Robotics · Cross-listed from eess.SY
A K-means Inspired Solution Framework for Large-Scale Multi-Traveling Salesman Problems
Xiubin Chen
The Multi-Traveling Salesman Problem (MTSP) is a commonly used mathematical model for multi-
agent task allocation. However, as the number of
agents and task targets increases, existing optimization-based methods often incur prohibitive computational costs, posing significant challenges to large-scale coordination in unmanned systems. To address this issue, this paper proposes a K-means-inspired task allocation framework that reformulates the MTSP as a spatially constrained classification process. By leveraging spatial coherence, the proposed method enables fast estimation of path costs and efficient task grouping, thereby fundamentally reducing overall computational complexity. Extensive simulation results demonstrate that the framework can maintain high solution quality even in extremely large-scale scenarios-for instance, in tasks involving 1000
agents and 5000 targets. The findings indicate that this "cluster-then-route" decomposition strategy offers an efficient and reliable solution for large-scale multi-
agent task allocation.
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Robotics · Cross-listed from cs.DC
Computational Onboard Data Management for Heterogeneous Autonomous Vehicle Streams
Yuxin Wang, Yuankai He, Damien Kah et al. (+1 more)
Autonomous vehicles continuously generate large volumes of heterogeneous sensor and state data streams. While this data supports real-time autonomy, it also enables emerging vehicle computing applications that require retained historical context. Existing in-vehicle logging stacks are largely replay- or compliance-oriented: rich sensor streams are often discarded after online processing or recorded only in limited windows for offline replay. This creates a new onboard vehicle-data operating point: transforming transient
multimodal streams into compact, long-horizon, queryable history under embedded resource constraints. We present Autonomous Vehicle Storage (AVS), a computational and hierarchical in-vehicle data management system for this operating point. AVS introduces a vehicle-specialized architecture including (i) utility-aware modality preprocessing before persistence, (ii) a chunk-indexed append-only logger for sustained ingest, time-range lookup, and prefix recovery, and (iii) an SSD-HDD hot/cold hierarchy that supports direct cold queries. Evaluated on a real L4 platform with three days of traces and concurrent
LiDAR, camera, and GPS logging, AVS reduces storage by 8.0-8.7x relative to raw rosbag2 from its modality and task aware preprocessing, delivers hot-tier (SSD) query Time To First Byte (TTFB) of 0.9-9.1 ms and cold-tier (HDD) query TTFB of 25.6-44.0 ms, recovers from power loss in 9.98 s with a bounded 0.24 s loss window, and sustains background archival to HDD at about 107-108 MB/s. It shows the feasibility of AVS on real in-vehicle platforms, and it also exposes key tradeoffs and open challenges for vehicle data engineering.
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All authors Yuxin Wang, Yuankai He, Damien Kah, Weisong Shi
Robotics · Cross-listed from eess.SY
Strong Duality and Dual Ascent Approach to Continuous-Time Chance-Constrained Stochastic Optimal Control
Apurva Patil, Alfredo Duarte, Fabrizio Bisetti et al. (+1 more)
The paper addresses a continuous-time continuous-space chance-constrained
stochastic optimal control (SOC) problem where the probability of failure to satisfy given state constraints is explicitly bounded. We leverage the notion of exit time from continuous-time
stochastic calculus to formulate a chance-constrained SOC problem. Without any conservative approximation, the chance constraint is transformed into an expectation of an indicator function which can be incorporated into the cost function by considering a dual formulation. We then express the dual function in terms of the solution to a Hamilton-Jacobi-Bellman partial differential equation parameterized by the dual variable. Under a certain assumption on the system dynamics and cost function, it is shown that a strong duality holds between the primal chance-constrained problem and its dual. The Path integral approach is utilized to numerically solve the dual problem via gradient ascent using open-loop samples of system trajectories. We present simulation studies on chance-constrained motion planning for spatial
navigation of mobile
robots and the solution of the path integral approach is compared with that of the finite difference method.
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All authors Apurva Patil, Alfredo Duarte, Fabrizio Bisetti, Takashi Tanaka
Robotics
I've Changed My Mind: Robots Adapting to Changing Human Goals during Collaboration
Debasmita Ghose, Oz Gitelson, Ryan Jin et al. (+3 more)
For effective human-
robot collaboration, a
robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world scenarios, humans frequently shift goals, making it challenging for
robots to adapt without explicit communication. We propose a method for detecting goal changes by tracking multiple candidate action sequences and verifying their plausibility against a policy bank. Upon detecting a change, the
robot refines its belief in relevant past actions and constructs Receding Horizon Planning (RHP) trees to actively select actions that assist the human while encouraging Differentiating Actions to reveal their updated goal. We evaluate our approach in a collaborative cooking environment with up to 30 unique recipes and compare it to three comparable human goal prediction
algorithms. Our method outperforms all baselines, quickly converging to the correct goal after a switch, reducing task completion time, and improving collaboration efficiency.
