Networked Humanoid Intelligence: Communication, Coordination, Collaboration, and Social Interaction

Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environments. Although recent advancements have significantly enhanced the agility of individual robots, bridging the gap to physically coupled multi-humanoid interaction remains challenging, primarily due to severe kinematic mismatches and complex contact dynamics. To address this, we introduce Rhythm, the first unified framework enabling real-world deployment of dual-humanoid systems for complex, physically plausible interactions. Our framework integrates three core components: (1) an Interaction-Aware Motion Retargeting (IAMR) module that generates feasible humanoid interaction references from human data; (2) an Interaction-Guided Reinforcement Learning (IGRL) policy that masters coupled dynamics via graph-based rewards; and (3) a real-world deployment system that enables robust transfer of dual-humanoid interaction. Extensive experiments on physical Unitree G1 robots demonstrate that our framework achieves robust interactive whole-body control, successfully transferring diverse behaviors such as hugging and dancing from simulation to reality.

Full-stack human-robot collaboration (HRC) can become brittle when replacing a planner, partner model, coordination policy, or controller changes the physical meaning of cross-layer signals. We introduce C2C, an object-centric cognition-to-control architecture that preserves these meanings through physical contracts. Rather than standardizing individual modules, C2C standardizes the physical semantics exchanged between them: task intent is represented by a geometrically verified payload path, partner information by a source-independent physical state, learned coordination by bounded 11-D task-space commands, and robot-specific feasibility remains inside whole-body control (WBC). The reference system combines a vision-language model with deterministic geometric verification and multi-agent reinforcement learning (MARL) for partner-aware coordination. Across nine transport scenarios, adaptive MARL variants achieve 77.1-82.1% mean success under unchanged interfaces, compared with 56.5% for a scripted-partner reference. The same contracts further support neural and interpretable hard-tree actors, two- and three-carrier teams, and simulated-to-human partner substitution. Physical Unitree G1-human tests achieve 100% success in spatially confined transport and 80% in super-long-object handling, while a three-carrier system with two G1 humanoids and one human validates the same interface structure on hardware. Together, these results show that C2C converts a tightly coupled HRC stack into a plug-compatible system in which key components can change without redefining the physical collaboration task.

The scalability of humanoid robots will depend not only on models and hardware, but also on whether physical experience can accumulate across robots, tasks, organizations, and time. Drawing on the authors' work in developing ISO/WD 26264-1, Humanoid robot datasets -- Part 1: General requirements, within ISO/TC 299/WG 16, this article argues that data standards are becoming foundational infrastructure for Physical AI. We develop three insights. First, humanoid robot data is embodied interaction data, not a collection of isolated digital samples; a useful dataset must preserve the relationship among robot body, action, task, scene, execution trace, and outcome. Second, its value depends on physical coherence: multimodal streams are reusable only when timing, coordinate frames, calibration, kinematics, units, and synchronization assumptions remain inspectable. Third, the main bottleneck is not only data scarcity, but non-cumulative data caused by high collection costs, data silos, and inconsistent evaluation. We argue that humanoid robot data standards address these bottlenecks by making embodied experience interpretable, shareable, traceable, and reusable. A general standard should provide horizontal infrastructure for lifecycle management, metadata, provenance, quality, versioning, and traceability, while capability-specific parts should define domain grammar for manipulation, locomotion, human-robot interaction, cognition, and future humanoid capabilities. As AI moves from screens into bodies, data standards must evolve from organizing digital information to structuring physical interaction.