Robotics
Perturbation-mitigated USV Navigation with Distributionally Robust Reinforcement Learning
Zhaofan Zhang, Minghao Yang, Sihong Xie et al. (+1 more)
The robustness of Unmanned Surface Vehicles (USV) is crucial when facing unknown and complex marine environments, especially when heteroscedastic observational noise poses significant challenges to sensor-based
navigation tasks. Recently, Distributional Reinforcement Learning (DistRL) has shown promising results in some challenging autonomous
navigation tasks without prior environmental information. However, these methods overlook situations where noise patterns vary across different environmental conditions, hindering safe
navigation and disrupting the learning of value functions. To address the problem, we propose DRIQN to integrate Distributionally Robust Optimization (DRO) with implicit
quantile networks to optimize worst-case performance under natural environmental conditions. Leveraging explicit subgroup modeling in the replay buffer, DRIQN incorporates heterogeneous noise sources and target robustness-critical scenarios. Experimental results based on the risk-sensitive environment demonstrate that DRIQN significantly outperforms state-of-the-art methods, achieving +13.51% success rate, -12.28% collision rate and +35.46% for time saving, +27.99% for energy saving, compared with the runner-up.
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All authors Zhaofan Zhang, Minghao Yang, Sihong Xie, Hui Xiong
Robotics
Tactile Data Recording System for Clothing with Motion-Controlled Robotic Sliding
Michikuni Eguchi, Takekazu Kitagishi, Yuichi Hiroi et al. (+1 more)
The tactile sensation of clothing is critical to wearer comfort. To reveal physical properties that make clothing comfortable, systematic collection of tactile data during sliding motion is required. We propose a robotic arm-based system for collecting tactile data from intact garments. The system performs stroking measurements with a simulated fingertip while precisely controlling speed and direction, enabling creation of motion-labeled,
multimodal tactile databases. Machine learning evaluation showed that including motion-related parameters improved identification accuracy for audio and acceleration data, demonstrating the efficacy of motion-related labels for characterizing clothing tactile sensation. This system provides a scalable, non-destructive method for capturing tactile data of clothing, contributing to future studies on fabric perception and reproduction.
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All authors Michikuni Eguchi, Takekazu Kitagishi, Yuichi Hiroi, Takefumi Hiraki
Robotics
PlaCo: a QP-based robot planning and control framework
Marc Duclusaud, Grégoire Passault, Vincent Padois et al. (+1 more)
This article introduces PlaCo, a software framework designed to simplify the formulation and solution of Quadratic Programming (QP)-based planning and control problems for robotic systems. PlaCo provides a high-level interface that abstracts away the low-level mathematical formulation of QP problems, allowing users to specify tasks and constraints in a modular and intuitive manner. The framework supports both Python bindings for rapid prototyping and a C++ implementation for real-time performance.
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All authors Marc Duclusaud, Grégoire Passault, Vincent Padois, Olivier Ly
Robotics
Development and testing of novel soft sleeve actuators
Mohammed Abboodi
Aging populations and the rising prevalence of neurological and musculoskeletal disorders increase the demand for wearable mobility assistive devices that are effective, comfortable, and anatomically compatible. Many existing systems use rigid mechanisms and bulky interfaces that impede force transmission and reduce wearability. This study introduces a soft sleeve actuation architecture that conforms to the limb while transmitting forces and moments efficiently. We develop three soft sleeve actuators that produce linear, bending, and twisting motion, and an omnidirectional design that combines these motions in one device. Actuators are fabricated from thermoplastic elastomers using a customized fused filament fabrication process that produces airtight and compliant structures and resolves leakage observed with conventional methods. A dedicated experimental platform quantifies kinematic outputs such as displacement, angle, and twist, and kinetic outputs such as force and torque under low pneumatic pressures. A parametric study varies
geometric features and material properties to determine their influence on performance. Results show reproducible multi axis motion with improved transfer of force to the limb and reduced need for complex attachment hardware. The work establishes a unified and manufacturable framework for soft sleeve actuation that enables compact and user centered assistive technologies with enhanced kinematic and kinetic performance.
