94 citations · 323 across the 29 of their papers we have counts for
32 papers
Morphological Wobbling Can Help Robots Learn
Fabien C. Y. Benureau, Jun Tani
We propose to make the physical characteristics of a robot oscillate while it learns to improve its behavioral performance. We consider quantities such as mass, actuator strength,…
Initialization of Latent Space Coordinates via Random Linear Projections for Learning Robotic Sensory-Motor Sequences
Vsevolod Nikulin, Jun Tani
Robot kinematics data, despite being a high dimensional process, is highly correlated, especially when considering motions grouped in certain primitives. These almost linear correl…
Goal-directed Planning and Goal Understanding by Active Inference: Evaluation Through Simulated and Physical Robot Experiments
Takazumi Matsumoto, Wataru Ohata, Fabien C. Y. Benureau +1
We show that goal-directed action planning and generation in a teleological framework can be formulated using the free energy principle. The proposed model, which is built on a var…
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Pablo Lanillos, Cristian Meo, Corrado Pezzato +8
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning. Recently, it ha…
Emergence of sensory attenuation based upon the free-energy principle
Hayato Idei, Wataru Ohata, Yuichi Yamashita +2
The brain attenuates its responses to self-produced exteroceptions (e.g., we cannot tickle ourselves). Is this phenomenon, known as sensory attenuation, enabled innately, or acquir…
Goal-Directed Planning by Reinforcement Learning and Active Inference
Dongqi Han, Kenji Doya, Jun Tani
What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is in…