# Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
## 5 Discussion  
The research and application of RL in robotics have exploded over the past few years. However, its potential is still far from reaching, and certain limitations still hinder its use in industrial applications. The results of this paper show that RL alone can execute contact-rich manipulation tasks while avoiding potential collisions with a co-worker in an HRI scenario and has significant benefits over other current approaches. A significant benefit is the observed safe performance without the need to model human behavior. Refs. [55, 56] addressed controller safety by considering data-driven, such as RL, and model-based control theories. Data-driven approaches attempt to manage uncertainties and reduce the conservatism of the safe controller. Despite the uncertainty introduced by human behavior, the results show that accurate reward shaping and compliant robot behavior that halts its motion in case of collision are sufficient to ensure a safe HRI environment. Moreover, the proposed approach tries to avoid the activation of safety constraints, such as the safety stops established by the ISO/TS 15066:2016 [57] standard in collaborative robotics in the case, in any of the four collaboration modes defined by the standard, any requirement is violated. By executing the disassembly to the opposite side of the worker, due to the adaptive behavior of the agent to dynamic obstacles, the likelihood of a potential collision is reduced and, therefore, the need to execute a safety stop and stop the workflow. Another benefit is the agent's ability to generalize. The experiments carried out in reality not only show how the agent can generalize the disassembly to parts with different orientations but also the randomization of the friction between the parts in the learning \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Robot**} & \multicolumn{8}{c}{**2 \(\times\) 10\({}^{-4}\) m Tolerance Parts Disassembly**} \\ \cline{2-10}  & \multicolumn{2}{c}{\(-\)10\({}^{\circ}\)} & \multicolumn{2}{c}{\(-\)5\({}^{\circ}\)} & \multicolumn{2}{c}{0\({}^{\circ}\)} & \multicolumn{2}{c}{5\({}^{\circ}\)} & \multicolumn{2}{c}{**10\({}^{\circ}\)**} \\ \cline{2-10}  & **S** & **D** & **S** & **D** & **S** & **D** & **S** & **D** \\ \hline KUKA & 15/20 & 15/20 & 20/20 & 17/20 & 19/20 & 14/20 & 20/20 & 17/20 & 20/20 & 13/20 \\ LBR & (75\%) & (75\%) & (100\%) & (85\%) & (95\%) & (70\%) & (100\%) & (85\%) & (100\%) & (65\%) \\ \hline Franka & 14/20 & 14/20 & 18/20 & 16/20 & 18/20 & 17/20 & 20/20 & 15/20 & 19/20 & 14/20 \\ Emika & (70\%) & (70\%) & (90\%) & (80\%) & (90\%) & (85\%) & (100\%) & (75\%) & (95\%) & (70\%) \\ \hline \hline \end{tabular} \({}^{1}\) Static obstacle (5). \({}^{2}\) Dynamic obstacle (D).  
\end{table} 
Table 3: _Cont._process and the introduction of artifacts on the obstacle position measurements, favor the extraction of parts with different tolerances and the achievement of a robust policy in the face of noisy measurements in reality. The last aspect worth noting is creating the multi-control framework for the direct transfer of actions to the robot exploited by the proposed approach. Since ROS does not satisfy real-time requirements [53], a framework allowing direct control through an API developed in Python was proposed. The low latency in the system's communication infrastructure enables a shorter reaction time for the robot, being able to modify its trajectory to avoid a potential collision in a shorter time range. A natural concern of the research is related to the complexity of reward shaping. While many authors provide only sparse rewards during learning [35; 39], these are not usually successful when learning involves multiple goals. However, this does not necessarily imply the definition of multiple reward sources, each associated with a single goal. [PERSON] et al.