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Computer Science Robotics

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Sunday 26 October

Robotics

Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines

Shenbagaraj Kannapiran, Elena Oikonomou, Albert Chu et al. (+2 more)

In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based controller that uses encounter-triggered transitions to create rigid, stable walls. The second integrates FSM states with a Deep Q-Network (DQN), dynamically optimizing separation through emergent "demilitarized zones." In simulation, both controllers reduce mixing between subgroups, with the DQN-enhanced controller improving adaptability and reducing mixing by 40-50% while achieving faster convergence.
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All authors Shenbagaraj Kannapiran, Elena Oikonomou, Albert Chu, Spring Berman, Theodore P. Pavlic

Robotics

On Steerability Factors for Growing Vine Robots

Ciera McFarland, Antonio Alvarez, Sarah Taher et al. (+2 more)

Vine robots extend their tubular bodies by everting material from the tip, enabling navigation in complex environments with a minimalist soft body. Despite their promise for field applications, especially in the urban search and rescue domain, performance is constrained by the weight of attached sensors or tools, as well as other design and control choices. This work investigates how tip load, pressure, length, diameter, and fabrication method shape vine robot steerability–the ability to maneuver with controlled curvature–for robots that steer with series pouch motor-style pneumatic actuators. We conduct two groups of experiments: (1) studying tip load, chamber pressure, length, and diameter in a robot supporting itself against gravity, and (2) studying fabrication method and ratio of actuator to chamber pressure in a robot supported on the ground. Results show that steerability decreases with increasing tip load, is best at moderate chamber pressure, increases with length, and is largely unaffected by diameter. Robots with actuators attached on their exterior begin curving at low pressure ratios, but curvature saturates at high pressure ratios; those with actuators integrated into the robot body require higher pressure ratios to begin curving but achieve higher curvature overall. We demonstrate that robots optimized with these principles outperform those with ad hoc parameters in a mobility task that involves maximizing upward and horizontal curvatures.
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All authors Ciera McFarland, Antonio Alvarez, Sarah Taher, Nathaniel Hanson, Margaret McGuinness

Robotics

Forward Kinematics Solution For A General Stewart Platform Through Iteration Based Simulation

Sourabh Karmakar and Cameron J. Turner

This paper presents a method to generate feasible, unique forward-kinematic solutions for a general Stewart platform. This is done by using inverse kinematics to obtain valid workspace data and corresponding actuator lengths for the moving platform. For parallel kinematic machines, such as the Stewart Platform, inverse kinematics are straight forward, but the forward kinematics are complex and generates multiple solutions due to the closed loop structure of the kinematic links. In this research, a simple iterative algorithm has been used employing modified Denavit-Hartenberg convention. The outcome is encouraging as this method generates a single feasible forward kinematic solution for each valid pose with the solved DH parameters and unlike earlier forward kinematics solutions, this unique solution does not need to be manually verified. Therefore, the forward kinematic solutions can be used directly for further calculations without the need for manual pose verification. This capability is essential for the six degree of freedom materials testing system developed by the authors in their laboratory. The developed system is aimed at characterizing additively manufactured materials under complex combined multiple loading conditions. The material characterization is done by enabling high precision force control on the moving platform via in situ calibration of the as-built kinematics of the Stewart Gough Platform.
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Saturday 25 October

Robotics

A short methodological review on social robot navigation benchmarking

Pranup Chhetri, Alejandro Torrejon, Sergio Eslava et al. (+1 more)

Social Robot Navigation is the skill that allows robots to move efficiently in human-populated environments while ensuring safety, comfort, and trust. Unlike other areas of research, the scientific community has not yet achieved an agreement on how Social Robot Navigation should be benchmarked. This is notably important, as the lack of a de facto standard to benchmark Social Robot Navigation can hinder the progress of the field and may lead to contradicting conclusions. Motivated by this gap, we contribute with a short review focused exclusively on benchmarking trends in the period from January 2020 to July 2025. Of the 130 papers identified by our search using IEEE Xplore, we analysed the 85 papers that met the criteria of the review. This review addresses the metrics used in the literature for benchmarking purposes, the algorithms employed in such benchmarks, the use of human surveys for benchmarking, and how conclusions are drawn from the benchmarking results, when applicable.
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All authors Pranup Chhetri, Alejandro Torrejon, Sergio Eslava, Luis J. Manso

Robotics · Cross-listed from cs.DC

Separation of Unconscious Robots with Obstructed Visibility

Prajyot Pyati, Navjot Kaur, Saswata Jana et al. (+2 more)

