Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have incre...
Jianshu Zhang, Keliang Wu, Haoran Lu et al.· arXiv.org· 1 citation
The proposed Mixture of Roles (MoRe), which adaptively composes multiple specializations into a single steering vector for single-turn inference, enables multi-perspective specialization in a single-agent, single-turn inference process.
Zhichen Zeng, Hui-Yuan Chen, Jingru Cheng et al.· 2 citations
This paper proposes World Model RL (WMRL), which replaces environment execution with a world model to remove this bottleneck and accelerates training by 3-4x on various tasks at different agent scales, while exceeding the performance of standard RL baselines.
Xi-Yuan Yang, S. Sarwar, Jingru Cheng et al.· 0 citations
This work proposes a principled VLM TTA method called \algname, and theoretically reveals that the InfoNCE loss can be neatly reformulated as a Wasserstein OT formulation, thereby unifying the objectives of the inference and adaptation of VLMs to achieve their mutual benefits.
Qi Yu, Zhichen Zeng, Katherine Tieu et al.· 0 citations
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