Aprendizaje impulsado por la curiosidad de tareas conjuntas de locomoción y manipulación
Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks (Robotic Systems Lab: Legged Robotics at ETH Zürich)
Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks (Robotic Systems Lab: Legged Robotics at ETH Zürich)
Learning complex locomotion and manipulation tasks presents significant challenges, often requiring extensive engineering of, e.g., reward functions or curricula to provide meaningful feedback to the Reinforcement Learning (RL) algorithm. This paper proposes an intrinsically motivated RL approach to reduce task-specific engineering. The desired task is encoded in a single sparse reward, i.e., a reward of “+1” is given if the task is achieved. Intrinsic motivation enables learning by guiding exploration toward the sparse reward signal. Specifically, we adapt the idea of Random Network Distillation (RND) to the robotics domain to learn holistic motion control policies involving simultaneous locomotion and manipulation. We investigate opening doors as an exemplary task for robotic applications. A second task involving package manipulation from a table to a bin highlights the generalization capabilities of the presented approach. Finally, the resulting RL policies are executed in real-world experiments on a wheeled-legged robot in biped mode. We experienced no failure in our experiments, which con- sisted of opening push doors (over 15 times in a row) and manipulating packages (over 5 times in a row).
Paper: https://openreview.net/pdf?id=QG_ERxt...
Authors: Clemens Schwarke, Victor Klemm, Matthijs van der Boon, Marko Bjelonic, and Marco Hutter
Spin-off company: https://www.swiss-mile.com/
Tweet
No hay comentarios:
Publicar un comentario