We study a recently introduced unconscious mobile robot model, where each robot is associated with a color, which is visible to other robots but not to itself. The robots are autonomous, anonymous, oblivious and silent, operating in the Euclidean plane under the conventional Look-Compute-Move cycle. A primary task in this model is the separation problem, where unconscious robots sharing the same color must separate from others, forming recognizable geometric shapes such as circles, points, or lines. All prior works model the robots as transparent, enabling each to know the positions and colors of all other robots. In contrast, we model the robots as opaque, where a robot can obstruct the visibility of two other robots, if it lies on the line segment between them. Under this obstructed visibility, we consider a variant of the separation problem in which robots, starting from any arbitrary initial configuration, are required to separate into concentric semicircles. We present a collision-free algorithm that solves the separation problem under a semi-synchronous scheduler in $O(n)$ epochs, where $n$ is the number of robots. The robots agree on one coordinate axis but have no knowledge of $n$.
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All authors Prajyot Pyati, Navjot Kaur, Saswata Jana, Adri Bhattacharya, Partha Sarathi Mandal

Robotics

A Novel Multi-Timescale Stability-Preserving Hierarchical Reinforcement Learning Controller Framework for Adaptive Control in High-Dimensional Dynamical Systems

Mohammad Ali Labbaf Khaniki, Fateme Taroodi and Benyamin Safizadeh

Controlling high-dimensional stochastic systems, critical in robotics, autonomous vehicles, and hyperchaotic systems, faces the curse of dimensionality, lacks temporal abstraction, and often fails to ensure stochastic stability. To overcome these limitations, this study introduces the Multi-Timescale Lyapunov-Constrained Hierarchical Reinforcement Learning (MTLHRL) framework. MTLHRL integrates a hierarchical policy within a semi-Markov Decision Process (SMDP), featuring a high-level policy for strategic planning and a low-level policy for reactive control, which effectively manages complex, multi-timescale decision-making and reduces dimensionality overhead. Stability is rigorously enforced using a neural Lyapunov function optimized via Lagrangian relaxation and multi-timescale actor-critic updates, ensuring mean-square boundedness or asymptotic stability in the face of stochastic dynamics. The framework promotes efficient and reliable learning through trust-region constraints and decoupled optimization. Extensive simulations on an 8D hyperchaotic system and a 5-DOF robotic manipulator demonstrate MTLHRL's empirical superiority. It significantly outperforms baseline methods in both stability and performance, recording the lowest error indices (e.g., Integral Absolute Error (IAE): 3.912 in hyperchaotic control and IAE: 1.623 in robotics), achieving faster convergence, and exhibiting superior disturbance rejection. MTLHRL offers a theoretically grounded and practically viable solution for robust control of complex stochastic systems.
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Robotics

BLIP-FusePPO: A Vision-Language Deep Reinforcement Learning Framework for Lane Keeping in Autonomous Vehicles

Seyed Ahmad Hosseini Miangoleh, Amin Jalal Aghdasian and Farzaneh Abdollahi

In this paper, we propose Bootstrapped Language-Image Pretraining-driven Fused State Representation in Proximal Policy Optimization (BLIP-FusePPO), a novel multimodal reinforcement learning (RL) framework for autonomous lane-keeping (LK), in which semantic embeddings generated by a vision-language model (VLM) are directly fused with geometric states, LiDAR observations, and Proportional-Integral-Derivative-based (PID) control feedback within the agent observation space. The proposed method lets the agent learn driving rules that are aware of their surroundings and easy to understand by combining high-level scene understanding from the VLM with low-level control and spatial signals. Our architecture brings together semantic, geometric, and control-aware representations to make policy learning more robust. A hybrid reward function that includes semantic alignment, LK accuracy, obstacle avoidance, and speed regulation helps learning to be more efficient and generalizable. Our method is different from the approaches that only use semantic models to shape rewards. Instead, it directly embeds semantic features into the state representation. This cuts down on expensive runtime inference and makes sure that semantic guidance is always available. The simulation results show that the proposed model is better at LK stability and adaptability than the best vision-based and multimodal RL baselines in a wide range of difficult driving situations. We make our code publicly available.
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Robotics

Estimating Continuum Robot Shape under External Loading using Spatiotemporal Neural Networks

Enyi Wang, Zhen Deng, Chuanchuan Pan et al. (+2 more)

This paper presents a learning-based approach for accurately estimating the 3D shape of flexible continuum robots subjected to external loads. The proposed method introduces a spatiotemporal neural network architecture that fuses multi-modal inputs, including current and historical tendon displacement data and RGB images, to generate point clouds representing the robot's deformed configuration. The network integrates a recurrent neural module for temporal feature extraction, an encoding module for spatial feature extraction, and a multi-modal fusion module to combine spatial features extracted from visual data with temporal dependencies from historical actuator inputs. Continuous 3D shape reconstruction is achieved by fitting Bézier curves to the predicted point clouds. Experimental validation demonstrates that our approach achieves high precision, with mean shape estimation errors of 0.08 mm (unloaded) and 0.22 mm (loaded), outperforming state-of-the-art methods in shape sensing for TDCRs. The results validate the efficacy of deep learning-based spatiotemporal data fusion for precise shape estimation under loading conditions.
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All authors Enyi Wang, Zhen Deng, Chuanchuan Pan, Bingwei He, Jianwei Zhang

