Learning to Role-Switch in Multi-Robot Systems

We present an approach that uses Q-learning on individual robotic agents, for coordinating a mission-tasked team of robots in a complex scenario. To reduce the size of the state space, actions are grouped into sets of related behaviors called roles and represented as behavioral assemblages. A role is a Finite State Automata such as Forager, where the behaviors and their sequencing for finding objects, collecting them, and returning them are already encoded and do not have to be relearned. Each robot starts out with the same set of possible roles to play, the same perceptual hardware for coordination, and no contact other than perception regarding other members of the team. Over the course of training, a team of Q-learning robots will converge to solutions that best the performance of a well-designed handcrafted homogeneous team

Planiranje robotskog djelovanja primjenom principa "pojačanog učenja"

Proces učenja koji proizlazi kao odgovor na vizualnu spoznaju okoline polazna je odrednica brojnih istraživanja iz područja robotike te umjetne inteligencije. Proces planiranja djelovanja autonomnog robota nad neuređenim skupom objekata obrađen je u ovom radu koristeći principe pojačanog učenja. Korištene su Metode Privremenih Razlika uz primjenu linearnih baznih funkcija za aproksimaciju vrijednosne funkcije stanja zbog prevelikog broja diskretnih stanja u kojim se sustav može naći. Cilj je pronaći optimalan slijed akcija kojima agent (robot) premješta predmete dok ne postigne unaprijed definirano ciljno stanje. Algoritam je podijeljen u dva dijela. U prvom dijelu cilj je naučiti parametre kako bi mogli pravilno aproksimirati Q funkciju, dok se u drugom dijelu algoritma iskorištavaju dobiveni parametri za definiranje slijeda akcija koje se šalju UR robotu pomoću TCP protokola. Pojačano učenje pokazalo se prikladnim za navedeni problem te su rezultati prikazani na slikama (26) i (27). Pošto je u radu korišten dvodimenzionalni pristup problemu u vidu budućeg rada postoji mogućnost modificiranja algoritma za kreiranje 3D prostornih struktura

Peripersonal Space in the Humanoid Robot iCub

Developing behaviours for interaction with objects close to the body is a primary goal for any organism to survive in the world. Being able to develop such behaviours will be an essential feature in autonomous humanoid robots in order to improve their integration into human environments. Adaptable spatial abilities will make robots safer and improve their social skills, human-robot and robot-robot collaboration abilities. This work investigated how a humanoid robot can explore and create action-based representations of its peripersonal space, the region immediately surrounding the body where reaching is possible without location displacement. It presents three empirical studies based on peripersonal space findings from psychology, neuroscience and robotics. The experiments used a visual perception system based on active-vision and biologically inspired neural networks. The first study investigated the contribution of binocular vision in a reaching task. Results indicated the signal from vergence is a useful embodied depth estimation cue in the peripersonal space in humanoid robots. The second study explored the influence of morphology and postural experience on confidence levels in reaching assessment. Results showed that a decrease of confidence when assessing targets located farther from the body, possibly in accordance to errors in depth estimation from vergence for longer distances. Additionally, it was found that a proprioceptive arm-length signal extends the robot’s peripersonal space. The last experiment modelled development of the reaching skill by implementing motor synergies that progressively unlock degrees of freedom in the arm. The model was advantageous when compared to one that included no developmental stages. The contribution to knowledge of this work is extending the research on biologically-inspired methods for building robots, presenting new ways to further investigate the robotic properties involved in the dynamical adaptation to body and sensing characteristics, vision-based action, morphology and confidence levels in reaching assessment.CONACyT, Mexico (National Council of Science and Technology