Human-in-the-loop humanoid experiment
A new Torobo humanoid robot controlled by PV-RNN (Ahmadi & Tani, 2018) with using force feedback control can interact with human dynamically. We are examining various emergent phenomena observed in the human-in-the-loop experiment. This experiment has been conducted by Ferreira Chame Hendry and Jun Tani.
Spontaneous interactions between two robots
Humanoid Robot OP2 performs imitative interaction through learning. They shows spontaneous turn-taking and switching of movement patterns. This experiment has been conducted by Jungsk Hwang, Nadine Wirkuttis, and Jun Tani.
Predicting future and reflecting past in terms of visuo-proprioceptive patterns
A simulated humanoid robot experiment using a predictive-coding and active inference model for hierarchical and associative learning of visuo-proprioceptive sequential patterns. This experiment has been conducted by Jungsk Hwang.
A humanoid robot learns to acquire a set of primitive behaviors and their combinations. The robot experiment utilizes MTRNN model which is characterized by its composition of fast and slow dynamics parts.
iCub robot controlled by MTRNN
iCub robot is controlled by MTRNN. This was done by Martin Peniak at Univ. of Plymouth
sdr chaos 2
A humanoid robot spontaneously generates sequences of learned primitives. Chaos self-organized in the higher level of artificial brain (MTRNN model) generates pseudo stochastic sequences of moving an object among left, middle and right positions on a table.
Mental rehearsing and planning based on partially learned behavioral experiences. Visual stream image is generated for actually experienced ones as well as hallucinated ones.
Pathology of schizophrenia reconstructed in a humanoid robot
Pathology of schizophrenia (delusion of control) is reconstructed in a humanoid robot. The delusion of control is manifested under malfunction of top-down and bottom-up interactions in MTRNN.
A mobile robot with a hand learns to associate primitive sentences and corresponding behaviors with certain level of generalization. In the video, a robot, by recognizing a sentence “hit red”, generated the corresponding behavior. The robot was implemented with RNNPB model.