Robotics

Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery

Bo Yue, Sheng Xu, Kui Jia et al. (+1 more)

Humanoid robots represent a central frontier in embodied intelligence, as their anthropomorphic form enables natural deployment in humans' workspace. Brain-body co-design for humanoids presents a promising approach to realizing this potential by jointly optimizing control policies and physical morphology. Within this context, fall recovery emerges as a critical capability. It not only enhances safety and resilience but also integrates naturally with locomotion systems, thereby advancing the autonomy of humanoids. In this paper, we propose RoboCraft, a scalable humanoid co-design framework for fall recovery that iteratively improves performance through the coupled updates of control policy and morphology. A shared policy pretrained across multiple designs is progressively finetuned on high-performing morphologies, enabling efficient adaptation without retraining from scratch. Concurrently, morphology search is guided by human-inspired priors and optimization algorithms, supported by a priority buffer that balances reevaluation of promising candidates with the exploration of novel designs. Experiments show that RoboCraft achieves an average performance gain of 44.55% on seven public humanoid robots, with morphology optimization drives at least 40% of improvements in co-designing four humanoid robots, underscoring the critical role of humanoid co-design.
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All authors Bo Yue, Sheng Xu, Kui Jia, Guiliang Liu

Robotics

Breaking the Static Assumption: A Dynamic-Aware LIO Framework Via Spatio-Temporal Normal Analysis

Chen Zhiqiang, Le Gentil Cedric, Lin Fuling et al. (+5 more)

This paper addresses the challenge of Lidar-Inertial Odometry (LIO) in dynamic environments, where conventional methods often fail due to their static-world assumptions. Traditional LIO algorithms perform poorly when dynamic objects dominate the scenes, particularly in geometrically sparse environments. Current approaches to dynamic LIO face a fundamental challenge: accurate localization requires a reliable identification of static features, yet distinguishing dynamic objects necessitates precise pose estimation. Our solution breaks this circular dependency by integrating dynamic awareness directly into the point cloud registration process. We introduce a novel dynamic-aware iterative closest point algorithm that leverages spatio-temporal normal analysis, complemented by an efficient spatial consistency verification method to enhance static map construction. Experimental evaluations demonstrate significant performance improvements over state-of-the-art LIO systems in challenging dynamic environments with limited geometric structure. The code and dataset are available at https://github.com/thisparticle/btsa.
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All authors Chen Zhiqiang, Le Gentil Cedric, Lin Fuling, Lu Minghao, Qiao Qiyuan, Xu Bowen, Qi Yuhua, Lu Peng

Robotics · Cross-listed from cs.MA

CGoT: A Novel Inference Mechanism for Embodied Multi-Agent Systems Using Composable Graphs of Thoughts

Yixiao Nie, Yang Zhang, Yingjie Jin et al. (+3 more)

The integration of self-driving cars and service robots is becoming increasingly prevalent across a wide array of fields, playing a crucial and expanding role in both industrial applications and everyday life. In parallel, the rapid advancements in Large Language Models (LLMs) have garnered substantial attention and interest within the research community. This paper introduces a novel vehicle-robot system that leverages the strengths of both autonomous vehicles and service robots. In our proposed system, two autonomous ego-vehicles transports service robots to locations within an office park, where they perform a series of tasks. The study explores the feasibility and potential benefits of incorporating LLMs into this system, with the aim of enhancing operational efficiency and maximizing the potential of the cooperative mechanisms between the vehicles and the robots. This paper proposes a novel inference mechanism which is called CGOT toward this type of system where an agent can carry another agent. Experimental results are presented to validate the performance of the proposed method.
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All authors Yixiao Nie, Yang Zhang, Yingjie Jin, Zhepeng Wang, Xiu Li, Xiang Li

Robotics

Human-Inspired Neuro-Symbolic World Modeling and Logic Reasoning for Interpretable Safe UAV Landing Site Assessment

Weixian Qian, Tianyi Yang, Sebastian Schroder et al. (+5 more)

