## 1 Introduction  
Logistics applications demand the development of flexible, safe and dependable robotic solutions for part-handling including efficient pick-and-place solutions.  
Pick and place are basic operations in most robotic applications, whether in industrial setups (e.g., machine tending, assembling or bin picking) or in service robotics domains (e.g., agriculture or home). In some structured scenarios, picking and placing is a mature process. However, that is not the case when it comes to manipulating parts with high variability or in less structured environments. In this case, picking systems only exist at laboratory level, and have not reached the market due to factors such as lack of efficiency, robustness and flexibility of currently available manipulation and perception technologies. In fact, the manipulation of goods is still a potential bottleneck to achieve efficiency in the industry and the logistic market.  
At the same time, the market demands more flexible systems that allow for a reduction of costs in the supply chain, increasing the competitiveness for manufacturers and bringing a cost reduction for consumers. The introduction of robotic solutions for picking in unstructured environments requires the development of flexible robotic configurations, robust environment perception, methods for trajectory planning, flexible grasping strategies and human-robot collaboration.  
This study focused specifically on the development of adaptive trajectory planning strategies for pick and place operations using mobile manipulators. A mobile manipulator is a mobile platform carrying one or more manipulators. The typical configuration of mobile manipulators in industry is an anthropomorphic manipulator mounted on a mobile platform complemented with sensors (laser, vision, and ultrasonic) and tools to perform operations. The mobility of the base extends the work-space of the manipulator, which increases the operational capacity.  
Currently, mobile manipulators implementation in industry is limited. One of the main challenges is to establish coordinated movements between the base and the arm in a unstructured environment depending on the application.  
Although navigation and manipulation are fields where much work has been done, mobile manipulation is a less known area because it suffers from the difficulties and uncertainties of both previously mentioned fields. We propose to solve the path planning problem using a reinforcement learning (RL) strategy. The aim is to avoid explicit programming of hard-to-engineer behaviours, or at least to reduce it, taking into account the difficulty of foreseeing situations in unstructured environments. RL-based algorithms have been successfully applied to robotic applications [1] and enable learning complex behaviours only driven by a reward signal.  
Specifically, this study focused on learning to navigate to such a place that the mobile manipulator's arm is able to pick an object from a table. Due to the limited scope of the arm, not all positions near the table are feasible to pick the object and to calculate those positions analytically is not trivial. The goal of our work was to evaluate the performance of deep reinforcement learning (DRL) [2] to acquire complex control policies such as mobile manipulator positioning by learning only through the interaction with the environment. Specifically, we compared the performance of two model-free DRL algorithms, namely Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimisation (PPO) algorithms, in two simulation tests.  
The simulated robot we used is the mobile manipulator miwa, depicted in Figure 1, which has been used in industrial mobile manipulation tasks (e.g., [3]).  
Figure 1: Kuka miwa.