Affiliation:
1. Integrated Group for Engineering Research, Centre for Information and Communications Technology Research, Universidade da Coruña, Coruña, Spain
2. Computer Vision Lab, University of Ljubljana, Ljubljana, Slovenia
Abstract
Achieving Lifelong Open-ended Learning Autonomy (LOLA) is a key challenge in the field of robotics to advance to a new level of intelligent response. Robots should be capable of discovering goals and learn skills in specific domains that permit achieving the general objectives the designer establishes for them. In addition, robots should reuse previously learnt knowledge in different domains to facilitate learning and adaptation in new ones. To this end, cognitive architectures have arisen which encompass different components to support LOLA. A key feature of these architectures is to implement a proper balance between deliberative and reactive processes that allows for efficient real time operation and knowledge acquisition, but this is still an open issue. First, objectives must be defined in a domain-independent representation that allows for the autonomous determination of domain-dependent goals. Second, as no explicit reward function is available, a method to determine expected utility must also be developed. Finally, policy learning may happen in an internal deliberative scale (dreaming), so it is necessary to provide an efficient way to infer relevant and reliable data for dreaming to be meaningful. The first two aspects have already been addressed in the realm of the e-MDB cognitive architecture. For the third one, this work proposes Perceptual Classes (P-nodes) as a metacognitive structure that permits generating relevant “dreamt” data points that allow creating “imagined” trajectories for deliberative policy learning in a very efficient way. The proposed structure has been tested by means of an experiment with a real robot in LOLA settings, where it has been shown how policy dreaming is possible in such a challenging realm.
Subject
Artificial Intelligence,Computational Theory and Mathematics,Computer Science Applications,Theoretical Computer Science,Software
Reference49 articles.
1. A modified firefly algorithm for the inverse kinematics solutions of robotic manipulators;Hernandez-Barragan;Integr Comput Aided Eng,2021
2. A three-step model for the detection of stable grasp points with machine learning;Schwan;Integr Comput Aided Eng,2021
3. Open-ended learning: A conceptual framework based on representational redescription;Doncieux;Front Neurorobot,2018
4. Lifelong robot learning;Thrun;Rob Auton Syst,1995
5. Sutton RS, Barto AG. Reinforcement learning: An introduction. MIT Press Cambridge; 1998. vol. 1.