Reliable assessment of safe landing sites in unstructured environments is essential for deploying Unmanned Aerial Vehicles (UAVs) in real-world applications such as delivery, inspection, and surveillance. Existing learning-based approaches often degrade under covariate shift and offer limited transparency, making their decisions difficult to interpret and validate on resource-constrained platforms. We present NeuroSymLand, a neuro-symbolic framework for marker-free UAV landing site safety assessment that explicitly separates perception-driven world modeling from logic-based safety reasoning. A lightweight segmentation model incrementally constructs a probabilistic semantic scene graph encoding objects, attributes, and spatial relations. Symbolic safety rules, synthesized offline via large language models with human-in-the-loop refinement, are executed directly over this world model at runtime to perform white-box reasoning, producing ranked landing candidates with human-readable explanations of the underlying safety constraints. Across 72 simulated and hardware-in-the-loop landing scenarios, NeuroSymLand achieves 61 successful assessments, outperforming four competitive baselines, which achieve between 37 and 57 successes. Qualitative analysis highlights its superior interpretability and transparent reasoning, while deployment incurs negligible edge overhead. Our results suggest that combining explicit world modeling with symbolic reasoning can support accurate, interpretable, and edge-deployable safety assessment in mobile systems, as demonstrated through UAV landing site assessment.
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All authors Weixian Qian, Tianyi Yang, Sebastian Schroder, Yao Deng, Jiaohong Yao, Xiao Cheng, Richard Han, Xi Zheng

Robotics

ACG: Action Coherence Guidance for Flow-based Vision-Language-Action models

Minho Park, Kinam Kim, Junha Hyung et al. (+5 more)

Diffusion and flow matching models have emerged as powerful robot policies, enabling Vision-Language-Action (VLA) models to generalize across diverse scenes and instructions. Yet, when trained via imitation learning, their high generative capacity makes them sensitive to noise in human demonstrations: jerks, pauses, and jitter which reduce action coherence. Reduced action coherence causes instability and trajectory drift during deployment, failures that are catastrophic in fine-grained manipulation where precision is crucial. In this paper, we present Action Coherence Guidance (ACG) for VLA models, a training-free test-time guidance algorithm that improves action coherence and thereby yields performance gains. Evaluated on RoboCasa, DexMimicGen, and real-world SO-101 tasks, ACG consistently improves action coherence and boosts success rates across diverse manipulation tasks. Code and project page are available at https://github.com/DAVIAN-Robotics/ACG and https://DAVIAN-Robotics.github.io/ACG , respectively.
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All authors Minho Park, Kinam Kim, Junha Hyung, Hyojin Jang, Hoiyeong Jin, Jooyeol Yun, Hojoon Lee, Jaegul Choo

Robotics · Cross-listed from cs.CV

MOGRAS: Human Motion with Grasping in 3D Scenes

Kunal Bhosikar, Siddharth Katageri, Vivek Madhavaram et al. (+2 more)

Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack the fidelity for fine-grained tasks like object grasping. Conversely, methods that generate precise grasping motions typically ignore the surrounding 3D scene. This gap, generating full-body grasping motions that are physically plausible within a 3D scene, remains a significant challenge. To address this, we introduce MOGRAS (Human MOtion with GRAsping in 3D Scenes), a large-scale dataset that bridges this gap. MOGRAS provides pre-grasping full-body walking motions and final grasping poses within richly annotated 3D indoor scenes. We leverage MOGRAS to benchmark existing full-body grasping methods and demonstrate their limitations in scene-aware generation. Furthermore, we propose a simple yet effective method to adapt existing approaches to work seamlessly within 3D scenes. Through extensive quantitative and qualitative experiments, we validate the effectiveness of our dataset and highlight the significant improvements our proposed method achieves, paving the way for more realistic human-scene interactions.
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All authors Kunal Bhosikar, Siddharth Katageri, Vivek Madhavaram, Kai Han, Charu Sharma

Robotics

LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons

Jianeng Wang, Matias Mattamala, Christina Kassab et al. (+5 more)

Self-balancing exoskeletons offer a promising mobility solution for individuals with lower-limb disabilities. For reliable long-term operation, these exoskeletons require a perception system that is effective in changing environments. In this work, we introduce LT-Exosense, a vision-centric, multi-session mapping system designed to support long-term (semi)-autonomous navigation for exoskeleton users. LT-Exosense extends single-session mapping capabilities by incrementally fusing spatial knowledge across multiple sessions, detecting environmental changes, and updating a persistent global map. This representation enables intelligent path planning, which can adapt to newly observed obstacles and can recover previous routes when obstructions are removed. We validate LT-Exosense through several real-world experiments, demonstrating a scalable multi-session map that achieves an average point-to-point error below 5 cm when compared to ground-truth laser scans. We also illustrate the potential application of adaptive path planning in dynamically changing indoor environments.
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All authors Jianeng Wang, Matias Mattamala, Christina Kassab, Nived Chebrolu, Guillaume Burger, Fabio Elnecave, Marine Petriaux, Maurice Fallon

