Harness evolution enables frozen language model agents to adapt to unfamiliar tasks by modifying the external programs that govern their behavior. For long-horizon tasks, each rollout is costly, and a limited interaction budget may yield few examples of effective behavior. Sparse, delayed feedback also makes it difficu...
Li-Rong Che, Yu-Zhe Yang, Pei Lin et al.· 5 citations· ⚡1
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.· 7 citations· ⚡2
In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is...
Zhe Gan, Yan-Bo Chen, Li-Rong Che et al.· 0 citations
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
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