Publikation
Stepping Locomotion for a Walking Excavator Robot Using Hierarchical Reinforcement Learning and Action Masking
Ajish Babu; Frank Kirchner
In: 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025). IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2025), October 18-25, Hangzhou, China, IEEE, 2025.
Zusammenfassung
The employment of walking excavator robots, endowed with hybrid locomotion capabilities, holds considerable promise in facilitating the execution of intricate tasks in challenging terrain environments. A critical skill for such a system pertains to traversing obstacles through stepping locomotion, a process entailing the momentary disengagement of the end-effectors from the ground. Existing solutions are encumbered by two significant limitations. Primarily, they are often too cumbersome to develop and implement due to the complexity of the problem formulations. Secondly, they present restrictions on the available avenues to influence the behavior, hindering the effective leveraging of domain knowledge to achieve the intended objective.