Backend
Fluidic Body: Swarm Robots as a Fluid Extension of the Hand
arXiv cs.HC0 views
arXiv:2611.06068v1 Announce Type: new Abstract: Swarm robots have attracted growing interest as a physical interface for human--computer interaction, since a swarm can reshape its formation to fit the objects and surroundings it works with. For a human operator, however, adapting the swarm's behavior remains difficult. Most existing methods require the user to specify where the swarm should go, and they offer no intuitive way to change the collective's behavior as each task demands. We present Fluidic Body, a system that lets a person operate a swarm of fluid-imitating robots as an extension of the hand. The system places a virtual fluid between the hand and the swarm, and the user adapts the swarm's behavior to each task by changing the fluid's properties through simple hand movements. When the user opens the hand, the swarm becomes soft enough to deform and flow around obstacles. Closing the hand turns it into a hard, cohesive body that can push objects. We evaluated the controller's performance and its operability in two simulation experiments and one demonstration with physical robots. First, our controller handled both obstacle avoidance and object transportation better than an existing method that behaves the same way in every task. Second, in a VR user study, 17 participants completed the combined avoidance-and-transportation task faster and more reliably than with that method. They also reported a lower workload and a stronger sense of agency. Finally, this experience carried over to physical robots in a real-world demonstration (88 participants). These results suggest that treating a swarm as a fluid, a material people already understand, allows a robot swarm to work as a deformable extension of the human body.
Source:https://arxiv.org/abs/2611.06068PreviousRobots in the City: Persona-Based Perceptions of Urban RobotsNextGrowing One's AI Tools: A Paradigm of Practice-Grown Tools
Related posts
Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning
0arXiv cs.MA
Same Method, Different Task: What the Bitter Lesson Does Not License in Social Simulation
0arXiv cs.MA
Conjugate priors for Bayesian Variable Selection in exponential family Generalized Linear Models
0arXiv stat.TH
Optimal distribution-free sequential change detection with conformal martingales
0arXiv stat.TH
Uniform Central Limit Theorems for Degree Distributions of Sparse Graphon Models
0arXiv stat.TH
TAGR: Temporally Adaptive Generative Recommendation for Industrial Live-Streaming Advertising
0arXiv cs.IR