Robots can use historical observations to answer spatiotemporal queries in familiar environments, but the presence of relevant evidence does not imply that existing tools support the operations a task requires. We introduce ReSight, a task-driven self-improving memory system for embodied agents that expands the capabil...
Li-Rong Che, Yang Chen, Yi-Meng Su et al.· 2 citations· ⚡1
Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, but embodied RSI methods either refine the harness around a frozen executor and so cannot add a missing motion, or train the executor on persistent failures without aski...
Pei-Dong Wang, Ying Chang, Zhi-Ming Ma et al.· 4 citations
This work introduces HarnessVLN, a zero-shot, training-free framework that unifies instruction-following and object-goal navigation through a shared Agent Harness and demonstrates its applicability to both navigation tasks in real-world environments.
Yang Chen, Li-Rong Che, Zhen-Yu Huang et al.· arXiv.org· 8 citations· ⚡2
CoBench is introduced, a construct-level benchmark for evaluating multi-agent embodied coordination in executable household tasks and shows that coordination ability is highly construct-specific: strong overall performance does not imply balanced competence across different coordination types.
Yang Chen, Ye-Xin Xie, Li-Rong Che et al.· 0 citations
VERA (Visual Evidence-Retaining strategy for long-horizon Agents), a training-free context manager built on deterministic rendering with no exposed memory operations, supporting a modality-preserving view of long-horizon context management.
Jiang-Feng Su, Cong Pang, Jiawei Hong et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