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All authors Debasmita Ghose, Oz Gitelson, Ryan Jin, Grace Abawe, Marynel Vazquez, Brian Scassellati
Robotics
Gimballed Rotor Mechanism for Omnidirectional Quadrotors
J. Cristobal, A. Z. Zain Aldeen, M. Izadi et al. (+1 more)
This paper presents the design of a gimballed rotor mechanism as a modular and efficient solution for constructing omnidirectional quadrotors. Unlike conventional quadrotors, which are underactuated, this class of quadrotors achieves full actuation, enabling independent motion in all six degrees of freedom. While existing omnidirectional quadrotor designs often require significant structural modifications, the proposed gimballed rotor system maintains a lightweight and easy-to-integrate design by incorporating servo motors within the rotor platforms, allowing independent tilting of each rotor without major alterations to the central structure of a quadrotor. To accommodate this unconventional design, we develop a new control allocation scheme in PX4 Autopilot and present successful flight tests, validating the effectiveness of the proposed approach.
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All authors J. Cristobal, A. Z. Zain Aldeen, M. Izadi, R. Faieghi
Robotics
In-N-On: Scaling Egocentric Manipulation with in-the-wild and on-task Data
Xiongyi Cai, Ri-Zhao Qiu, Geng Chen et al. (+5 more)
Egocentric videos are a valuable and scalable data source to learn manipulation policies. However, due to significant data heterogeneity, most existing approaches utilize human data for simple pre-training, which does not unlock its full potential. This paper first provides a scalable recipe for collecting and using
egocentric data by categorizing human data into two categories: in-the-wild and on-task alongside with systematic analysis on how to use the data. We first curate a
dataset, PHSD, which contains over 1,000 hours of diverse in-the-wild
egocentric data and over 20 hours of on-task data directly aligned to the target manipulation tasks. This enables learning a large
egocentric language-conditioned flow matching policy, Human0. With domain adaptation techniques, Human0 minimizes the gap between humans and
humanoids. Empirically, we show Human0 achieves several novel properties from scaling human data, including
language following of instructions from only human data, few-shot learning, and improved robustness using on-task data. Project website: https://xiongyicai.github.io/In-N-On/
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All authors Xiongyi Cai, Ri-Zhao Qiu, Geng Chen, Lai Wei, Isabella Liu, Tianshu Huang, Xuxin Cheng, Xiaolong Wang
Robotics · Cross-listed from cs.CV
FDIO: Frequency Decomposed Inertial Odometry
Shanshan Zhang, Liqin Wu, Wenying Cao et al. (+2 more)
Pedestrian inertial odometry (PIO) estimates autonomous pedestrian motion using only acceleration and angular velocity measurements collected by an inertial measurement unit (IMU), making it highly valuable for consumer level localization applications. However, under a dual device acquisition setting, IMU signals collected by a freely carried mobile device are inherently composite signals in which the global motion of the human torso is coupled with perturbations induced by local limb motion. This coupling makes accurate human motion modeling more challenging. To address this issue, this paper proposes frequency decomposed inertial odometry (FDIO). The proposed method first decomposes input IMU signals into low frequency and high frequency components using a
Laplacian pyramid. It then adopts a Mamba module to model long range motion information from the low frequency component and uses a multi scale convolution module to extract fine grained local dynamic features from the high frequency component. Experiments on five public PIO
datasets show that FDIO achieves an average absolute trajectory error of 3.221~m and an average relative trajectory error of 2.550~m, reducing the errors by 33.3% and 16.7% compared with the RoNIN ResNet baseline, respectively. These results validate the effectiveness of the proposed frequency decomposition strategy. To the best of our knowledge, this work is among the first efforts to introduce Mamba and a frequency decomposition architecture into inertial odometry.
