Coupling Mobile Base and End-Effector Motion in Task Space

Dynamic systems are a practical alternative to motion planning in executing robot actions. They are of particular interest in Learning from Demonstration, as here we aim to carry out actions in a certain fashion, without a model or in-depth knowledge about the world, which might be difficult to achieve with a planner. Using model-based dynamic systems in task space enables robots to flexibly reproduce demonstrated actions. Nevertheless, when dealing with mobile manipulators, we face the challenge of including the kinematic constraints of the robot in the action models. In this paper we propose to couple robot base and end-effector motions generated by arbitrary dynamical systems modulating the base velocity, while respecting the robots kinematic design. To this end we learn an approximation of the inverse reachability in closed form. In real-world robot experiments we demonstrate that we are able to maintain kinematically feasible trajectories in the presence of obstacles and in configurations differing profoundly from the training scene.

Authors

• Tim Welschehold
• Christian Dornhege
• Fabian Paus
• Tamim Asfour
• Wolfram Burgard

Keywords

• End effectors
• Trajectory
• Task analysis
• Grippers
• Dynamics
• Planning
• Motion In Space
• Task Space
• End-effector Motion
• System Dynamics
• Dynamical
• Reachable
• Real-world Experiments
• Presence Of Obstacles
• Kinematic Constraints
• Inverse Reinforcement Learning
• Robot Base
• Motion Generation
• Training Scenes
• Mobile Manipulator
• Parametrized
• Major Axis
• Gaussian Process
• Kriging
• Obstacle Avoidance
• Pitch Angle
• Mobile Platform
• Trajectory Generation
• Door Handle
• Static Obstacles
• Inverse Kinematics
• Robot Configuration
• Base Position
• Roll Angle