Physically interacting individuals estimate the partner’s goal to enhance their movements

Abstract

From a parent helping to guide their child during their first steps, to a therapist 15 supporting a patient, physical assistance enabled by haptic interaction is a fundamental modus 16 for improving motor abilities. However, what movement information is exchanged between 17 partners during haptic interaction, and how this information is used to coordinate and assist 18 others remains unclear 1 . Here, we propose a model where haptic information, provided by touch 19 and proprioception 2 , enables interacting individuals to estimate the partner's movement goal, 20 and to improve their own motor performance. We utilize an empirical physical interaction task 3 21 to show that our model can explain human behaviours better than existing models of interaction 22 in literature [4][5][6][7][8] . Furthermore, we experimentally verify our model by embodying it in a robot 23 partner and checking that it induces the same improvements in motor performance and learning 24 in a human individual as interacting with a human partner. These results promise collaborative 25 robots that provide human-like assistance, and suggest that movement goal exchange is the key 26 to physical assistance. 27 Humans are adept at physically interacting with and assisting one another, from helping 28 children to walk, to the incredible feats of balance in acrobatics, and synchrony during the 29 Tango. For over a decade, physical coupling has been documented to promote partners to adopt 30 specialised roles 9-11 and enable pairs or dyads to improve in many joint tasks 3,9,12,13 . However, 31 the underlying computational principle that enables movement coordination is still unknown 1 . 32 The improvement in interacting partners has been shown not to be due to changes in attention or 33 impedance of the interacting limbs 3 , and is absent when the interaction is not physical 14,15 and 34 when the interacting partners do not fully share control 16 . These results highlight the importance 35 of haptics, the sensory modality related to tactile and proprioceptive senses 2 , during continuous 36 physical interactions, and suggest that individuals jointly coordinate with a partner by 37 exchanging information haptically. However, what information is being exchanged and how it 38 is used to adapt one's behaviour remains unknown. We hypothesised that haptically interacting 39 partners can estimate and exchange sensory information about the task goal and the uncertainty 40 of this information with their partner, and tested this hypothesis against competing models of 41 haptic interaction during an interactive target tracking task. 42 Specifically, we first simulated the mechanical dynamics and control behaviour of 43 2 individual partners during the interactive task. We considered our proposed interpersonal goal 44 integration model against three well-known models of interaction in literature that propose 45 different information being exchanged between the partners. We compared the prediction of 46 these...

Differential game theory for versatile physical human–robot interaction

“…On the other hand, the study of physical tasks between humans has revealed how the capability to understand the partner's sensorimotor control and to adapt one's own control is key to interaction benefits [16]. Some recent papers have studied how finite games can deal with limited knowledge of the partner [17,18], but it is unclear how the available techniques should be transferred to the continuous control of the interaction with a robot.…”

Incomplete information about the partner affects the development of collaborative strategies in joint action

“…The rapid emergence of stable coordination in rigid 445 coupling may be due to the fact that the partner actions are easier to predict and a 446 joint coordination strategy is simple to develop as the two subjects shared the same 447 task. However, dyads need more time to develop an interaction strategy when the task 448 is more challenging [4,10] or -as in the present study -when the subjects have different 449 and partly conflicting goals.…”

Optimizing motor decision-making through competition with opponents

“…For over a decade, sub-optimal and risk-seeking behaviours have been repeatedly confirmed in studies of motor decision-making tasks with an asymmetric gain function 4,5,6,8 , that require a choice with different variances of pay-off 2,3,7,9 and involve a speed-accuracy trade-off 10 we assessed the potential effect of interaction with opponents on sub-optimal motor decision-making with a prediction that other people's actions/intentions can influence the subjects' motor system 10,11,20,21 . First, we found that the subjects' risk-seeking strategy in the individual task reversed to risk-averse strategy at the very beginning of the competitive task ( Fig.…”