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All authors Shanshan Zhang, Liqin Wu, Wenying Cao, Lingxiang Zheng, Yu Yang
Robotics
Optimus-Q: Utilizing Federated Learning in Adaptive Robots for Intelligent Nuclear Power Plant Operations through Quantum Cryptography
Sai Puppala, Ismail Hossain, Jahangir Alam et al. (+1 more)
The integration of advanced robotics in
nuclear power plants (NPPs) presents a transformative opportunity to enhance safety, efficiency, and environmental monitoring in high-stakes environments. Our paper introduces the Optimus-Q
robot, a sophisticated system designed to autonomously monitor air quality and detect contamination while leveraging adaptive learning techniques and secure
quantum communication. Equipped with advanced infrared sensors, the Optimus-Q
robot continuously streams real-time environmental data to predict hazardous gas emissions, including carbon dioxide (CO$_2$), carbon monoxide (CO), and methane (CH$_4$). Utilizing a
federated learning approach, the
robot collaborates with other systems across various NPPs to improve its predictive capabilities without compromising data
privacy. Additionally, the implementation of
Quantum Key Distribution (QKD) ensures secure data transmission, safeguarding sensitive operational information. Our methodology combines systematic
navigation patterns with machine learning
algorithms to facilitate efficient coverage of designated areas, thereby optimizing contamination monitoring processes. Through simulations and real-world experiments, we demonstrate the effectiveness of the Optimus-Q
robot in enhancing operational safety and responsiveness in
nuclear facilities. This research underscores the potential of integrating robotics, machine learning, and
quantum technologies to revolutionize monitoring systems in hazardous environments.
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All authors Sai Puppala, Ismail Hossain, Jahangir Alam, Sajedul Talukder
Robotics
SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models
Senyu Fei, Siyin Wang, Li Ji et al. (+7 more)
Vision-
Language-Action (VLA) models excel in robotic manipulation but are constrained by their heavy reliance on expert demonstrations, leading to demonstration bias and limiting performance. Reinforcement learning (RL) is a vital post-training strategy to overcome these limits, yet current VLA-RL methods, including group-based optimization approaches, are crippled by severe reward sparsity. Relying on binary success indicators wastes valuable information in failed trajectories, resulting in low training efficiency. To solve this, we propose Self-Referential Policy Optimization (SRPO), a novel VLA-RL framework. SRPO eliminates the need for external demonstrations or manual reward engineering by leveraging the model's own successful trajectories, generated within the current training batch, as a self-reference. This allows us to assign a progress-wise reward to failed attempts. A core innovation is the use of latent world representations to measure behavioral progress robustly. Instead of relying on raw pixels or requiring domain-specific fine-tuning, we utilize the compressed, transferable encodings from a world model's latent space. These representations naturally capture progress patterns across environments, enabling accurate, generalized trajectory comparison. Empirical evaluations on the LIBERO
benchmark demonstrate SRPO's efficiency and effectiveness. Starting from a supervised baseline with 48.9% success, SRPO achieves a new state-of-the-art success rate of 99.2% in just 200 RL steps, representing a 103% relative improvement without any extra supervision. Furthermore, SRPO shows substantial robustness, achieving a 167% performance improvement on the LIBERO-Plus
benchmark.
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All authors Senyu Fei, Siyin Wang, Li Ji, Ao Li, Shiduo Zhang, Liming Liu, Jinlong Hou, Jingjing Gong, Xianzhong Zhao, Xipeng Qiu
Robotics
UltraDP: Generalizable Carotid Ultrasound Scanning with Force-Aware Diffusion Policy
Ruoqu Chen, Xiangjie Yan, Kangchen Lv et al. (+3 more)
Ultrasound scanning is a critical imaging technique for real-time, non-invasive diagnostics. However, variations in patient anatomy and complex human-in-the-loop interactions pose significant challenges for autonomous robotic scanning. Existing ultrasound scanning
robots are commonly limited to relatively low generalization and inefficient data utilization. To overcome these limitations, we present UltraDP, a
Diffusion-Policy-based method that receives multi-sensory inputs (ultrasound images, wrist camera images, contact wrench, and probe pose) and generates actions that are fit for multi-modal action distributions in autonomous ultrasound scanning of carotid artery. We propose a specialized guidance module to enable the policy to output actions that center the artery in ultrasound images. To ensure stable contact and safe interaction between the
robot and the human subject, a hybrid force-impedance controller is utilized to drive the
robot to track such trajectories. Also, we have built a large-scale training
dataset for carotid scanning comprising 210 scans with 460k sample pairs from 21 volunteers of both genders. By exploring our guidance module and DP's strong generalization ability, UltraDP achieves a 95% success rate in transverse scanning on previously unseen subjects, demonstrating its effectiveness.
