51 citations · 91 across the 28 of their papers we have counts for
28 papers
Integrated Altruistic and Fairness Preference Induces Advanced Mutual Cooperation in Sequential Social Dilemmas
Yu Wei, Yukiko Ogura, Yoshiyuki Ohmura +3
Inducing cooperation among distributed agents is still a difficult problem in the field of multi-agent reinforcement learning (MARL), particularly in social dilemma situations. The…
Closure of Self-Determining System Based on Causal and Constitutive Relations
Yoshiyuki Ohmura, Earnest Kota Carr, Yasuo Kuniyoshi
A self-determining system is defined as one in which causes originating within the system influence the system itself. This definition raises the question of how to specify system…
Causal Stance
Yoshiyuki Ohmura, Yasuo Kuniyoshi
What is the meaning of physical causal closure? Jaegwon Kim explicitly adopts a conception of causation according to which physical causation is effectively identified with determi…
Unsupervised Learning of Inter-Object Relationships via Group Homomorphism
Kyotaro Ushida, Takayuki Komatsu, Yoshiyuki Ohmura +1
While current deep learning models achieve high performance by learning statistical correlations from vast datasets,which stands in stark contrast to human learning. They lack the…
Transformation Categorization Based on Group Decomposition Theory Using Parameter Division
Takayuki Komatsu, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Representation learning seeks meaningful sensory representations without supervision and can model aspects of human development. Although many neural networks empirically learn use…
Defining causal mechanism in dual process theory and two types of feedback control
Yoshiyuki Ohmura, Yasuo Kuniyoshi
Mental events are considered to supervene on physical events. A supervenient event does not change without a corresponding change in the underlying subvenient physical events. Sinc…
Exploration-assisted Bottleneck Transition Toward Robust and Data-efficient Deformable Object Manipulation
Yujiro Onishi, Ryo Takizawa, Yoshiyuki Ohmura +1
Imitation learning has demonstrated impressive results in robotic manipulation but fails under out-of-distribution (OOD) states. This limitation is particularly critical in Deforma…
Minimal Set of Questions for Theories of Consciousness: Toward a Unified Explanatory Framework
Yoshiyuki Ohmura, Yasuo Kuniyoshi
A central challenge in consciousness research is the lack of agreement on what a theory of consciousness should explain, which makes it difficult to compare existing theories. We p…
A Mathematical Formalization of Self-Determining Agency
Yoshiyuki Ohmura, Earnest Kota Carr, Yasuo Kuniyoshi
Defining agency is an extremely important challenge for cognitive science and artificial intelligence. Physics generally describes mechanical happenings, but there remains an unbri…
Avoiding Epiphenomenalism in Theories of Consciousness: A Causal Framework Based on Asymmetry
Yoshiyuki Ohmura, Yasuo Kuniyoshi
Why do some physical systems possess consciousness, while others do not? A scientific theory of consciousness must explain differences in system behavior in terms of well-defined i…
Imitation Learning for Active Neck Motion Enabling Robot Manipulation beyond the Field of View
Koki Nakagawa, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Most prior research in deep imitation learning has predominantly utilized fixed cameras for image input, which constrains task performance to the predefined field of view. However,…
Feature-Based Lie Group Transformer for Real-World Applications
Takayuki Komatsu, Yoshiyuki Ohmura, Kayato Nishitsunoi +1
The main goal of representation learning is to acquire meaningful representations from real-world sensory inputs without supervision. Representation learning explains some aspects…
Learning Conditionally Independent Transformations using Normal Subgroups in Group Theory
Kayato Nishitsunoi, Yoshiyuki Ohmura, Takayuki Komatsu +1
Humans develop certain cognitive abilities to recognize objects and their transformations without explicit supervision, highlighting the importance of unsupervised representation l…
Gaze-Guided Task Decomposition for Imitation Learning in Robotic Manipulation
Ryo Takizawa, Yoshiyuki Ohmura, Yasuo Kuniyoshi
In imitation learning for robotic manipulation, decomposing object manipulation tasks into sub-tasks enables the reuse of learned skills and the combination of learned behaviors to…
Unsupervised categorization of similarity measures
Yoshiyuki Ohmura, Wataru Shimaya, Yasuo Kuniyoshi
In general, objects can be distinguished on the basis of their features, such as color or shape. In particular, it is assumed that similarity judgments about such features can be p…
Enhancing Reusability of Learned Skills for Robot Manipulation via Gaze Information and Motion Bottlenecks
Ryo Takizawa, Izumi Karino, Koki Nakagawa +2
Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning ha…
Multi-task real-robot data with gaze attention for dual-arm fine manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
In the field of robotic manipulation, deep imitation learning is recognized as a promising approach for acquiring manipulation skills. Additionally, learning from diverse robot dat…
Ablation Study to Clarify the Mechanism of Object Segmentation in Multi-Object Representation Learning
Takayuki Komatsu, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Multi-object representation learning aims to represent complex real-world visual input using the composition of multiple objects. Representation learning methods have often used un…
Mental causation in a physical world: A self-causation model of downward causation
Yoshiyuki Ohmura, Yasuo Kuniyoshi
Downward causation is self-causation, the causel effect from the whole to its parts, and is considered a promising theory for the problem of mental causation. However, it remains t…
An algebraic theory to discriminate qualia in the brain
Yoshiyuki Ohmura, Wataru Shimaya, Yasuo Kuniyoshi
The mind-brain problem is to bridge relations between in higher-level mental events and in lower-level neural events. To address this, some mathematical models have been proposed t…
Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer
T. Takada, W. Shimaya, Y. Ohmura +1
An effective way to model the complex real world is to view the world as a composition of basic components of objects and transformations. Although humans through development under…
Goal-conditioned dual-action imitation learning for dexterous dual-arm robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Long-horizon dexterous robot manipulation of deformable objects, such as banana peeling, is a problematic task because of the difficulties in object modeling and a lack of knowledg…
Memory-based gaze prediction in deep imitation learning for robot manipulation
Heecheol Kim, Yoshiyuki Ohmura, Yasuo Kuniyoshi
Deep imitation learning is a promising approach that does not require hard-coded control rules in autonomous robot manipulation. The current applications of deep imitation learning…
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer
Heecheol Kim, Yoshiyuki Ohmura, Akihiko Nagakubo +1
Deep imitation learning is promising for robot manipulation because it only requires demonstration samples. In this study, deep imitation learning is applied to tasks that require…