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Robotics · Cross-listed from eess.SY
Model-free Adaptive Output Feedback Vibration Suppression in a Cantilever Beam
Juan Augusto Paredes Salazar and Ankit Goel
This paper presents a model-free adaptive control approach to suppress vibrations in a cantilevered beam excited by an unknown disturbance. The cantilevered beam under harmonic excitation is modeled using a lumped parameter approach. Based on retrospective cost optimization, a sampled-data adaptive controller is developed to suppress vibrations caused by external disturbances. Both displacement and acceleration measurements are considered for feedback. Since acceleration measurements are more sensitive to spillover, which excites higher frequency modes, a filter is developed to extract key displacement information from the acceleration data and enhance suppression performance. The vibration suppression performance is compared using both displacement and acceleration measurements.
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Robotics · Cross-listed from eess.SP
Towards Human-AI-Robot Collaboration and AI-Agent based Digital Twins for Parkinson's Disease Management: Review and Outlook
Hassan Hizeh, Rim Chighri, Muhammad Mahboob Ur Rahman et al. (+3 more)
The current body of research on Parkinson's disease (PD) screening, monitoring, and management has evolved along two largely independent trajectories. The first research community focuses on
multimodal sensing of PD-related biomarkers using noninvasive technologies such as inertial measurement units (IMUs), force/pressure insoles, electromyography (EMG), electroencephalography (
EEG),
speech and acoustic analysis, and RGB/RGB-D motion capture systems. These studies emphasize data acquisition, feature extraction, and machine learning-based classification for PD screening, diagnosis, and disease progression modeling. In parallel, a second research community has concentrated on robotic intervention and rehabilitation, employing socially assistive
robots (SARs),
robot-assisted rehabilitation (RAR) systems, and virtual reality (VR)-integrated robotic platforms for improving motor and cognitive function, enhancing social engagement, and supporting caregivers. Despite the complementary goals of these two domains, their methodological and technological integration remains limited, with minimal data-level or decision-level coupling between the two. With the advent of advanced artificial intelligence (AI), including large
language models (LLMs), agentic AI systems, a unique opportunity now exists to unify these research streams. We envision a closed-loop sensor-AI-
robot framework in which
multimodal sensing continuously guides the interaction between the patient, caregiver,
humanoid robot (and physician) through AI
agents that are powered by a multitude of AI models such as robotic and wearables foundation models, LLM-based
reasoning, reinforcement learning, and continual learning. Such closed-loop system enables personalized, explainable, and context-aware intervention, forming the basis for digital twin of the PD patient that can adapt over time to deliver intelligent, patient-centered PD care.
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All authors Hassan Hizeh, Rim Chighri, Muhammad Mahboob Ur Rahman, Mohamed A. Bahloul, Ali Muqaibel, Tareq Y. Al-Naffouri
Robotics
Robustness study of the bio-inspired musculoskeletal arm robot based on the data-driven iterative learning algorithm
Jianbo Yuan, Jing Dai, Yerui Fan et al. (+3 more)
The human arm exhibits remarkable capabilities, including both explosive power and precision, which demonstrate dexterity, compliance, and robustness in unstructured environments. Developing robotic systems that emulate human-like operational characteristics through musculoskeletal structures has long been a research focus. In this study, we designed a novel lightweight tendon-driven musculoskeletal arm (LTDM-Arm), featuring a seven degree-of-freedom (DOF) skeletal joint system and a modularized artificial muscular system (MAMS) with 15 actuators. Additionally, we employed a Hilly-type muscle model and data-driven iterative learning control (DDILC) to learn and refine activation signals for repetitive tasks within a finite time frame. We validated the anti-interference capabilities of the musculoskeletal system through both simulations and experiments. The results show that the LTDM-Arm system can effectively achieve desired trajectory tracking tasks, even under load disturbances of 20 % in simulation and 15 % in experiments. This research lays the foundation for developing advanced robotic systems with human-like operational performance.
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All authors Jianbo Yuan, Jing Dai, Yerui Fan, Yaxiong Wu, Yunpeng Liang, Weixin Yan
Robotics · Cross-listed from cs.CV
Runtime Safety Monitoring of Deep Neural Networks for Perception: A Survey
Albert Schotschneider, Svetlana Pavlitska and J. Marius Zöllner
Deep neural networks (DNNs) are widely used in perception systems for safety-critical applications, such as autonomous driving and robotics. However, DNNs remain vulnerable to various safety concerns, including generalization errors, out-of-distribution (OOD) inputs, and
adversarial attacks, which can lead to hazardous failures. This survey provides a comprehensive overview of runtime safety monitoring approaches, which operate in parallel to DNNs during inference to detect these safety concerns without modifying the DNN itself. We categorize existing methods into three main groups: Monitoring inputs, internal representations, and outputs. We analyze the state-of-the-art for each category, identify strengths and limitations, and map methods to the safety concerns they address. In addition, we highlight open challenges and future research directions.