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All authors Ruoqu Chen, Xiangjie Yan, Kangchen Lv, Gao Huang, Zheng Li, Xiang Li
Robotics
NMPC-based Motion Planning with Adaptive Weighting for Dynamic Object Interception
Chen Cai, Saksham Kohli and Steven Liu
Catching fast-moving objects serves as a
benchmark for robotic agility, posing significant coordination challenges for cooperative manipulator systems holding a catcher, particularly due to inherent closed-chain constraints. This paper presents a nonlinear model predictive control (MPC)-based motion planner that bridges high-level interception planning with real-time joint space control, enabling dynamic object interception for systems comprising two cooperating arms. We introduce an Adaptive- Terminal (AT) MPC formulation featuring cost shaping, which contrasts with a simpler Primitive-Terminal (PT) approach relying heavily on terminal penalties for rapid convergence. The proposed AT formulation is shown to effectively mitigate issues related to actuator power limit violations frequently encountered with the PT strategy, yielding trajectories and significantly reduced control effort. Experimental results on a robotic platform with two cooperative arms, demonstrating excellent real time performance, with an average planner cycle computation time of approximately 19 ms-less than half the 40 ms system sampling time. These results indicate that the AT formulation achieves significantly improved motion quality and robustness with minimal computational overhead compared to the PT baseline, making it well-suited for dynamic, cooperative interception tasks.
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Robotics
Decentralized Gaussian Process Classification and an Application in Subsea Robotics
Yifei Gao, Hans J. He, Daniel J. Stilwell et al. (+1 more)
Teams of cooperating autonomous
underwater vehicles (AUVs) rely on acoustic communication for coordination, yet this communication medium is constrained by limited range, multi-path effects, and low bandwidth. One way to address the uncertainty associated with acoustic communication is to learn the communication environment in real-time. We address the challenge of a team of
robots building a map of the probability of communication success from one location to another in real-time. This is a decentralized classification problem – communication events are either successful or unsuccessful – where AUVs share a subset of their communication measurements to build the map. The main contribution of this work is a rigorously derived data sharing policy that selects measurements to be shared among AUVs. We experimentally validate our proposed sharing policy using real acoustic communication data collected from teams of Virginia Tech 690 AUVs, demonstrating its effectiveness in
underwater environments.
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All authors Yifei Gao, Hans J. He, Daniel J. Stilwell, James McMahon
Robotics · Cross-listed from cs.LG
PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles
Yinan Yu and Samuel Scheidegger
Runtime geofencing for ground vehicles is rapidly emerging as a critical technology for enforcing Operational Design Domains (ODDs). However, existing solutions struggle to reconcile high-fidelity learning with the structural requirements of verifiable control. We address this by introducing PCARNN-DCBF, a novel pipeline integrating a Physics-encoded Control-Affine Residual Neural Network with a preview-based Discrete Control Barrier Function. Unlike generic learned models, PCARNN explicitly preserves the control-affine structure of vehicle dynamics, ensuring the linearity required for reliable optimization. This enables the DCBF to enforce polygonal keep-in constraints via a real-time Quadratic Program (QP) that handles high relative degree and mitigates actuator saturation. Experiments in CARLA across electric and combustion platforms demonstrate that this structure-preserving approach significantly outperforms analytical and unstructured neural baselines.
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Robotics
Theoretical Closed-loop Stability Bounds for Dynamical System Coupled with Diffusion Policies
Gabriel Lauzier, Alexandre Girard and François Ferland
Diffusion Policy has shown great performance in robotic manipulation tasks under
stochastic perturbations, due to its ability to model
multimodal action distributions. Nonetheless, its reliance on a computationally expensive reverse-time
diffusion (denoising) process, for action inference, makes it challenging to use for real-time applications where quick decision-making is mandatory. This work studies the possibility of conducting the denoising process only partially before executing an action, allowing the plant to evolve according to its dynamics in parallel to the reverse-time
diffusion dynamics ongoing on the computer. In a classical
diffusion policy setting, the plant dynamics are usually slow and the two dynamical processes are uncoupled. Here, we investigate theoretical bounds on the stability of closed-loop systems using
diffusion policies when the plant dynamics and the denoising dynamics are coupled. The contribution of this work gives a framework for faster imitation learning and a metric that yields if a controller will be stable based on the variance of the demonstrations.
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Robotics
Discovering Optimal Natural Gaits of Dissipative Systems via Virtual Energy Injection
Korbinian Griesbauer, Davide Calzolari, Maximilian Raff et al. (+2 more)
Legged
robots offer several advantages when navigating unstructured environments, but they often fall short of the efficiency achieved by wheeled
robots. One promising strategy to improve their energy economy is to leverage their natural (unactuated) dynamics using elastic elements. This work explores that concept by designing energy-optimal control inputs through a unified, multi-stage framework. It starts with a novel energy injection technique to identify passive motion patterns by harnessing the system's natural dynamics. This enables the discovery of passive solutions even in systems with energy dissipation caused by factors such as friction or plastic collisions. Building on these passive solutions, we then employ a continuation approach to derive energy-optimal control inputs for the fully actuated, dissipative robotic system. The method is tested on simulated models to demonstrate its applicability in both single- and multi-legged robotic systems. This analysis provides valuable insights into the design and operation of elastic legged
robots, offering pathways to improve their efficiency and adaptability by exploiting the natural system dynamics.