Robotics · Cross-listed from cs.CV

LOC: A General Language-Guided Framework for Open-Set 3D Occupancy Prediction

Yuhang Gao, Xiang Xiang, Sheng Zhong et al. (+1 more)

Vision-Language Models (VLMs) have shown significant progress in open-set challenges. However, the limited availability of 3D datasets hinders their effective application in 3D scene understanding. We propose LOC, a general language-guided framework adaptable to various occupancy networks, supporting both supervised and self-supervised learning paradigms. For self-supervised tasks, we employ a strategy that fuses multi-frame LiDAR points for dynamic/static scenes, using Poisson reconstruction to fill voids, and assigning semantics to voxels via K-Nearest Neighbor (KNN) to obtain comprehensive voxel representations. To mitigate feature over-homogenization caused by direct high-dimensional feature distillation, we introduce Densely Contrastive Learning (DCL). DCL leverages dense voxel semantic information and predefined textual prompts. This efficiently enhances open-set recognition without dense pixel-level supervision, and our framework can also leverage existing ground truth to further improve performance. Our model predicts dense voxel features embedded in the CLIP feature space, integrating textual and image pixel information, and classifies based on text and semantic similarity. Experiments on the nuScenes dataset demonstrate the method's superior performance, achieving high-precision predictions for known classes and distinguishing unknown classes without additional training data.
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All authors Yuhang Gao, Xiang Xiang, Sheng Zhong, Guoyou Wang

Robotics

EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control

Guanwen Xie, Jingzehua Xu, Jiwei Tang et al. (+6 more)

Despite recent advances in Unmanned Underwater Vehicle (UUV) attitude control, existing methods still struggle with generalizability, robustness to real-world disturbances, and efficient deployment. To address the above challenges, this paper presents EasyUUV, a Large Language Model (LLM)-enhanced, platform-agnostic, and lightweight simulation-to-reality reinforcement learning (RL) framework for robust attitude control of UUVs. EasyUUV combines parallelized RL training with a hybrid control architecture, where a learned policy outputs high-level attitude corrections executed by an adaptive S-Surface controller. A multimodal LLM is further integrated to adaptively tune controller parameters at runtime using visual and textual feedback, enabling training-free adaptation to unmodeled dynamics. Also, we have developed a low-cost 6-DoF UUV platform and applied an RL policy trained through efficient parallelized simulation. Extensive simulation and real-world experiments validate the effectiveness and adaptive performance of EasyUUV in achieving robust and adaptive UUV attitude control across diverse underwater conditions. To facilitate reproducibility, the source code, LLM prompts, video, and the supplementary material are provided in the following repositories: Homepage: https://360zmem.github.io/easyuuv/ Video:https://youtu.be/m2yLQzxiIL Supplementary Material: https://drive.google.com/file/d/1ImMiyGIPoPyj2ATnXQwbiuOroTSZ2SSs
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All authors Guanwen Xie, Jingzehua Xu, Jiwei Tang, Yubo Huang, Zixi Wang, Shuai Zhang, Dongfang Ma, Juntian Qu, Xiaofan Li

Robotics

RaycastGrasp: Eye-Gaze Interaction with Wearable Devices for Robotic Manipulation

Zitiantao Lin, Yongpeng Sang and Yang Ye

Robotic manipulators are increasingly used to assist individuals with mobility impairments in object retrieval. However, the predominant joystick-based control interfaces can be challenging due to high precision requirements and unintuitive reference frames. Recent advances in human-robot interaction have explored alternative modalities, yet many solutions still rely on external screens or restrictive control schemes, limiting their intuitiveness and accessibility. To address these challenges, we present an egocentric, gaze-guided robotic manipulation interface that leverages a wearable Mixed Reality (MR) headset. Our system enables users to interact seamlessly with real-world objects using natural gaze fixation from a first-person perspective, while providing augmented visual cues to confirm intent and leveraging a pretrained vision model and robotic arm for intent recognition and object manipulation. Experimental results demonstrate that our approach significantly improves manipulation accuracy, reduces system latency, and achieves single-pass intention and object recognition accuracy greater than 88% across multiple real-world scenarios. These results demonstrate the system's effectiveness in enhancing intuitiveness and accessibility, underscoring its practical significance for assistive robotics applications.
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Friday 24 October

