SocialCoAct: Enabling and Benchmarking Egocentric Social Co-Action for VLM-Based Humanoid Robots

Abstract

Enabling humanoid robots to participate effectively in social interactions is important for their integration into human-centered environments. Existing methods, however, typically map pre-generated speech directly to continuous motion trajectories but have limited awareness of complex social environments and lack explicit high-level semantic action planning. We introduce SocialCoAct, a unified agent-centered framework for VLM-based egocentric social co-action. We formulate egocentric social co-action as an end-to-end agent task and develop SocialCoAct-Agent, which transforms egocentric observations and dialogue history into verbal responses and atomic action sequences, aligns each action with its corresponding utterance span, and executes the resulting behaviors in MuJoCo and on a physical humanoid robot. To evaluate the VLM capabilities required by this task, we construct SocialCoAct-Bench from continuous egocentric recordings of natural multi-party interactions. The benchmark comprises 423 human-verified multiple-choice questions across three subtasks: Long Episode Understanding (LEU), Speaker Intention Understanding (SIU), and Coordinated Action Planning (CAP). Across a diverse set of closed-source and open-weight VLMs, including general and egocentric models, the highest overall accuracy is 77.54%, while accuracy on the hard subset reaches only 46.51%. Extended reasoning improves Qwen3-VL-2B and Qwen3-VL-4B by 19.62 and 17.02 percentage points, respectively, but reduces inference throughput by 53.6$\times$ and 20.2$\times$. Visual-input ablations further show that the benefits of visual context vary across models. SocialCoAct provides a foundation for developing and evaluating VLM-based humanoid agents that coordinate context-aware speech and action in complex social interactions. The codebase and benchmark will be made publicly available upon publication.