Although the advancement of vision-language models (VLMs) has endowed robots with enhanced environmental understanding and task reasoning, a comprehensive evaluation methodology is important to advance the integration of VLMs in robotic navigation and manipulation. However, current benchmarks lack a comprehensive metho...
Hua-Peng Li, Fu-Xiang Feng, Jin-Qiu Fan et al.· 0 citations
2AM shows that task memory can remain Agent-side and shows that Action Model capability depends not only on what the policy has learned, but on how precisely the Agent can steer it, and isolates this question through a deliberately constrained design.
Yu-Tong Hu, Feng Chen, Xue-Zhi Cao et al.· 2 citations· ⚡2
ClawTrack is presented, a dual-assessment benchmark that simultaneously measures what an agent achieves (Task Score) and how it achieves it (Process Score) and finds that process scores effectively attribute success and failure to specific reasoning dimensions, filtering lucky passes invisible to outcome-only evaluatio...
Xingjian Wu, Xuhan Zhu, Xing-Chen Liu et al.· arXiv.org· 0 citations
Contrastive Reinforced Policy Optimization (CRPO) is introduced, which reformulates agentic OPSD from a contrastive learning perspective, and conducts group-wise contrast to preserve reliable, fine-grained optimization signals.
Xingjian Wu, Junlin Liu, Xing-Chen Liu et al.· arXiv.org· 2 citations
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