Robotics · Cross-listed from cs.CV

Point-level Uncertainty Evaluation of Mobile Laser Scanning Point Clouds

Ziyang Xu, Olaf Wysocki and Christoph Holst

Reliable quantification of uncertainty in Mobile Laser Scanning (MLS) point clouds is essential for ensuring the accuracy and credibility of downstream applications such as 3D mapping, modeling, and change analysis. Traditional backward uncertainty modeling heavily rely on high-precision reference data, which are often costly or infeasible to obtain at large scales. To address this issue, this study proposes a machine learning-based framework for point-level uncertainty evaluation that learns the relationship between local geometric features and point-level errors. The framework is implemented using two ensemble learning models, Random Forest (RF) and XGBoost, which are trained and validated on a spatially partitioned real-world dataset to avoid data leakage. Experimental results demonstrate that both models can effectively capture the nonlinear relationships between geometric characteristics and uncertainty, achieving mean ROC-AUC values above 0.87. The analysis further reveals that geometric features describing elevation variation, point density, and local structural complexity play a dominant role in predicting uncertainty. The proposed framework offers a data-driven perspective of uncertainty evaluation, providing a scalable and adaptable foundation for future quality control and error analysis of large-scale point clouds.
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Robotics

Estimation of Minimum Stride Frequency for the Frontal Plane Stability of Bipedal Systems

Harsha Karunanayaka and Siavash Rezazadeh

Stability of bipedal systems in frontal plane is affected by the hip offset, to the extent that adjusting stride time using feedforward retraction and extension of the legs can lead to stable oscillations without feedback control. This feedforward stabilization can be leveraged to reduce the control effort and energy expenditure and increase the locomotion robustness. However, there is limited understanding of how key parameters, such as mass, stiffness, leg length, and hip width, affect stability and the minimum stride frequency needed to maintain it. This study aims to address these gaps through analyzing how individual model parameters and the system's natural frequency influence the minimum stride frequency required to maintain a stable cycle. We propose a method to predict the minimum stride frequency, and compare the predicted stride frequencies with actual values for randomly generated models. The findings of this work provide a better understanding of the frontal plane stability mechanisms and how feedforward stabilization can be leveraged to reduce the control effort.
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Robotics

Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising

Mateo Clemente, Leo Brunswic, Rui Heng Yang et al. (+5 more)

Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originally developed for vision tasks like image and video generation, many of their inference strategies have been directly transferred to control domains without adaptation. In this work, we show that by tailoring the denoising process to the specific characteristics of embodied AI tasks – particularly structured, low-dimensional nature of action distributions – diffusion policies can operate effectively with as few as 5 neural function evaluations (NFE). Building on this insight, we propose a population-based sampling strategy, genetic denoising, which enhances both performance and stability by selecting denoising trajectories with low out-of-distribution risk. Our method solves challenging tasks with only 2 NFE while improving or matching performance. We evaluate our approach across 14 robotic manipulation tasks from D4RL and Robomimic, spanning multiple action horizons and inference budgets. In over 2 million evaluations, our method consistently outperforms standard diffusion-based policies, achieving up to 20% performance gains with significantly fewer inference steps.
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All authors Mateo Clemente, Leo Brunswic, Rui Heng Yang, Xuan Zhao, Yasser Khalil, Haoyu Lei, Amir Rasouli, Yinchuan Li

Robotics

Design and Structural Validation of a Micro-UAV with On-Board Dynamic Route Planning

Inbazhagan Ravikumar, Ram Sundhar and Narendhiran Vijayakumar

Micro aerial vehicles are becoming increasingly important in search and rescue operations due to their agility, speed, and ability to access confined spaces or hazardous areas. However, designing lightweight aerial systems presents significant structural, aerodynamic, and computational challenges. This work addresses two key limitations in many low-cost aerial systems under two kilograms: their lack of structural durability during flight through rough terrains and inability to replan paths dynamically when new victims or obstacles are detected. We present a fully customised drone built from scratch using only commonly available components and materials, emphasising modularity, low cost, and ease of assembly. The structural frame is reinforced with lightweight yet durable materials to withstand impact, while the onboard control system is powered entirely by free, open-source software solutions. The proposed system demonstrates real-time perception and adaptive navigation capabilities without relying on expensive hardware accelerators, offering an affordable and practical solution for real-world search and rescue missions.
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Robotics

Enhancing Tactile-based Reinforcement Learning for Robotic Control

Elle Miller, Trevor McInroe, David Abel et al. (+2 more)

Achieving safe, reliable real-world robotic manipulation requires agents to evolve beyond vision and incorporate tactile sensing to overcome sensory deficits and reliance on idealised state information. Despite its potential, the efficacy of tactile sensing in reinforcement learning (RL) remains inconsistent. We address this by developing self-supervised learning (SSL) methodologies to more effectively harness tactile observations, focusing on a scalable setup of proprioception and sparse binary contacts. We empirically demonstrate that sparse binary tactile signals are critical for dexterity, particularly for interactions that proprioceptive control errors do not register, such as decoupled robot-object motions. Our agents achieve superhuman dexterity in complex contact tasks (ball bouncing and Baoding ball rotation). Furthermore, we find that decoupling the SSL memory from the on-policy memory can improve performance. We release the Robot Tactile Olympiad (RoTO) benchmark to standardise and promote future research in tactile-based manipulation. Project page: https://elle-miller.github.io/tactile_rl
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All authors Elle Miller, Trevor McInroe, David Abel, Oisin Mac Aodha, Sethu Vijayakumar

