Embodiment Selection and Distance-Aware World Models for Mobile Manipulation
2024年1月1日
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1 min read
We study world-model-based reinforcement learning for mobile manipulation that jointly addresses embodiment selection (when to move the base vs. use the arm) and motion planning. By introducing reachability rewards and distance-aware rewards, we move beyond similarity-based latent-space rewards toward reliable motor-skill acquisition for separately controlled mobile-manipulation behaviors.
Related papers:
- Xiaoxu Feng, Takato Horii. “Distance-Aware World Model-based Reinforcement Learning for Mobile Manipulation Behaviors”. IEEE Access, Early Access, 2026.
- Xiaoxu Feng, Takato Horii, Takayuki Nagai. “Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors”. IEEE Robotics and Automation Letters, February 2025.
- Xiaoxu Feng, Takato Horii, Takayuki Nagai. “Predictive Reachability for Embodiment Selection in Mobile Manipulation Behaviors”. 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025.
Authors
Takato Horii
(he/him)
Associate Professor
Associate Professor at Graduate School of Engineering Science, Osaka University.
Research interests include cognitive developmental robotics, computational modeling
of emotional development, and human-robot interaction.