E Milner, and Mitsuo Kawato. The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature, 414(6862):446, 2001. [OpenAIRE]

[22] Antoine Bussy, Abderrahmane Kheddar, André Crosnier, and François Keith. Human-humanoid haptic joint object transportation case study. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3633-3638. IEEE, 2012.

[23] Maya Cakmak, Siddhartha S Srinivasa, Min Kyung Lee, Jodi Forlizzi, and Sara Kiesler. Human preferences for robot-human hand-over configurations. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1986-1993. IEEE, 2011.

[24] Maya Cakmak, Siddhartha S Srinivasa, Min Kyung Lee, Sara Kiesler, and Jodi Forlizzi. Using spatial and temporal contrast for fluent robot-human hand-overs. In 2011 6th ACM/IEEE International Conference on HumanRobot Interaction (HRI), pages 489-496. IEEE, 2011.

[25] Stéphane Caron and Abderrahmane Kheddar. Multi-contact walking pattern generation based on model preview control of 3d com accelerations. In IEEERAS International Conference on Humanoid Robots, November 2016. doi: 10.1109/HUMANOIDS.2016.7803329.

[26] Stéphane Caron, Abderrahmane Kheddar, and Olivier Tempier. Stair climbing stabilization of the hrp-4 humanoid robot using whole-body admittance control. arXiv preprint arXiv:1809.07073, 2018.

[27] Thierry Chaminade and Gordon Cheng. Social cognitive neuroscience and humanoid robotics. Journal of Physiology-Paris, 103(3-5):286-295, 2009.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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