Development of collaborative strategies in joint action

Many tasks in daily life involve coordinating movements between two or more individuals. A couple of dancers, a team of players, two workers carrying a load or a therapist interacting with a patient are just a few examples. Acting in collaboration or joint action is a crucial human ability, and our sensorimotor system is shaped to support this capability efficiently. When two partners have different goals but may benefit from collaborating, they face the challenge of negotiating a joint strategy. To do this, first and foremost both subjects need to know their partner\u2019s state and current strategy. It is unclear how the collaboration would be affected if information about the partner is unreliable or incomplete. This work intends to investigate the development of collaborative strategies in joint action. To this purpose, I developed a dedicated experimental apparatus and task. I also developed a general computational framework \u2013 based on differential game theory \u2013 for the description and implementation of interactive behaviours of two subjects performing a joint motor task. The model allows to simulate any joint sensorimotor action in which the joint dynamics can be represented as a linear dynamical system and each agent\u2019s task is formulated in terms of a quadratic cost functional. The model also accounts for imperfect information about dyad dynamics and partner\u2019s actions, and can predict the development of joint action through repeated performance. A first experimental study, focused on how the development of joint action is affected by incomplete and unreliable information. We found that information about the partner not only affects the speed at which a collaborative strategy is achieved (less information, slower learning) but also optimality of the collaboration. In particular, when information about the partner is reduced, the learned strategy is characterised by the development of alternating patterns of leader-follower roles, whereas greater information leads to a more synchronous behaviour. Simulations with a computational model based on game theory suggest that synchronous behaviours are close to optimal in a game theoretic sense (Nash equilibrium). The emergence of roles is a compensation strategy which minimises the need to estimate partner\u2019s intentions and is, therefore, more robust to incomplete information. A second study addresses how physical interaction develops between adults with Autism spectrum disorder (ASD) and typically developing subjects. ASD remains mostly a mystery and has therefore generated some theories trying to explain their cognitive disabilities, which involve an impaired ability to interact with other human partners. Although preliminary due to the small number of subjects, our results suggest that ASD subjects display heterogeneity in establishing a collaboration, which can be only partly explained with their ability to perceive haptic force. This work is a first attempt to establish a sensorimotor theory of joint action. It may provide new insights into the development of robots that are capable of establishing optimal collaborations with human partners, for instance in the context of robot-assisted rehabilitation

Investigation of controllable multi electrode based FES (functional electrical stimulation) system for restoration of grasp-preliminary study on healthy individuals

Functional electrical stimulation applied via surface electrode can be used for hand rehabilitation particularly for enabling grasp in patients with stroke or spinal cord injury. The use of multi-pad electrode and multi-channel electrical stimulator based improve the effectiveness of conventional FES. Such a system consists of a multi-pad surface electrode and a matching multi-channel stimulator. This system will allow the targeting of motor neurons which activate muscle groups to produce corresponding functional movements of the hand. This paper presents our study on normal subjects to quantify the movement resulting from stimulation of electrodes spatially distributed around the forearm. The device was tested on four healthy subjects and the results show that multi-pad electrode provide desired amount of selectivity and can be used for generating functional grasp. The results also show that the effect of stimulation varies from person to person reflecting inters subject anatomical variability

Game theory and partner representation in joint action: toward a computational theory of joint agency

The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions (comparator model). In the study of joint action, models are even more necessary to understand the mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner’s actions and intentions (a ‘partner model’) and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for ‘joint’ and ‘self’ action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the ‘joint’ and ‘self’ motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions

Vinil Thekkedath Chackochan's Quick Files

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Modelling collaborative strategies in physical human-human interaction

none2noSystematic analysis of the mechanisms of physical human-human interaction (pHHI) requires adequate computational modelling framework. Recently differential game theory modelsâa multi-agent counterpart of optimal controlâhave been used to analyse sensorimotor collaborative strategies. A task can be defined by a pair of quadratic cost functionals (one per partner). The âplantâ is constituted by the two partnersâ body dynamics plus their mechanical links (if any). Every partner has his/her own sensory system. In analogy with single-agent dynamics, we assume that each partner maintains an internal model or state observer of own and partnerâs dynamics. The framework naturally incorporates the effects of noisy or incomplete sensory information about own body state. Different interaction strategies can be simulated, ranging from âoptimalâ collaboration (Nash equilibrium) to no collaboration (two separate LQG controllers). We compared the model predictions with an experimental scenario in which two partners have partly conflicting goalsâa reaching task with different via-points. This framework also reproduces behavioursâlike âslackingââthat are typical of the robot-human interaction in robot-assisted adaptation or rehabilitation trials.noneChackochan, Vinil Thekkedath*; Sanguineti, VittorioTHEKKEDATH CHACKOCHAN, Vinil; Sanguineti, Vittori