Mobile manipulators, which combine a mobile platform with a robotic arm, are versatile robots that can be used for a variety of tasks like logistic pick-and-placing, manufacturing or assembly. Compliant control for mobile manipulators could improve the safety of the users sharing their workspace with these robots. The two general methods of compliant control, admittance control and impedance control, require force/torque sensors, which are often not available on low-cost or lightweight robots. This report presents an adaption of impedance control, including a strategy to compensate for joint friction, that can be used on current-controlled robots without the use of force/torque sensors. A calibration method is designed for the arm, that enables estimation of the actuator's current/torque ratios and frictions, used by the adapted impedance controller. Software is developed to use the controller on a combination of the Kinova GEN3 Lite arm and the Clearpath Dingo Omnidirectional base. Real-world experiments with the arm show that the calibration method is consistent and that the designed controller is compliant while also being able to track targets with five-millimeter precision when no interaction is present. Experiments with the complete mobile manipulator showcase two new modes, both suitable for interaction with a human. The first is a guidance mode where the user can control the robot using interaction with the arm. The second is a tracking mode where the mobile manipulator tracks a moving target while still being compliant.

Socially compliant robot navigation in pedestrian environments remains challenging owing to uncertainty in human behavior and varying pedestrian preferences in different social contexts. Local optimization planners like Model Predictive Control can incorporate collision avoidance constraints, but they can only lead to socially compliant trajectories if the cost function embeds information about the desired social behavior. The same holds for Reinforcement Learning, where a sophisticated reward function needs to be defined. However, formalizing social behavior through a reward or cost function is difficult due to the complex nature of pedestrian behavior. Imitation learning allows for inferring the desired behavior by learning from human demonstrations, making them suitable for learning socially compliant navigation policies but without any safety considerations. In this work, we propose to learn a socially compliant navigation policy directly by observing surrounding pedestrians’ trajectories from a commonly available detection and tracking pipeline and combine it with a local optimization planner to enhance safety. A Subgoal Recommender policy is developed to guide the local optimization planner to generate socially compliant trajectories by providing intermediate subgoals. To adapt the policy to changing social contexts without forgetting previously learned information, we train the Subgoal Recommender in a Continual Learning setup exploiting new pedestrian data.
We demonstrate in simulation that our method can learn a policy that has similar performance metrics as that of the observed trajectories with 95% confidence estimated from a t-test, resulting in a lesser number of collisions. Further, the policy can adapt to different social preferences exhibited by pedestrians, while being able to remember the learned behavior in a previously encountered social context. Furthermore, we show that our proposed method can learn navigation policies from actual pedestrian data recorded using the onboard perception pipeline of a Clearpath Jackal robot.