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Robotics
10 Open Challenges Steering the Future of Vision-Language-Action Models
Soujanya Poria, Navonil Majumder, Chia-Yu Hung et al. (+7 more)
Due to their ability of follow natural
language instructions,
vision-
language-action (VLA) models are increasingly prevalent in the
embodied AI arena, following the widespread success of their precursors – LLMs and VLMs. In this paper, we discuss 10 principal milestones in the ongoing development of VLA models – multimodality,
reasoning, data, evaluation, cross-
robot action generalization, efficiency, whole-body coordination, safety,
agents, and coordination with humans. Furthermore, we discuss the emerging trends of using spatial understanding, modeling world dynamics, post training, and data synthesis – all aiming to reach these milestones. Through these discussions, we hope to bring
attention to the research avenues that may accelerate the development of VLA models into wider acceptability.
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All authors Soujanya Poria, Navonil Majumder, Chia-Yu Hung, Amir Ali Bagherzadeh, Chuan Li, Kenneth Kwok, Ziwei Wang, Cheston Tan, Jiajun Wu, David Hsu
Robotics · Cross-listed from eess.SY
Disentangled Control of Multi-Agent Systems
Ruoyu Lin, Gennaro Notomista and Magnus Egerstedt
This paper develops a general framework with convergence guarantees for multi-
agent control synthesis, which applies to a wide range of problems, including those with time-varying objective functions. The proposed framework achieves decentralization without inducing entangled dynamics among
agents, and it naturally supports multi-objective robotics and real-time implementation. To demonstrate its generality and effectiveness, the framework is applied to three representative problems, namely time-varying leader-follower formation control, decentralized coverage control for time-varying density functions, which is a long-standing open problem, and safe formation
navigation in a dense environment.
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Robotics
From Words to Safety: Language-Conditioned Safety Filtering for Robot Navigation
Zeyuan Feng, Haimingyue Zhang and Somil Bansal
As
robots become increasingly integrated into open-world, human-centered environments, their ability to interpret natural
language instructions and adhere to safety constraints is critical for effective and trustworthy interaction. Existing approaches often focus on mapping
language to reward functions instead of safety specifications or address only narrow constraint classes (e.g., obstacle avoidance), limiting their robustness and applicability. We propose a modular framework for
language-conditioned safety in
robot navigation. Our framework is composed of three core components: (1) a large
language model (LLM)-based module that translates free-form instructions into structured safety specifications, (2) a perception module that grounds these specifications by maintaining object-level 3D representations of the environment, and (3) a model predictive control (MPC)-based safety filter that enforces both semantic and
geometric constraints in real time. We evaluate the effectiveness of the proposed framework through both simulation studies and hardware experiments, demonstrating that it robustly interprets and enforces diverse
language-specified constraints across a wide range of environments and scenarios.
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Robotics
Fair and Safe: A Real-Time Hierarchical Control Framework for Intersections
Lei Shi, Yongju Kim, Xinzhi Zhong et al. (+3 more)
Ensuring
fairness in the coordination of connected and automated vehicles at intersections is essential for equitable access, social acceptance, and long-term system efficiency, yet it remains underexplored in safety-critical, real-time traffic control. This paper proposes a
fairness-aware hierarchical control framework that explicitly integrates inequity aversion into intersection management. At the top layer, a centralized allocation module assigns control authority (i.e., selects a single vehicle to execute its trajectory) by maximizing a utility that accounts for waiting time, urgency, control history, and velocity deviation. At the bottom layer, the authorized vehicle executes a precomputed trajectory using a Linear Quadratic Regulator (LQR) and applies a high-order Control Barrier Function (HOCBF)-based safety filter for real-time collision avoidance. Simulation results across varying traffic demands and demand distributions demonstrate that the proposed framework achieves near-perfect
fairness, eliminates collisions, reduces average delay, and maintains real-time feasibility. These results highlight that
fairness can be systematically incorporated without sacrificing safety or performance, enabling scalable and equitable coordination for future autonomous traffic systems.
