3. Relevant Studies in Human-like Arm Motion Generation 

A literature review on the most recent techniques of human-like arm motion generation has been presented. The analysis included 54 papers that were firstly classified according to the sources of knowledge. The vast majority of the papers was found in Scopus, immediately followed by the IEEEXplore, Web of Science, and, with minor contribution, ACM Digital Library. The equipment of the included studies was roughly equally composed by simulators, robotic manipulators and anthropomorphic platforms. The latter devices are of particular impact on motion planning solutions because provide a significant level of anthropomorphism that can positively influence interactions with humans^[16][17]. The large majority of the reviewed papers proposed global methods that operate with kinematic variables and address the generation of trajectories in the operational space of robotic manipulators. This result might be due to the fact that a globally-considered static environment can provide solutions in a low dimensional space and a more accurate match with kinematic human-like features^[18][19][20]. However, such methods might be reductive and risk to ignore human-like characteristics of the joints space, such as synchrony and bell-shaped angular velocity profiles^[21]. Dynamics variables are often the focus of biologically-inspired solutions, which have been poorly addressed due to the difficulties that arise in transferring the activities of the muscles into robotic manipulators. Local human-like arm motion generation methods have also been scarcely proposed, but they can introduce the capability of simulating human reflexes and react to changing scenarios during the execution of a movement. However, these techniques only partially consider the workspace and, therefore, may fail to achieve human-likeness in complex tasks.

Roboticists have considered a variety of human motor control principles as inspirational and evaluational tools for proposing techniques of human-like arm motion generation. The included studies showed that researchers have mainly focused on biomimetic and human-robot mapping approaches, which can ensure a strong resemblance with human motion that is acknowledged as an objective reference of an action. In particular, human-robot transferring techniques can guarantee real-time execution of the movements, which is essential during tele-operation. However, most of these methods need expensive and complicated equipment for capturing human movements, databases to collect them and post-processing for features extraction. Additionally, these techniques generally lack of generalization because usually work for a limited range of goal-directed tasks. Similar issues are also experienced by learning-based methods to teach specific tasks to a robot by imitation or training. Although learning from observations ensures a stable repeatability and a degree of adaptation to external perturbations, generalization is seldom achieved.

The kinematics of the obtained end-effector trajectories have often been compared to the hand path and hand velocity profile of human reaching to show similarities and divergences between robotic and human movements. Most of these particular solutions were developed for single-arm motion and based on human-like optimization principles that minimize energy under tasks-related constraints. Manipulation in the workspace of a robot is often neglected when kinematics is assessed because of the inevitable complications that pick-and-place tasks would import into the analysis of human-likeness. The majority of the proposed methods were not tested with qualitatively assessments to understand the perception of human observers. However, there are many scientific shreds of evidence demonstrating that, for pleasant human-robot interactions and collaborations, the movements of the robot have to be perceived as natural, predictable, and showing the intention of the underlying action without any verbal communication ^{[12][22][23][24]}.

The smoothness of a trajectory has often been measured in relation with the third derivative of position, i.e., the jerk, which is a compact and intuitive index of human-likeness. However, smoothness is not exclusively connected with jerk, but it must be assessed more comprehensively with other validation systems, such as, for example, the number of movement units ^[24]. The application of the two-third power law (2/3-PL) is relatively simple for the generation of biologically-inspired planar movements, like writing or drawing. However, the 2/3-PL has shown to introduce systematic errors on the complete shape of a trajectory and needs to be extended to explain regularities of three-dimensional reaching movements ^[25]. Although path planarity in the operational space may significantly simplify a planning problem, this human motor principle should emerge from other independent human-like motor principles instead of being a constraint of the generation process. Otherwise, there is a risk of complicating a planning problem and decrease the overall performance. The repeatability of motion on imitating observed human movements can certainly increase predictability and simplify learning, but it can seriously undermine generalization on different tasks. With two optimization processes in sequence, the Spatio-Temporal Correspondence^[26] can negatively affect real-time interactions with humans, but it is a versatile tool that can be used to either assess or generate human-like body trajectories from one single sample. Human-like arm motion has also been achieved by the RULA-driven technique^[27], which introduced an upper limb assessment of the inverse kinematics and the sampling of the search space to enhance ergonomics, but ignored human-like time parametrizations.

Most of the included studies addressed single-arm reaching movements, while human-like dual-arm applications are emerging and pick-and-place tasks still deserve more attention. The simple nature of motion without manipulation and the vast literature concerning the psychology of human reaching have probably encouraged roboticists towards this direction of investigation. Replicating human writing, drawing, and rhythmic movements revealed to be a small research branch, which seems to be isolated because not of easy integration with more classical methods of picking, placing, and reaching motion generation.

The prevention of self-collisions and the avoidance of obstacles in the workspace of a robot were rarely considered in the reviewed papers, even though these are necessary features for interacting devices in human-centered environments. Additionally, many of the proposed solutions ignored a human-like kinematic analysis during the avoidance of obstacles and addressed collisions prevention under simplifying assumptions, which reduce their range of applicabilities.

Roboticists have usually defined the genesis of human-like motion as a connected system of different functional modules^[28] where optimization, learning, and control are integrated. However, the included studies do not refer to a common framework of motor generation for optimizing human motor models, learning internal models and control on external disturbances. On a local level, human motor minimization principles are applied, while learning techniques are used for clustering motion on a global level, for extracting regularities of captured human movements and for implementing decision-making settings. Studies have shown that the synthesis of motor solutions happens in the Central Nervous System before execution^[29]. However, the reviewed controllers act in the absence of prior optimized trajectories that may guide the process of on-line regulation. Moreover, these controllers are applied to a few degrees of freedom and remain to be tested on more complex redundant manipulators and interacting tasks.

A categorization of the included studies in accordance with their most peculiar methodological features permits to analyze their possible fields of application. With a significant presence in the current literature of human-like arm motion generation, biomimetic techniques for the generation of single-arm reaching movements certainly endow human operators with novel capabilities. For instance, for an efficient tele-operation of a robotic manipulator, accurate human-robot mapping methods for a real-time execution are necessary. Additionally, biomimetic methods privilege the mimicking of simple reaching motion because it is often sufficient for tele-operated tasks and the activation of different types of end-effector might not resemble typical human prehension. Due to the complexity of joint manipulation, dual-arm human-like mimicking is at its early stage of investigation and is expected to advance in the near future. While such techniques aim at directly augmenting human capabilities, the rest of the proposed methods aims at a more indirect augmentation, which passes through human-robot interactions that resemble human-human interactions. In these situations, arm movements are planned to achieve human-like kinematic characteristics and high levels of smoothness to meet the expectations of human observers and co-workers. A full autonomy of such human-like devices is achieved by, for instance, their capability of successfully accomplishing pick-and-place tasks in office-like and industrial-like scenarios, which are often cluttered with generic obstacles. The analysis of the included studies also showed that an advanced level of motor independence is also reached when internal human-like functionals are minimized, repetitive human-like behaviors are learned, and human-like reactions to external perturbations are controlled. It is also worth noticing that, in a broader sense, robots are more human-centered through the generation of human-like arm motion. Their ultimate services augment the human capabilities of action because their goal is not to replace human operators but, instead, to interact and collaborate with them.