Effective motion planning is an indispensable prerequisite for the optimal performance of robotic manipulators in any task. In this regard, the research and application of reinforcement learning in robotic manipulators for motion planning have gained great relevance in recent years. The ability of reinforcement learning agents to adapt to variable environments, especially those featuring dynamic obstacles, has propelled their increasing application in this domain. Notwithstanding, a clear need remains for a resource that critically examines the progress, challenges, and future directions of this machine learning control technique in motion planning. This article undertakes a comprehensive review of the landscape of reinforcement learning, offering a retrospective analysis of its application in motion planning from 2018 to the present. The exploration extends to the trends associated with reinforcement learning in the context of serial manipulators and motion planning, as well as the various technological challenges currently presented by this machine learning control technique. The overarching objective of this review is to serve as a valuable resource for the robotics community, facilitating the ongoing development of systems controlled by reinforcement learning. By delving into the primary challenges intrinsic to this technology, the review seeks to enhance the understanding of reinforcement learning’s role in motion planning and provides insights that may suggest future research directions in this domain.

In the new industrial contexts, the workers’ well-being is the central pillar. Therefore, research on methods and technics to improve the workers’ user experience in a human–robot collaborative environment is necessary. While the effects of kinematic variables, such as speed and acceleration, on human safety have been extensively studied, their impact on human perception has not been fully explored. This study investigates the effects of the robot’s speed and acceleration on humans. An experimental research approach was used, where 20 participants (10 women and 10 men) performed an assembly task collaborating with a robot. An experiment was defined with two procedures, and the participants were evenly distributed: in the first experiment, the participants started by performing the task at a slow robot speed and then performed the same task at a faster speed. In the second experiment, the other half followed the opposite procedure. Key Performance Indicators (KPIs), physiological values (via EEG and EDA), and perceptual values (using the standardised UEQ-S questionnaire) were collected. The results showed that the robot’s speed and acceleration impact the task’s completion time and participants’ emotional responses. Our results lead to a new concept, “HRI speed bell”, which indicates that it is necessary to investigate the optimal speed and acceleration to ensure good trust and perceived safety. Furthermore, the task sequence also influences participants’ expectations and performance. Finally, the results are examined according to gender perspective.

The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker.