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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.RO2026

ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning

Yilun Kong, Yunpeng Qing, Guozheng Ma +4

The paper proposes ExToken, a framework that conditions vision‑language‑action policies on discrete behavioral tokens derived from offline demonstrations to promote diverse, struct…

cs.RO2026

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

Brain Team, Ziyang Gong, Haoming Gu +28

Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, monitor progress, and improve f…

cs.RO2026

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization

Sixu Lin, Yunpeng Qing, Litao Liu +4

Recent progress in Reinforcement Learning (RL) provides a principled approach to optimizing Vision-Language-Action (VLA) models, facilitating a shift from trajectory imitation to a…

cs.LG2026

BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning

Yunpeng Qing, Yixiao Chi, Shuo Chen +5

Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints on pre-collected datasets. Howe…

cs.LG2025

A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

Yunpeng Qing, Shunyu Liu, Jie Song +4

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of d…

cs.AI2025

Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?

Yihe Zhou, Shunyu Liu, Yunpeng Qing +4

Centralized Training with Decentralized Execution (CTDE) has recently emerged as a popular framework for cooperative Multi-Agent Reinforcement Learning (MARL), where agents can use…