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All authors Lei Shi, Yongju Kim, Xinzhi Zhong, Wissam Kontar, Qichao Liu, Soyoung Ahn
Robotics · Cross-listed from eess.IV
EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion
Tong Chen, Xinyu Ma, Long Bai et al. (+3 more)
Endoscopic images often suffer from diverse and co-occurring degradations such as low lighting, smoke, and bleeding, which obscure critical clinical details. Existing restoration methods are typically task-specific and often require prior knowledge of the degradation type, limiting their robustness in real-world clinical use. We propose EndoIR, an all-in-one, degradation-agnostic
diffusion-based framework that restores multiple degradation types using a single model. EndoIR introduces a Dual-Domain Prompter that extracts joint spatial-frequency features, coupled with an adaptive
embedding that encodes both shared and task-specific cues as conditioning for denoising. To mitigate feature confusion in conventional concatenation-based conditioning, we design a Dual-Stream
Diffusion architecture that processes clean and degraded inputs separately, with a Rectified Fusion Block integrating them in a structured, degradation-aware manner. Furthermore, Noise-Aware Routing Block improves efficiency by dynamically selecting only noise-relevant features during denoising. Experiments on SegSTRONG-C and CEC
datasets demonstrate that EndoIR achieves state-of-the-art performance across multiple degradation scenarios while using fewer parameters than strong baselines, and downstream
segmentation experiments confirm its clinical utility.
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All authors Tong Chen, Xinyu Ma, Long Bai, Wenyang Wang, Yue Sun, Luping Zhou
Robotics
ViTaMIn-B: A Reliable and Efficient Visuo-Tactile Bimanual Manipulation Interface
Chuanyu Li, Chaoyi Liu, Daotan Wang et al. (+6 more)
Handheld devices have opened up unprecedented opportunities to collect large-scale, high-quality demonstrations efficiently. However, existing systems often lack robust tactile sensing or reliable pose tracking to handle complex interaction scenarios, especially for bimanual and contact-rich tasks. In this work, we propose ViTaMIn-B, a more capable and efficient handheld data collection system for such tasks. We first design DuoTact, a novel compliant visuo-tactile sensor built with a flexible frame to withstand large contact forces during manipulation while capturing high-resolution contact geometry. To enhance the cross-sensor generalizability, we propose reconstructing the sensor's global deformation as a 3D point cloud and using it as the policy input. We further develop a robust, unified 6-DoF bimanual pose acquisition process using Meta Quest controllers, which eliminates the trajectory drift issue in common SLAM-based methods. Comprehensive user studies confirm the efficiency and high usability of ViTaMIn-B among novice and expert operators. Furthermore, experiments on four bimanual manipulation tasks demonstrate its superior task performance relative to existing systems. Project page: https://chuanyune.github.io/ViTaMIn-B_page/
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All authors Chuanyu Li, Chaoyi Liu, Daotan Wang, Shuyu Zhang, Lusong Li, Zecui Zeng, Fangchen Liu, Jing Xu, Rui Chen
Robotics
Gentle Manipulation Policy Learning via Demonstrations from VLM Planned Atomic Skills
Jiayu Zhou, Qiwei Wu, Jian Li et al. (+3 more)
Autonomous execution of long-horizon, contact-rich manipulation tasks traditionally requires extensive real-world data and expert engineering, posing significant cost and scalability challenges. This paper proposes a novel framework integrating hierarchical semantic decomposition, reinforcement learning (RL), visual
language models (VLMs), and knowledge
distillation to overcome these limitations. Complex tasks are decomposed into atomic skills, with RL-trained policies for each primitive exclusively in simulation. Crucially, our RL formulation incorporates explicit force constraints to prevent object damage during delicate interactions. VLMs perform high-level task decomposition and skill planning, generating diverse expert demonstrations. These are distilled into a unified policy via Visual-Tactile
Diffusion Policy for end-to-end execution. We conduct comprehensive ablation studies exploring different VLM-based task planners to identify optimal demonstration generation pipelines, and systematically compare imitation learning
algorithms for skill
distillation. Extensive simulation experiments and physical deployment validate that our approach achieves policy learning for long-horizon manipulation without costly human demonstrations, while the VLM-guided atomic skill framework enables scalable generalization to diverse tasks.
