Networked humanoid robots improve teamwork and human interaction
Networked Humanoid Intelligence: Communication, Coordination, Collaboration, and Social Interaction
RoboticsEmerging TechnologiesNetworking and Internet Architecture
Summary
Humanoid robots are becoming more connected and working together along with humans in shared spaces. This paper reviews how these robots communicate, coordinate, collaborate, and interact socially over networks. It looks at the challenges of sharing information while moving and balancing, which are unique to humanoid robots. The authors also discuss risks of network delays and errors, and suggest future research directions for safer, smarter robot fleets.
What this means in practice
- •For industrial robot teams: Manage multiple humanoid robots in factories to efficiently share workspace and tasks while coordinating their movements and information.
- •For human-robot interaction designers: Design social interaction skills for humanoid robots that work safely and intuitively with human coworkers in networked environments.
A survey. It maps existing work.
Authors
Loc X. Nguyen, Huy Q. Le, Ji Su Yoon, Nguyen H. Tran, Eui-Nam Huh, Choong Seon Hong
Abstract
Humanoid robots are moving from laboratory platforms toward connected fleets that share workspaces, infrastructure, tasks, and knowledge with other robots and humans. As deployments grow, the operation and improvement of each humanoid depend on information exchanged across the network. However, existing research has largely studied humanoids as individual systems. This survey examines networked humanoid intelligence through four functions of information exchange: communication, coordination, collaboration, and social intelligence with human interaction. We first establish the foundations of networked humanoid systems, define the question addressed by each function, and connect them through a common factory scenario. We review the literature along six dimensions: interaction architecture, exchanged information, physical coupling, operating timescale, system scale, and embodiment. We separate results obtained with physical and simulated humanoids from those based on other robotic platforms or disembodied agents, because the latter do not fully capture humanoid-specific constraints such as balance, whole-body motion, contact, and physical interaction. We examine hardware, datasets, and benchmarks, and distinguish risks handled locally from those networking introduces, such as stale information or unsafe knowledge propagation. Finally, we identify open problems in control-aware networking, compact body-centric messages, physically grounded coordination, network- and embodiment-aware knowledge sharing, fleet-level social consistency, and accountable human oversight.