Abstract: Methods and systems for controlling a robot. In one aspect, the method (1300) comprises obtaining (s1302) first input information associated with a first simulation environment to which a first level of realism is assigned and obtaining (s1304) second input information associated with a second simulation environment to which a second level of realism is assigned. The first level of realism is different from the second level of realism. The method further comprises associating (s1306) the first input information with a first realism value representing the first level of realism; and associating (s1308) the second input information with a second realism value representing the second level of realism. The method further comprises modifying (s1310), based on the associated first input information and the associated second input information, one or more parameters of a machine learning (ML) process used for controlling the robot.

Abstract: A system (1) for emulating remote control of a physical robot (5) via a wireless network (11) is disclosed. The system comprises a control module (2) for determining a trajectory for the physical robot and generating trajectory control data that comprises velocity data and positional data based on the determined trajectory. The system further comprises a first control loop (3), comprising a first feed forward controller (4). The first feed forward controller is configured to receive the trajectory control data, send, via the wireless network (11), and a first velocity command to a first control interface of the physical robot (5). The first velocity command is based on the trajectory data. The first feed forward controller (4) is further configured to receive, via the wireless network, a first set of sensor data from physical robot.