Robotics · Cross-listed from cs.CV

MATrack: Efficient Multiscale Adaptive Tracker for Real-Time Nighttime UAV Operations

Xuzhao Li, Xuchen Li and Shiyu Hu

Nighttime UAV tracking faces significant challenges in real-world robotics operations. Low-light conditions not only limit visual perception capabilities, but cluttered backgrounds and frequent viewpoint changes also cause existing trackers to drift or fail during deployment. To address these difficulties, researchers have proposed solutions based on low-light enhancement and domain adaptation. However, these methods still have notable shortcomings in actual UAV systems: low-light enhancement often introduces visual artifacts, domain adaptation methods are computationally expensive and existing lightweight designs struggle to fully leverage dynamic object information. Based on an in-depth analysis of these key issues, we propose MATrack-a multiscale adaptive system designed specifically for nighttime UAV tracking. MATrack tackles the main technical challenges of nighttime tracking through the collaborative work of three core modules: Multiscale Hierarchy Blende (MHB) enhances feature consistency between static and dynamic templates. Adaptive Key Token Gate accurately identifies object information within complex backgrounds. Nighttime Template Calibrator (NTC) ensures stable tracking performance over long sequences. Extensive experiments show that MATrack achieves a significant performance improvement. On the UAVDark135 benchmark, its precision, normalized precision and AUC surpass state-of-the-art (SOTA) methods by 5.9%, 5.4% and 4.2% respectively, while maintaining a real-time processing speed of 81 FPS. Further tests on a real-world UAV platform validate the system's reliability, demonstrating that MATrack can provide stable and effective nighttime UAV tracking support for critical robotics applications such as nighttime search and rescue and border patrol.
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Robotics

Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos

Qixiu Li, Yu Deng, Yaobo Liang et al. (+14 more)

This paper presents a novel approach for pretraining robotic manipulation Vision-Language-Action (VLA) models using a large corpus of unscripted real-life video recordings of human hand activities. Treating human hand as dexterous robot end-effector, we show that "in-the-wild" egocentric human videos without any annotations can be transformed into data formats fully aligned with existing robotic V-L-A training data in terms of task granularity and labels. This is achieved by the development of a fully-automated holistic human activity analysis approach for arbitrary human hand videos. This approach can generate atomic-level hand activity segments and their language descriptions, each accompanied with framewise 3D hand motion and camera motion. We process a large volume of egocentric videos and create a hand-VLA training dataset containing 1M episodes and 26M frames. This training data covers a wide range of objects and concepts, dexterous manipulation tasks, and environment variations in real life, vastly exceeding the coverage of existing robot data. We design a dexterous hand VLA model architecture and pretrain the model on this dataset. The model exhibits strong zero-shot capabilities on completely unseen real-world observations. Additionally, fine-tuning it on a small amount of real robot action data significantly improves task success rates and generalization to novel objects in real robotic experiments. We also demonstrate the appealing scaling behavior of the model's task performance with respect to pretraining data scale. We believe this work lays a solid foundation for scalable VLA pretraining, advancing robots toward truly generalizable embodied intelligence.
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All authors Qixiu Li, Yu Deng, Yaobo Liang, Lin Luo, Lei Zhou, Chengtang Yao, Lingqi Zeng, Zhiyuan Feng, Huizhi Liang, Sicheng Xu, Yizhong Zhang, Xi Chen, Hao Chen, Lily Sun, Dong Chen, Jiaolong Yang, Baining Guo

Robotics · Cross-listed from cs.AI

Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning

Yuxuan Yang and Hussein Sibai

Safety is a fundamental requirement for autonomous systems operating in critical domains. Control barrier functions (CBFs) have been used to design safety filters that minimally alter nominal controls for such systems to maintain their safety. Learning neural CBFs has been proposed as a data-driven alternative for their computationally expensive optimization-based synthesis. However, it is often the case that the failure set of states that should be avoided is non-obvious or hard to specify formally, e.g., tailgating in autonomous driving, while a set of expert demonstrations that achieve the task and avoid the failure set is easier to generate. We use ICL to train a constraint function that classifies the states of the system under consideration to safe, i.e., belong to a controlled forward invariant set that is disjoint from the unspecified failure set, and unsafe ones, i.e., belong to the complement of that set. We then use that function to label a new set of simulated trajectories to train our neural CBF. We empirically evaluate our approach in four different environments, demonstrating that it outperforms existing baselines and achieves comparable performance to a neural CBF trained with the same data but annotated with ground-truth safety labels.
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Robotics

AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation

Narendhiran Vijayakumar

Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-scene environments. However, conventional encoder-decoder models often recover coarse region masks while losing the fine spatial information needed to localize drivable-area boundaries accurately. We propose Attention-Guided Upsampling with Residual-Assisted Boundary Refinement (AURASeg), a segmentation framework designed to preserve region-level accuracy while improving boundary quality. Built on a ResNet-18 encoder, AURASeg introduces an Attention Progressive Upsampling Decoder (APUD) that progressively combines semantic context with high-resolution spatial detail, together with a Residual Boundary Refinement Module (RBRM) that explicitly refines contour-sensitive features before final prediction. We evaluate AURASeg across indoor simulation, ground-robot imagery, and road-driving benchmarks. The results show that our proposed model remains competitive with established segmentation models on region-level metrics while providing particularly strong boundary localization, including in comparison with boundary-focused methods. Detailed ablations further demonstrate the role of the proposed decoding and refinement modules.
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Robotics

Enhancing Social Robots through Resilient AI

Domenico Palmisano, Giuseppe Palestra and Berardina Nadja De Carolis

As artificial intelligence continues to advance and becomes more integrated into sensitive areas like healthcare, education, and everyday life, it's crucial for these systems to be both resilient and robust. This paper shows how resilience is a fundamental characteristic of social robots, which, through it, ensure trust in the robot itself-an essential element especially when operating in contexts with elderly people, who often have low trust in these systems. Resilience is therefore the ability to operate under adverse or stressful conditions, even when degraded or weakened, while maintaining essential operational capabilities.
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Robotics · Cross-listed from cs.CV

PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis

Yu Yang, Zhilu Zhang, Xiang Zhang et al. (+3 more)

Interactive world models that simulate object dynamics are crucial for robotics, VR, and AR. However, it remains a significant challenge to learn physics-consistent dynamics models from limited real-world video data, especially for deformable objects with spatially-varying physical properties. To overcome the challenge of data scarcity, we propose PhysWorld, a novel framework that utilizes a simulator to synthesize physically plausible and diverse demonstrations to learn efficient world models. Specifically, we first construct a physics-consistent digital twin within MPM simulator via constitutive model selection and global-to-local optimization of physical properties. Subsequently, we apply part-aware perturbations to the physical properties and generate various motion patterns for the digital twin, synthesizing extensive and diverse demonstrations. Finally, using these demonstrations, we train a lightweight GNN-based world model that is embedded with physical properties. The real video can be used to further refine the physical properties. PhysWorld achieves accurate and fast future predictions for various deformable objects, and also generalizes well to novel interactions. Experiments show that PhysWorld has competitive performance while enabling inference speeds 47 times faster than the recent state-of-the-art method, i.e., PhysTwin.
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All authors Yu Yang, Zhilu Zhang, Xiang Zhang, Yihan Zeng, Hui Li, Wangmeng Zuo

Robotics

PREVENT: Proactive Risk Evaluation and Vigilant Execution of Tasks for Mobile Robotic Chemists using Multi-Modal Behavior Trees

Satheeshkumar Veeramani, Zhengxue Zhou, Francisco Munguia-Galeano et al. (+5 more)

Mobile robotic chemists are a fast growing trend in the field of chemistry and materials research. However, so far these mobile robots lack workflow awareness skills. This poses the risk that even a small anomaly, such as an improperly capped sample vial could disrupt the entire workflow. This wastes time, and resources, and could pose risks to human researchers, such as exposure to toxic materials. Existing perception mechanisms can be used to predict anomalies but they often generate excessive false positives. This may halt workflow execution unnecessarily, requiring researchers to intervene and to resume the workflow when no problem actually exists, negating the benefits of autonomous operation. To address this problem, we propose PREVENT a system comprising navigation and manipulation skills based on a multimodal Behavior Tree (BT) approach that can be integrated into existing software architectures with minimal modifications. Our approach involves a hierarchical perception mechanism that exploits AI techniques and sensory feedback through Dexterous Vision and Navigational Vision cameras and an IoT gas sensor module for execution-related decision-making. Experimental evaluations show that the proposed approach is comparatively efficient and completely avoids both false negatives and false positives when tested in simulated risk scenarios within our robotic chemistry workflow. The results also show that the proposed multi-modal perception skills achieved deployment accuracies that were higher than the average of the corresponding uni-modal skills, both for navigation and for manipulation.
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All authors Satheeshkumar Veeramani, Zhengxue Zhou, Francisco Munguia-Galeano, Hatem Fakhruldeen, Thomas Roddelkopf, Mohammed Faeik Ruzaij Al-Okby, Kerstin Thurow, Andrew Ian Cooper