Experience-Based Adaptation of Locomotion Behaviors for Kinematically Complex Robots in Unstructured Terrain
Alexander Dettmann, Anna Born, Sebastian Bartsch, Frank Kirchner
In In Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), (IROS-2015), 28.9.-01.10.2015, Hamburg, IEEE, pages 4504-4511, 2015. IEEE.

Zusammenfassung (Abstract) :

Kinematically complex robots such as legged robots provide a large degree of mobility and flexibility, but demand a sophisticated motion control, which has more tunable parameters than a general planning and decision layer should take into consideration. A lot of parameterizations exist which produce locomotion behaviors that fulfill the desired action but with varying performance, e.g., stability or efficiency. In addition, the performance of a locomotion behavior at any given time is highly depending on the current environmental context. Consequently, a complex mapping is required that closes the gap between robot-independent actions and robot-specific control parameters considering the environmental context and a given prioritization of performance indices. In the proposed approach, the robot learns from experiences made during its interaction with the environment. A knowledge base is created which links locomotion behaviors with performance features for visited contexts. This behavior library is utilized by a case-based reasoner to select motion control parameters for a desired action within the current context. The paper provides an overview of the control approach, the algorithms used to determine the current context and the robot&#8217;s performance, as well as a description of the reasoner which selects appropriate locomotion behaviors. In experiments, different behavior libraries were automatically built when operators had to control a walking robot manually through obstacle courses. Afterwards, the collected experiences and a trajectory follower were used to traverse an obstacle course autonomously. The provided experimental evaluation shows the performance dependency of the autonomous control with respect to different sizes and qualities of utilized behavior libraries and compares it to manual control.





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