{"id":377,"date":"2021-09-20T14:28:41","date_gmt":"2021-09-20T14:28:41","guid":{"rendered":"http:\/\/merlin.deib.polimi.it\/?p=377"},"modified":"2023-04-20T16:53:04","modified_gmt":"2023-04-20T14:53:04","slug":"cobots-key-allies-to-increase-productivity-and-mitigate-stress","status":"publish","type":"post","link":"https:\/\/merlin.deib.polimi.it\/?p=377","title":{"rendered":"Cobots: key allies to increase productivity and mitigate stress"},"content":{"rendered":"\n

How many times do we feel exhausted after an intense workday? Maybe we did our best at work and then we feel like our own physical and mental resources just disappeared\u2026 or we feel like we weren\u2019t able to operate at maximum efficiency, inevitably collapsing into a heap of perceived failure.<\/p>\n\n\n\n

In today\u2019s society the modern work paradigm often seems to ask us to do more in less time, and do it well. However, this \u201cfast-and-well\u201d work culture intrinsically incorporates the elements that, in the long run, can lead us to be less productive, or undermine our psycho-physiological safety. This might not only entail serious productivity loss but also lead workers to burnout, overstress and absenteeism.<\/p>\n\n\n\n\n\n\n\n

So, where can we buy the ticket for a workday filled with less stress and more throughput? Is it just utopia the idea of simultaneously improving both workers\u2019 well-being<\/strong> (reducing stress in the workplace) and job performance<\/strong>? Although this may seem unrealistic, it\u2019s not. Today the collaborative robot (a.k.a. cobot<\/strong>) can be a key ally to help the industrial shop-floor worker achieve both these objectives!<\/p>\n\n\n\n

How? The key answer is enclosed in the worlds of game theory<\/strong>, strategic decision making <\/strong>and reinforcement learning<\/strong>. But let\u2019s proceed one step at a time. According to the psycho-physiological theory, the relationship between stress and performance is described by an inverted U-shaped curve<\/strong>, as illustrated hereafter. This means that, while too little stress results in boredom and demotivation (calm) and seems not to boost our productivity, moderate stress levels are beneficial in terms of performance (eustress), but only up to a certain point. After that, the increase of stress has a negative impact on both our safety and performance (distress), leading to mental exhaustion and poor productive capabilities.<\/p>\n\n\n\n

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Too little stress demotivates, too much destroys, but the right amount gets the job done.<\/figcaption><\/figure>\n\n\n\n

So, stress and performance are strongly interrelated and staying on top of the curve (where just the right amount of stress allows us to achieve the peak performance) is a delicate balancing act and \u2013of course\u2013 a matter of trade-offs and equilibria!<\/p>\n\n\n\n

Wouldn\u2019t it be great if the cobot could help us reach this optimal stress-performance point? Here\u2019s how to do that.<\/p>\n\n\n\n

At Politecnico di Milano<\/a>, we enabled the cobot<\/strong> to autonomously adjust its behaviour<\/strong> during the interaction with the human co-worker to simultaneously increase<\/strong> his\/her performance<\/strong> and mitigate<\/strong> work-related stress<\/strong>.<\/p>\n\n\n\n

The very first ingredient to let the cobot realize this strategy is to monitor in real-time the stress and the performance of the human worker.  Then, to express the trade-off between the human stress minimization and performance maximization, we borrow some elements of game theory.<\/p>\n\n\n\n

Indeed, game theory<\/strong> is the science that studies the optimal decision-making of competing and independent agents (a.k.a. players<\/strong>) in strategic frameworks.  The core of game theory is the so-called \u2018game\u2019<\/strong>, which is used as a model of the interaction between rational players. Each player is then a strategic decision-maker within the situation exemplified by the game. Given the circumstances that can arise within the game, each player decides which action to take among a predefined set. The combination of simultaneous game actions played by the agents is called \u2018game outcome\u2019<\/strong>. This represents a possible state that the players can reach during their interaction. For each game outcome, each agent receives a payout (a.k.a. \u2018payoff\u2019<\/strong> or \u2018utility\u2019<\/strong>), i.e. a numeric value quantifying the player\u2019s level of satisfaction with respect to that outcome.<\/p>\n\n\n\n

In human-robot collaborative frameworks, typically, a human operator and a cobot cooperate to accomplish a certain task. Clearly, the way they behave during cooperation influences the human attitudinal state in terms of stress and performance. Unfortunately, this influence cannot be predicted a priori.<\/p>\n\n\n\n

So, game theory provides the theoretical framework to model how the actual human-robot interaction is affecting the stress and performance of the human worker. To realize the model of this trade-off, we reinterpret the interaction between human and robot as a game between two self-interested agents: H and R. These are used to express the two competing aspects of whatever working framework:  be productive on one side, and keep stress low on the other side. As displayed in the following figure, we assume the goal of player R is to maximize productivity and the one of player H is to minimize stress. Then, we exploit the game actions to express the possible behaviours adopted by the human and the robot during the interaction. Indeed, we assume that each player can adapt<\/strong> to the other one (i.e. playing action D) when it is aligning to the goal of the other player or do not adapt<\/strong> (i.e. playing action ND), in the opposite case.<\/p>\n\n\n\n

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Game theoretic representation of the \u201cproduce more, stress less\u201d paradigm in human-robot collaborative settings.<\/figcaption><\/figure>\n\n\n\n

So, this game-theoretic model allows us to evaluate the admissible stress-performance compromises (outcomes) that the human-robot interaction can produce. Besides, it also let us identify the particular game outcome (Nash equilibrium) that represents the local equilibrium state of the game and a \u201cno regrets\u201d situation for the agents. This is due to the fact that, by definition, the Nash Equilibrium is a game outcome that, once reached, means that no player can increase its payoff by changing actions unilaterally. Thus, in the considered case, the Nash Equilibrium give us the chance to identify the local stress-performance equilibrium compromise.<\/p>\n\n\n\n

But how to make the cobot apply the right behaviour to optimize this trade-off?<\/p>\n\n\n\n

This time the answer is reinforcement learning<\/strong>. Basically, we penalize or reward the robot behaviour proportionally to its positive or negative influence on the human attitudinal state in terms of stress and performance. The game model serves to evaluate this effect. In our Lab we applied this approach and validated it in a real human-robot collaborative assembly task, as shown in the following video.<\/p>\n\n\n\n

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