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All authors Jiayu Zhou, Qiwei Wu, Jian Li, Zhe Chen, Xiaogang Xiong, Renjing Xu
Robotics
3D Mapping Using a Lightweight and Low-Power Monocular Camera Embedded inside a Gripper of Limbed Climbing Robots
Taku Okawara, Ryo Nishibe, Mao Kasano et al. (+2 more)
Limbed climbing
robots are designed to explore challenging vertical walls, such as the skylights of the Moon and Mars. In such
robots, the primary role of a hand-eye camera is to accurately estimate 3D positions of graspable points (i.e., convex terrain surfaces) thanks to its close-up views. While conventional climbing
robots often employ RGB-D cameras as hand-eye cameras to facilitate straightforward 3D terrain mapping and graspable point detection, RGB-D cameras are large and consume considerable power. This work presents a 3D terrain mapping system designed for space exploration using limbed climbing
robots equipped with a
monocular hand-eye camera. Compared to RGB-D cameras,
monocular cameras are more lightweight, compact structures, and have lower power consumption. Although
monocular SLAM can be used to construct 3D maps, it suffers from scale ambiguity. To address this limitation, we propose a SLAM method that fuses
monocular visual constraints with limb forward kinematics. The proposed method jointly estimates time-series gripper poses and the global metric scale of the 3D map based on factor graph optimization. We validate the proposed framework through both physics-based simulations and real-world experiments. The results demonstrate that our framework constructs a metrically scaled 3D terrain map in real-time and enables autonomous grasping of convex terrain surfaces using a
monocular hand-eye camera, without relying on RGB-D cameras. Our method contributes to scalable and energy-efficient perception for future space missions involving limbed climbing
robots. See the
video summary here: https://youtu.be/fMBrrVNKJfc
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All authors Taku Okawara, Ryo Nishibe, Mao Kasano, Kentaro Uno, Kazuya Yoshida
Robotics · Cross-listed from cs.MA
Evader-Agnostic Team-Based Pursuit Strategies in Partially-Observable Environments
Addison Kalanther, Daniel Bostwick, Chinmay Maheshwari et al. (+1 more)
We consider a scenario where a team of two unmanned aerial vehicles (
UAVs) pursue an evader
UAV within an urban environment. Each
agent has a limited view of their environment where buildings can occlude their field-of-view. Additionally, the pursuer team is agnostic about the evader in terms of its initial and final location, and the behavior of the evader. Consequently, the team needs to gather information by searching the environment and then track it to eventually intercept. To solve this multi-player, partially-observable, pursuit-evasion game, we develop a two-phase neuro-symbolic
algorithm centered around the principle of bounded rationality. First, we devise an offline approach using deep reinforcement learning to progressively train
adversarial policies for the pursuer team against fictitious evaders. This creates $k$-levels of rationality for each
agent in preparation for the online phase. Then, we employ an online classification
algorithm to determine a "best guess" of our current opponent from the set of iteratively-trained strategic
agents and apply the best player response. Using this schema, we improved average performance when facing a random evader in our environment.
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All authors Addison Kalanther, Daniel Bostwick, Chinmay Maheshwari, Shankar Sastry
Robotics
Adversarial Game-Theoretic Algorithm for Dexterous Grasp Synthesis
Yu Chen, Botao He, Yuemin Mao et al. (+7 more)
For many complex tasks, multi-finger
robot hands are poised to revolutionize how we interact with the world, but reliably grasping objects remains a significant challenge. We focus on the problem of synthesizing grasps for multi-finger
robot hands that, given a target object's geometry and pose, computes a hand configuration. Existing approaches often struggle to produce reliable grasps that sufficiently constrain object motion, leading to instability under disturbances and failed grasps. A key reason is that during grasp generation, they typically focus on resisting a single wrench, while ignoring the object's potential for
adversarial movements, such as escaping. We propose a new grasp-synthesis approach that explicitly captures and leverages the
adversarial object motion in grasp generation by formulating the problem as a two-player game. One player controls the
robot to generate feasible grasp configurations, while the other adversarially controls the object to seek motions that attempt to escape from the grasp. Simulation experiments on various
robot platforms and target objects show that our approach achieves a success rate of 75.78%, up to 19.61% higher than the state-of-the-art baseline. The two-player game mechanism improves the grasping success rate by 27.40% over the method without the game formulation. Our approach requires only 0.28-1.04 seconds on average to generate a grasp configuration, depending on the
robot platform, making it suitable for real-world deployment. In real-world experiments, our approach achieves an average success rate of 85.0% on ShadowHand and 87.5% on LeapHand, which confirms its feasibility and effectiveness in real
robot setups.
