Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto

Queißer J, Ishihara H, Hammer B, Steil JJ, Asada M. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. Presented at the International Conference on Development and Learning and on Epigenetic Robotics 2018 (ICDL-EPIROB2018), Tokyo

Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto

Queisser JF, Hammer B, Ishihara H, Asada M, Steil JJ. Skill Memories for Parameterized Dynamic Action Primitives on the Pneumatically Driven Humanoid Robot Child Affetto. In: 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE; 2018: 39-45.n this work, we propose an extension of parameterized skills to achieve generalization of forward control signals for action primitives that result in an enhanced control quality of complex robotic systems. We argue to shift the complexity of learning the full dynamics of the robot to a lower dimensional task related learning problem. Due to generalization over task variability, online learning for complex robots as well as complex scenarios becomes feasible. We perform an experimental evaluation of the generalization capabilities of the proposed online learning system through simulation of a compliant 2DOF arm. Scalability to a complex robotic system is demonstrated on the pneumatically driven humanoid robot Affetto including 6DOF

Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed

Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.Comment: Submitted to the International Journal of Robotics Research. Paper length is 21 pages (including references) with 12 figures. A video overview of the reinforcement learning experiment on the real robot can be seen at https://www.youtube.com/watch?v=WDq1rcupVM0. arXiv admin note: text overlap with arXiv:1710.0855