Thinking from the Robot’s View: The CoT-HRC Benchmark for Human Intent Reasoning in Embodied Collaboration

Effective Human-Robot Collaboration (HRC) requires robotsto accurately infer human intents under partial observations. However,existing benchmarks mostly rely on idealized views, overlooking the spa-tiotemporal incompleteness such as low-angle truncation and dynamicocclusions inherent in real-world quadruped robot perception. Further-more, current evaluations lack diagnostic transparency, failing to distin-guish whether Vision-Language Models (VLMs) perform reliable com-pensatory reasoning or merely hallucinate based on statistical biaseswhen visual features are degraded. To bridge these dual gaps, we in-troduce CoT-HRC, a large-scale benchmark built in Habitat 3.0 thatcouples simulated robot-centric views with explicit hierarchical Chain-of-Thought (CoT) annotations. We propose a diagnostic protocol featur-ing the Step-wise Consistency Assessment (SCAR ) to penalize spuriousaccuracy by enforcing intermediate logical consistency, and the Condi-tional Reasoning Accuracy (CRA) to explicitly decouple models’ un-derlying reasoning capabilities from visual perception failures. Exten-sive experiments across state-of-the-art VLMs reveal that while currentmodels inherently possess strong physical commonsense, their reason-ing chains are severely disrupted by the dynamic geometric constraintsof egocentric views. Our fine-grained ablations further highlight criti-cal bottlenecks in temporal aggregation and the compensatory role ofsemantic priors, establishing CoT-HRC as a vital stepping stone for ro-bust embodied intent inference.The full benchmark is publicly availableat https://github.com/Thus-cx/CoT-HRC.