Hyogo Hiruma

Hyogo Hiruma, Hiroshi Ito, Hiroki Mori et al.

Sufficiently perceiving the environment is a critical factor in robot motion generation. Although the introduction of deep visual processing models have contributed in extending this ability, existing methods lack in the ability to actively modify what to perceive; humans perform internally during visual cognitive processes. This paper addresses the issue by proposing a novel robot motion generation model, inspired by a human cognitive structure. The model incorporates a state-driven active top-down visual attention module, which acquires attentions that can actively change targets based on task states. We term such attentions as role-based attentions, since the acquired attention directed to targets that shared a coherent role throughout the motion. The model was trained on a robot tool-use task, in which the role-based attentions perceived the robot grippers and tool as identical end-effectors, during object picking and object dragging motions respectively. This is analogous to a biological phenomenon called tool-body assimilation, in which one regards a handled tool as an extension of one's body. The results suggested an improvement of flexibility in model's visual perception, which sustained stable attention and motion even if it was provided with untrained tools or exposed to experimenter's distractions.

Xianbo Cai, Hideyuki Ichiwara, Hyogo Hiruma et al.

Robots operating in open, unstructured real-world environments must rely on onboard visual perception while autonomously moving across different locations. Continuous changes in onboard camera viewpoints cause significant visual scale variations in target objects, affecting vision-based motion generation. In this work, we present a stereo multistage spatial attention-based deep predictive learning method for real-time mobile manipulation. The proposed methods extracts task-relevant spatial attention points from stereo images and integrates them with robot states through a hierarchical recurrent architecture for closed-loop action prediction. We evaluate the system on four real-world mobile manipulation tasks using a mobile manipulator, including rigid placement, articulated object manipulation, and deformable object interaction. Experiments under randomized initial positions and visual disturbance conditions demonstrate improved robustness and task success rates compared to representative imitation learning and vision-language-action baselines under identical control settings. The results indicate that structured stereo spatial attention combined with predictive temporal modeling provides an effective solution within the evaluated mobile manipulation scenarios.

Hyogo Hiruma, Hiroshi Ito, Tetsuya Ogata

Training robots to operate effectively in environments with uncertain states, such as ambiguous object properties or unpredictable interactions, remains a longstanding challenge in robotics. Imitation learning methods typically rely on successful examples and often neglect failure scenarios where uncertainty is most pronounced. To address this limitation, we propose the Uncertainty-driven Foresight Recurrent Neural Network (UF-RNN), a model that combines standard time-series prediction with an active "Foresight" module. This module performs internal simulations of multiple future trajectories and refines the hidden state to minimize predicted variance, enabling the model to selectively explore actions under high uncertainty. We evaluate UF-RNN on a door-opening task in both simulation and a real-robot setting, demonstrating that, despite the absence of explicit failure demonstrations, the model exhibits robust adaptation by leveraging self-induced chaotic dynamics in its latent space. When guided by the Foresight module, these chaotic properties stimulate exploratory behaviors precisely when the environment is ambiguous, yielding improved success rates compared to conventional stochastic RNN baselines. These findings suggest that integrating uncertainty-driven foresight into imitation learning pipelines can significantly enhance a robot's ability to handle unpredictable real-world conditions.

Hyogo Hiruma, Hiroshi Ito, Hiroki Mori et al.

This study investigates the developmental interaction between top-down (TD) and bottom-up (BU) visual attention in robotic learning. Our goal is to understand how structured, human-like attentional behavior emerges through the mutual adaptation of TD and BU mechanisms over time. To this end, we propose a novel attention model $A^3 RNN$ that integrates predictive TD signals and saliency-based BU cues through a bi-directional attention architecture. We evaluate our model in robotic manipulation tasks using imitation learning. Experimental results show that attention behaviors evolve throughout training, from saliency-driven exploration to prediction-driven direction. Initially, BU attention highlights visually salient regions, which guide TD processes, while as learning progresses, TD attention stabilizes and begins to reshape what is perceived as salient. This trajectory reflects principles from cognitive science and the free-energy framework, suggesting the importance of self-organizing attention through interaction between perception and internal prediction. Although not explicitly optimized for stability, our model exhibits more coherent and interpretable attention patterns than baselines, supporting the idea that developmental mechanisms contribute to robust attention formation.

Hyogo Hiruma, Hiroki Mori, Hiroshi Ito et al.

Deep robot vision models are widely used for recognizing objects from camera images, but shows poor performance when detecting objects at untrained positions. Although such problem can be alleviated by training with large datasets, the dataset collection cost cannot be ignored. Existing visual attention models tackled the problem by employing a data efficient structure which learns to extract task relevant image areas. However, since the models cannot modify attention targets after training, it is difficult to apply to dynamically changing tasks. This paper proposed a novel Key-Query-Value formulated visual attention model. This model is capable of switching attention targets by externally modifying the Query representations, namely top-down attention. The proposed model is experimented on a simulator and a real-world environment. The model was compared to existing end-to-end robot vision models in the simulator experiments, showing higher performance and data efficiency. In the real-world robot experiments, the model showed high precision along with its scalability and extendibility.