**1. Introduction and Motivation**

The agri-food industry, and particularly the meat industry, is one of the most dangerous industries when it comes to employee safety. Among the various occupations, slaughtering, cutting and meat processing operations require specific dexterity to handle sharp tools or dangerous machines. However, they also require physical strength to carry heavy loads such as pallets or carcasses, quarters or muscles of meat, or to perform deboning work tasks and cutting quarters into pieces of meat. Similarly, they require performing repetitive movements or working in a cold refrigerated room and humid environment. In fact, accidents at work are common and can occur at any time. Thus, the rate of these accidents and their frequency are among the highest, among all professions combined. In addition to work-related accidents, musculoskeletal disorders (MSDs) accounted for almost 91% of occupational illness cases in 2019 [1], with 842,490 days of temporary interruption of work for all sectors. A study carried out in the Brittany region of France showed that the agri-food industries are the most risky sectors in terms of MSDs [1]. For instance, at the French level, 30% of the declared MSDs are recorded in the meat sector.

Not only is this a major problem for employees, it is also a big problem for companies and society at large. In 2019, compensation for MSDs generated two billion euros in fees (social security estimate) in France, with an average of more than 21,000 euros for each MSD stoppage, not counting the daily allowances [2]. In some companies with 10 to 20% absenteeism, MSDs disrupt production and generate additional costs. In the meat sector, MSDs involve personnel at all stages. Thus, operators involved in meat cutting, represented by boners, parers and slicers, as well as those located at manufacturing and packaging stations are affected [3]. MSDs affecting the wrist, hand and fingers represent approximately 50% of all MSDs in the meat industry.

The arduousness of tasks related to physical effort, the repeatability of movements and the agri-food environment (cold, humidity and hygiene) encountered in the meat

sector deters the recruitment of young people, and ultimately few people, trained initially for this sector, remain there. This difficulty in recruiting or retaining young people leads to a significant shortage of manpower in these sectors. Technological evolution of certain workstations which would allow on the one hand a reduction of the arduousness, opening to the women certain activities which were until now reserved for the men, and on the other hand a revaluation of the trades, could change the outlook of the individuals in this sector of activity, give a positive image and promote its attractiveness.

Faced with these findings, the meat sector could soon integrate assistive and cobotic equipments to encourage companies to improve the quality of life at work of their employees. Cobotics, also known as collaborative robotics, is a technology that uses robotics, mechanics, electronics, and cognitive science to assist humans in their tasks. For the meat sector, the advantages of having cobotics are numerous:


In this paper, we propose the development of a proof-of-concept assistive strategy implemented through a collaborative robot in meat cutting tasks in order to reduce the musculoskeletal disorders on the wrist of human operators working in the meat industry. The developed impedance control strategy enables a KUKA LWR robot to provide assistive forces to a professional butcher while simultaneously allowing motion of the knife (tool) in all degrees of freedom. Previous robotic systems for autonomous meat handling [4] required one or several robots for performing very specific meat cutting operations. For instance, the ARMS system [5–7] was based on the separation of beef shoulder muscles, the GRIBBOT system [8] was applied to chicken breast fillet harvesting while the DEXDEB system [9,10] was useful for ham deboning. Therefore these robotic systems could not be reused for other meat handling tasks since the quality of their cut was not enough for other types of meat pieces. The new proposed robotic system (called Exoscarne) works on the principle of pHRI (physical Human-Robot Interaction) to solve this lack of generalization. From one side, the expertise of the skilled butcher is kept since the cutting trajectory is defined by the human operator, who is holding the tool at the same time as the robot. From the other side, the robot carries the load of the tool and increases the cutting force when touching the meat so that the effort applied by the human is smaller. This system results in a greater flexibility for different meat cutting tasks and can adapt itself to on-the-fly decisions made by the user.

Therefore, our new Exoscarne system, which will be described in the next sections (see Section 3 for its software components and Section 4 for its hardware components), is able to:


#### **2. Background on Robot Assistance**

One of the primary motivations of using robots for pHRI is their ability to share physical loads with their human partners. When the physical load of an object is shared or when the object is manipulated for a task, a natural division of effort between the human and the robot occurs. For example, load sharing can be for human-robot cooperative manipulation [11] or during rehabilitation [12]. Mortl et al. [13] proposed effort sharing policies for load sharing of an object by a human and multiple robots.

Some researchers focused on the larger question of selection of an 'assistance strategy' for a pHRI task. Dumora et al. [14] considered large object manipulation tasks in pHRI and proposed a library of robot assistances. Medina et al. [15] proposed a dynamic strategy selection between model-based and model-free strategies. The strategy selection is based on the concept of disagreement between the human and the robot, which in turn depends on the interaction force.

In the literature, the only example of load sharing of tool for a cooperative pHRI task, similar to meat cutting, was in [16] in which the author used a robot for assistive welding by supporting the weight of the welding equipment. In fact, most existing IADs, "Intelligent Assist devices" (i.e., active cobotics systems for human assistance) [17], are used in the automotive industry for the quasi-static collaborative transportation of heavy loads (e.g., motors, doors ...). However, these solutions are not suitable for the meat industry [18] since they can only assist through specific directions in the work-space [19] (while meat cutting requires complex 6D trajectories), they do not integrate safety solutions for handling dangerous tools (such as the knife for meat cutting) and they do not take into account the important dynamic non-linear effects of the meat cutting operations [20]. To the best of our knowledge, there is no prior pHRI related work which uses a robot for assisting a human for cutting meat by taking into account not only a classical force amplification strategy (such as in [20]) but also an intent prediction module in order to reduce the final forces to be applied by the human operator.

#### **3. Methodology**

#### *3.1. pHRI Assistive Strategies*

The main interaction controllers for pHRI are impedance and admittance control (see Section 3.2 for their mathematical definition). Controller stability issues are common with admittance control [21,22]. When a human holds the tool at the end-effector, it results in a coupled system that can lose stability if the human operator stiffens his arm muscles, leading to robot vibrations. Hence for this task we chose impedance control. Investigating the stability issues of admittance control is a field in itself and there are several heuristic methods in the literature [20]. Impedance control was possible as the robot had a torque sensor in each joint, enabling torque control. Admittance control is preferred with robots that have only position control, by using an external FT sensor. As the task involved motion in the cartesian space hence we used the cartesian impedance controller that is explained later. We devised two assistive strategies:

#### 1. Force amplification strategy

In this strategy we amplify the forces applied by the user on the knife's handle (see Section 4 for the experimental setup), detected by the FT sensor and input to our control scheme. The control diagram is shown in Figure 1. The appropriate parameter *η* has to be determined, as explained in Section 5.3.

#### 2. Intent prediction strategy

In this strategy we predict the forces to be applied by the user using RNN-LSTM networks explained in Section 3.3. The control diagram is shown in Figure 2.

**Figure 1.** Impedance controller diagram with amplification module for the force amplification strategy.

**Figure 2.** Impedance controller diagram with intent prediction module for intent prediction strategy.

Both Figures 1 and 2 are identical except for the module of their respective assistive strategies. In the force amplification strategy, the amplification module amplifies the human user's force input *Fh* at each time step through robot assistance *ηFh*. In the intent prediction strategy, a trained RNN-LSTM network takes the human user's force input *Fh* at each time step to anticipate the user's input for the next time step *Fpred* and provides this force via robot assistance. In both cases the user can haptically sense this assistance being provided by the robot.
