no code implementations • 24 Apr 2024 • Hakan Aktas, Yukie Nagai, Minoru Asada, Erhan Oztop, Emre Ugur
As a theoretical lens, affordances bridge the gap between effect and action, providing a nuanced understanding of the connections between agents' actions on entities and the effect of these actions.
no code implementations • 20 Oct 2023 • Hakan Aktas, Yukie Nagai, Minoru Asada, Erhan Oztop, Emre Ugur
To be specific, besides robots with similar morphologies with different degrees of freedom, we show that a fixed-based manipulator robot with joint control and a differential drive mobile robot can be addressed within the proposed framework.
no code implementations • 18 Jun 2021 • Alper Ahmetoglu, Emre Ugur, Minoru Asada, Erhan Oztop
To be concrete, in our simulation experiments, we either apply principal component analysis (PCA) or slow feature analysis (SFA) on the signals collected from the last hidden layer of a deep network while it performs a source task, and use these features for skill transfer in a new target task.
no code implementations • 6 Jul 2020 • Erhan Oztop, Minoru Asada
In this report, we show that all n-variable Boolean function can be represented as polynomial threshold functions (PTF) with at most $0. 75 \times 2^n$ non-zero integer coefficients and give an upper bound on the absolute value of these coefficients.
no code implementations • 1 Nov 2019 • Kyoichiro Kobayashi, Takato Horii, Ryo Iwaki, Yukie Nagai, Minoru Asada
This study proposes an extended framework called situated GAIL (S-GAIL), in which a task variable is introduced to both the discriminator and generator of the GAIL framework.
no code implementations • 21 Nov 2018 • Kazuki Tachikawa, Yuji Kawai, Jihoon Park, Minoru Asada
Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction.
no code implementations • 10 Oct 2017 • Ryo Iwaki, Minoru Asada
Monotonic policy improvement and off-policy learning are two main desirable properties for reinforcement learning algorithms.
no code implementations • 21 Oct 2014 • Matthias Rolf, Minoru Asada
Goals express agents' intentions and allow them to organize their behavior based on low-dimensional abstractions of high-dimensional world states.
no code implementations • 9 Dec 2011 • Sao Mai Nguyen, Masaki Ogino, Minoru Asada
To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos.