The proposed exploration-augmented key-observation gating first determines whether the current RL episode is judgment-worthy based on its similarity to a generated success trajectory, and proposes VLM success reward via Exploration-Augmented Key-Observation Gating (VLMeg) to well harness VLM for embodied reward design.
Xin Liu, Yu-Tong Jiang, Jia-Yue Su et al.· 0 citations
Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new skills often degrades previously mastered capabilities. Existing post-training paradigms face a fundamental trade-off: Supervised Fine-Tuning (SFT) is sample-efficient bu...
Yu-Qian Fu, Kai-Wen Jiang, Zi-Jun Song et al.· 0 citations
High-precision locomotion combines motion-command tracking with precise regulation of task-relevant physical states, enabling robots to interact reliably with their surroundings during motion. Joint end-to-end optimization can leave precision objectives insufficiently optimized, while reactive residual control adjusts...
Zi-Jie Zhao, Sheng-Qian Chen, Xiao-Xu Wang et al.· 0 citations
M3W is a novel approach that applies mixture-of-experts (MoE) to world model instead of policy, enabling both learning and planning, and demonstrates superior performance, sample efficiency, and multi-task adaptability.
Zi-Jie Zhao, Zhong Zhao, Kaixuan Xu et al.· Neural Information Processin...· 9 citations
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