103 citations · 104 across the 16 of their papers we have counts for
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cs.LG2025
Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps
Motoki Omura, Yusuke Mukuta, Kazuki Ota +2
Offline reinforcement learning (RL) aims to learn an optimal policy from a static dataset, making it particularly valuable in scenarios where data collection is costly, such as rob…
cs.LG2025
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning
Motoki Omura, Kazuki Ota, Takayuki Osa +2
For continuous action spaces, actor-critic methods are widely used in online reinforcement learning (RL). However, unlike RL algorithms for discrete actions, which generally model…
cs.RO2025
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…