Bugs with Features: Vision-Based Fault-Tolerant Collective Motion Inspired by Nature

• URL: http://arxiv.org/abs/2512.22448v1
• Date: Sat, 27 Dec 2025 03:29:50 GMT
• Title: Bugs with Features: Vision-Based Fault-Tolerant Collective Motion Inspired by Nature
• Abstract summary: In collective motion, perceptually-limited individuals move in an ordered manner, without centralized control.<n>This paper presents mechanisms for robust collective motion inspired by studies of locusts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In collective motion, perceptually-limited individuals move in an ordered manner, without centralized control. The perception of each individual is highly localized, as is its ability to interact with others. While natural collective motion is robust, most artificial swarms are brittle. This particularly occurs when vision is used as the sensing modality, due to ambiguities and information-loss inherent in visual perception. This paper presents mechanisms for robust collective motion inspired by studies of locusts. First, we develop a robust distance estimation method that combines visually perceived horizontal and vertical sizes of neighbors. Second, we introduce intermittent locomotion as a mechanism that allows robots to reliably detect peers that fail to keep up, and disrupt the motion of the swarm. We show how such faulty robots can be avoided in a manner that is robust to errors in classifying them as faulty. Through extensive physics-based simulation experiments, we show dramatic improvements to swarm resilience when using these techniques. We show these are relevant to both distance-based Avoid-Attract models, as well as to models relying on Alignment, in a wide range of experiment settings.

Related papers

• MotiMotion: Motion-Controlled Video Generation with Visual Reasoning [49.243234645532745]
  We introduce MotiMotion, a framework that reformulates motion control as a reasoning-then-generation problem.<n>To encourage causally grounded and commonsense-consistent interactions, we leverage a training-free vision-language reasoner.<n>To further improve motion naturalness, we propose a confidence-aware control scheme that modulates guidance strength.
  arXiv  Detail & Related papers  (2026-05-21T17:59:36Z)
• A Mechanistic Model for Collective Motion from Sensorimotor Regularities [6.805575417034369]
  We present a model based on a modeling framework from robotics, extended here to collective motion.<n>This simple model produces diverse collective behaviors including polarized motion, milling, ring formations, and fragmentation.<n>Collective behavior can therefore be understood as the emergent outcome of interacting sensorimotor regularities.
  arXiv  Detail & Related papers  (2026-05-15T18:17:41Z)
• Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving [12.657200460805173]
  Existing physical adversarial attacks on vision-based autonomous driving induce time-evolving perception errors.<n>We show that viewing-angle variation itself can be turned into an attack tool.<n>We demonstrate a novel attack on nuScenes dataset, showing the effectiveness with an end-to-end success rate of up to 87.5%.
  arXiv  Detail & Related papers  (2026-05-12T20:47:55Z)
• Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot [56.377161933570335]
  Humans naturally exploit a "weightless" state during non-self-stabilizing (NSS) motions.<n>Inspired by this biological mechanism, we propose a weightlessness-state auto-labeling strategy for dataset annotation.<n>Our work bridges the gap between precise trajectory tracking and adaptive environmental interaction, offering a biologically-inspired solution for contact-rich humanoid control.
  arXiv  Detail & Related papers  (2026-04-23T07:10:05Z)
• EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation [74.07852250099559]
  We investigate Egocentric Vision-Language (Ego-VL) motion generation.<n>This task requires synthesizing 3D human motion conditioned jointly on first-person visual observations and natural language instructions.<n>We propose a hierarchical generative framework textbfEgoMotion to address this challenge.
  arXiv  Detail & Related papers  (2026-04-21T05:31:06Z)
• ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration [51.69384671495837]
  ActiveGlasses is a system for learning robot manipulation from ego-centric human demonstrations with active vision.<n>A stereo camera mounted on smart glasses serves as the sole perception device for both data collection and policy inference.<n>To enable zero-transfer, we extract object trajectories from demonstrations and use an object-centric point-cloud policy to jointly predict manipulation and head movement.
  arXiv  Detail & Related papers  (2026-04-09T17:59:08Z)
• Hand-Aware Egocentric Motion Reconstruction with Sequence-Level Context [17.735273173582716]
  We present HaMoS, the first hand-aware, sequence-level diffusion framework that directly conditions on both head trajectory and intermittently visible hand cues.<n>We also demonstrate that sequence-level contexts such as body shape and field-of-view are crucial for accurate motion reconstruction.
  arXiv  Detail & Related papers  (2025-12-22T11:26:41Z)
• MoReact: Generating Reactive Motion from Textual Descriptions [57.642436102978245]
  MoReact is a diffusion-based method designed to disentangle the generation of global trajectories and local motions sequentially.<n>Our experiments, utilizing data adapted from a two-person motion dataset, demonstrate the efficacy of our approach.
  arXiv  Detail & Related papers  (2025-09-28T14:31:41Z)
• StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion [31.30409161905949]
  StyleLoco is a novel framework for learning humanoid locomotion.<n>It combines the agility of reinforcement learning with the natural fluidity of human-like movements.<n>We demonstrate that StyleLoco enables humanoid robots to perform diverse locomotion tasks.
  arXiv  Detail & Related papers  (2025-03-19T10:27:44Z)
• Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning [54.26816599309778]
  We propose a novel whole-body locomotion algorithm based on dynamic balance and Reinforcement Learning (RL)<n> Specifically, we introduce a dynamic balance mechanism by leveraging an extended measure of Zero-Moment Point (ZMP)-driven rewards and task-driven rewards in a whole-body actor-critic framework.<n> Experiments conducted on a full-sized Unitree H1-2 robot verify the ability of our method to maintain balance on extremely narrow terrains.
  arXiv  Detail & Related papers  (2025-02-24T14:53:45Z)
• SPIN: Simultaneous Perception, Interaction and Navigation [33.408010508592824]
  We present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination, we develop a mobile manipulator that exploits its ability to move and see.
  arXiv  Detail & Related papers  (2024-05-13T17:59:36Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• VAE-Loco: Versatile Quadruped Locomotion by Learning a Disentangled Gait Representation [78.92147339883137]
  We show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stance phases constituting a particular gait. We demonstrate that specific properties of the drive signal map directly to gait parameters such as cadence, footstep height and full stance duration. The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.
  arXiv  Detail & Related papers  (2022-05-02T19:49:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.