Physical assistance enabled by haptic interaction is a fundamental modus for improving motor abilities, from a parent helping to guide their child during their first steps to a therapist supporting a patient. In research carried out with Ganesh Gowrishankar, Atsushi Takagi, Li Yanan, Katja Ivanova, Xiaoxiao Cheng and other colleagues, we investigate how human communicate through haptic cues when e.g. moving objects together, and how robots should help humans users to carry out such tasks.

Atsushi first showed that social factors influence force perception (Scientific Reports 2016). He further demonstrated that joint reaching movements are not appropriate to study haptic communication as both partners can largely use feedforward control to carry it out (PLoS ONE 2016). By examining the behaviours of two individuals when their right hands are physically connected, we could reveal how physical interaction with a partner changes one's own motor behaviour. In particular Ganesh could show that one improves with a better and even with a worse partner (Scientific Reports 2014), which suggests advantages of interactive paradigms for sport-training and physical rehabilitation. Surprisingly, the benefits of haptic communication increase with the number of connected partners (eLife 2019).

Atsushi also elucidated the neural mechanism underlying haptic communication through computational modeling: haptic information provided by touch and proprioception enables one to infer the partners motion planning and use it to improve one own motor performance (Nature Human Behaviour 2017). This model was experimentally verified by embodying it in a robot partner that induced the same improvements in motor performance as a human partner. Atsushi, then Hendrik, further clarified how the interaction mechanics influence haptic communication (PLoS Computational Biology 2018) where interacting partners inconspicuously adapt their muscles contraction to extract best sensory information from the interaction with the partner (J Neurophysiology 2023). Finally, testing with delayed interaction suggests that subjects model the delay to compensate for it (IEEE T Haptics 2021).

Katja Ivanova has systematically investigated the learning effects of haptic communication and the interaction with the robot partner. Human subjects cannot differentiate the interaction with the robot partner to the interaction with human partners, thus the robotic partner has passed this haptic Turing Test (OJEMB2020). Furthermore, both induce a (slight) benefit relative to training alone as tested one day and one week after training (Scientific Reports 2022). This contrasts with training using trajectory guidance along the target movement, which was more appreciated by the subjects of our experiments, but deteriorated the performance significantly after training as it modified the control behaviour.

Altogether, these results provide important new insights into the neural mechanism of physical interactions in humans, and promise versatile collaborative robot systems for human-like assistance. With Li Yanan, we are currently developing a game theory framework for human-human and human-robot interaction (PLoS ONE 2012, Nature Machine Intelligence 2019).

 

Related publications

•  A Melendez, L Bagutti, B Pedrono and E Burdet (2011), A versatile dual-wrist device to study human-human interaction and bimanual control. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems (IROS) 2578-83.

•  N Jarrasse, T Charalambous and E Burdet (2012), A Framework to describe, analyze and generate interactive motor behaviors. PLoS ONE 7(11): e49945.

• G Ganesh, A Takagi, R Osu, T Yoshioka, M Kawato and E Burdet (2014), Two is better than one: Physical interactions improve motor performance in humans. Scientific Reports 4: 3824.

• A Melendez-Calderon, V Komisar and E Burdet (2015), Interpersonal strategies for disturbance attenuation during a rhythmic joint motor action. Physiology and Behavior 147: 348-58.

•  A Takagi, C Bagnato and E Burdet (2016), Facing the partner influences tit-for-tat exchanges in force. Scientific Reports 6: 35397.

•  A Takagi, N Beckers and E Burdet (2016), Motion plan changes predictably in dyadic reaching. PLoS ONE 11.12.

•  A Takagi, G Ganesh, T Yoshioka, M Kawato and E Burdet (2017), Physically interacting individuals estimate the partners goal to enhance their movements. Nature Human Behaviour 1: 0054.

• A Takagi, F Usai, G Ganesh, V Sanguineti and E Burdet (2018), Haptic communication between humans is tuned by the hard or soft mechanics of interaction. PLoS Computational Biology 14(3): e1005971.

• Y Li, G Carboni, F Gonzalez, D Campolo and E Burdet (2019), How a robot can understand and adapt to human action - differential game theory for versatile physical interaction. Nature Machine Intelligence 1(1): 36-43.

• A Takagi, M Hirashima, D Nozaki and E Burdet (2019), Individuals physically interacting in a group rapidly coordinate their movement by estimating the collective goal. eLife 8: e41328.

• E. Ivanova, G. Carboni, J. Eden, J. Krüger and E. Burdet (2020), For Motion Assistance Humans Prefer to Rely on a Robot Rather Than on an Unpredictable Human. IEEE Open Journal of Engineering in Medicine and Biology 1: 133-139.

• E. Ivanova, J. Eden, S. Zhu, G. Carboni, A. Yurkewich and E. Burdet (2021), Short time delay does not hinder haptic communication benefits. IEEE Transactions on Haptics. doi: 10.1109/TOH.2021.3079227.

• E Ivanova, J Eden, Gerolamo Carboni, Joerg Krueger and E Burdet (2022), Interaction with a reactive partner improves learning in contrast to passive guidance. Scientific Reports 12(1): 1-10.

• H Boerner, G Carboni, X Cheng, S Hirche, S Endo and E Burdet (2023), Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception. Journal of Neurophysiology 129(2): 494-9.

• A Takagi, C Bagnato, A Melendez-Calderon, N Jarrasse, G Ganesh and E Burdet (2023), Competition increases the effort put into a physical interaction task. IEEE Transactions on Haptics (in press).