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All authors Yu Chen, Botao He, Yuemin Mao, Arthur Jakobsson, Jeffrey Ke, Yiannis Aloimonos, Guanya Shi, Howie Choset, Jiayuan Mao, Jeffrey Ichnowski
Robotics
An Open-Source, Reproducible Tensegrity Robot that can Navigate Among Obstacles
William R. Johnson III, Patrick Meng, Nelson Chen et al. (+5 more)
Tensegrity
robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering path planning and obstacle avoidance. This paper presents a complete, open-source, and reproducible system that enables
navigation for a 3-bar tensegrity
robot. The system comprises: (i) an inexpensive, open-source hardware design, and (ii) an integrated, open-source software stack for physics-based modeling, system identification, state estimation, path planning, and control. All hardware and software are publicly available at https://sites.google.com/view/tensegrity-
navigation/. The proposed system tracks the
robot's pose and executes collision-free paths to a specified goal among known obstacle locations. System robustness is demonstrated through experiments involving unmodeled environmental challenges, including a vertical drop, an incline, and granular media, culminating in an outdoor field demonstration. To validate reproducibility, experiments were conducted using
robot instances at two different laboratories. This work provides the robotics community with a complete
navigation system for a compliant, impact-resistant, and shape-morphing
robot. This system is intended to serve as a springboard for advancing the
navigation capabilities of other unconventional robotic platforms.
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All authors William R. Johnson III, Patrick Meng, Nelson Chen, Luca Cimatti, Augustin Vercoutere, Mridul Aanjaneya, Rebecca Kramer-Bottiglio, Kostas E. Bekris
Robotics
VLAD-Grasp: Zero-shot Grasp Detection via Vision-Language Models
Manav Kulshrestha, S. Talha Bukhari, Damon Conover et al. (+1 more)
Robotic grasping is a fundamental capability for enabling autonomous manipulation, with usually infinite solutions. State-of-the-art approaches for grasping rely on learning from large-scale
datasets comprising expert annotations of feasible grasps. Curating such
datasets is challenging, and hence, learning-based methods are limited by the solution coverage of the
dataset, and require retraining to handle novel objects. Towards this, we present VLAD-Grasp, a
Vision-
Language model Assisted zero-shot approach for Detecting Grasps. Our method (1)
prompts a large
vision-
language model to generate a goal image where a virtual cylindrical proxy intersects the object's geometry, explicitly encoding an antipodal grasp axis in image space, then (2) predicts depth and
segmentation to lift this generated image into 3D, and (3) aligns generated and observed object point clouds via principal components and correspondence-free optimization to recover an executable grasp pose. Unlike prior work, our approach is training-free and does not require curated grasp
datasets, while achieving performance competitive with the state-of-the-art methods on the Cornell and Jacquard
datasets. Furthermore, we demonstrate zero-shot generalization to real-world objects on a Franka Research 3
robot, highlighting
vision-
language models as powerful priors for robotic manipulation.
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All authors Manav Kulshrestha, S. Talha Bukhari, Damon Conover, Aniket Bera
Robotics
A Unified Stochastic Mechanism Underlying Collective Behavior in Ants, Physical Systems, and Robotic Swarms
Lianhao Yin, Haiping Yu, Pascal Spino et al. (+1 more)
Biological swarms, such as ant colonies, achieve collective goals through decentralized and
stochastic individual behaviors. Similarly, physical systems composed of gases,
liquids, and solids exhibit random particle motion governed by
entropy maximization, yet do not achieve collective objectives. Despite this analogy, no unified framework exists to explain the
stochastic behavior in both biological and physical systems. Here, we present empirical evidence from Formica polyctena ants that reveals a shared statistical mechanism underlying both systems: maximization under different energy function constraints. We further demonstrate that robotic swarms governed by this principle can exhibit scalable, decentralized cooperation, mimicking physical phase-like behaviors with minimal individual computation. These findings established a unified
stochastic model linking biological, physical, and robotic swarms, offering a scalable principle for designing robust and intelligent swarm robotics.
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All authors Lianhao Yin, Haiping Yu, Pascal Spino, Daniela Rus