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All authors Korbinian Griesbauer, Davide Calzolari, Maximilian Raff, C. David Remy, Alin Albu-Schäffer
Robotics
RRT*former: Environment-Aware Sampling-Based Motion Planning using Transformer
Mingyang Feng, Shaoyuan Li and Xiang Yin
We investigate the sampling-based optimal path planning problem for robotics in complex and dynamic environments. Most existing sampling-based
algorithms neglect environmental information or the information from previous samples. Yet, these pieces of information are highly informative, as leveraging them can provide better heuristics when sampling the next state. In this paper, we propose a novel sampling-based planning
algorithm, called RRT*former, which integrates the standard RRT*
algorithm with a
Transformer network in a novel way. Specifically, the
Transformer is used to extract features from the environment and leverage information from previous samples to better guide the sampling process. Our extensive experiments demonstrate that, compared to existing sampling-based approaches such as RRT*, Neural RRT*, and their variants, our
algorithm achieves considerable improvements in both the optimality of the path and sampling efficiency. The code for our implementation is available on https://github.com/fengmingyang666/RRTformer.
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Robotics
Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention
Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis et al. (+1 more)
Autonomous exploration of obstacle-rich spaces requires strategies that ensure efficiency while guaranteeing safety against collisions with obstacles. This paper investigates a novel platform-agnostic reinforcement learning framework that integrates a graph neural network-based policy for next-waypoint selection, with a safety filter ensuring safe mobility. Specifically, the neural network is trained using reinforcement learning through the Proximal Policy Optimization (PPO)
algorithm to maximize exploration efficiency while minimizing safety filter interventions. Henceforth, when the policy proposes an infeasible action, the safety filter overrides it with the closest feasible alternative, ensuring consistent system behavior. In addition, this paper introduces a reward function shaped by a potential field that accounts for both the
agent's proximity to unexplored regions and the expected information gain from reaching them. The proposed framework combines the adaptability of reinforcement learning-based exploration policies with the reliability provided by explicit safety mechanisms. This feature plays a key role in enabling the deployment of learning-based policies on robotic platforms operating in real-world environments. Extensive evaluations in both simulations and experiments performed in a lab environment demonstrate that the approach achieves efficient and safe exploration in cluttered spaces.
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All authors Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos
Robotics · Cross-listed from cs.CV
Three-Way Open-Set Detection for Robust Autonomous Navigation
Spyridon Loukovitis, Vasileios Karampinis and Athanasios Voulodimos
Autonomous
navigation in complex scenes requires reliable perception across scenarios that the model did not encounter during its training. Along its route, an autonomous framework encounters objects it was trained to recognize, obstacles it has never seen, and background structures that resemble objects. Each of the three must be handled differently. To tackle this, existing open-set and out-of-distribution detectors discard low-confidence detections with an objectness threshold and only then test the rest for novelty. By forcing a single threshold like this they introduce a trade-off where a low threshold adds background clutter to the detected objects, while a high one may discard needed novel objects. We instead formulate open-set detection as a three-way classification of each detection into known object, unknown object, or background, computed post hoc from the outputs of a pretrained detector. We develop methods for domain generalization and for domain adaptation, evaluated across different detector families and
benchmarks up to a combined semantic and
covariate domain shift. To test the framework in a
navigation context, we conduct simulations parameterized by the measured detection performance. The results show that the three-way decision yields safer and more efficient missions than binary alternatives.
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Robotics
C2F-Space: Coarse-to-Fine Space Grounding for Spatial Instructions using Vision-Language Models
Nayoung Oh, Dohyun Kim, Junhyeong Bang et al. (+2 more)
Space grounding refers to localizing a set of spatial references described in natural
language instructions. Traditional methods often fail to account for complex
reasoning – such as distance, geometry, and inter-object relationships – while
vision-
language models (VLMs), despite strong
reasoning abilities, struggle to produce a fine-grained region of outputs. To overcome these limitations, we propose C2F-Space, a novel coarse-to-fine space-grounding framework that (i) estimates an approximated yet spatially consistent region using a VLM, then (ii) refines the region to align with the local environment through superpixelization. For the coarse estimation, we design a grid-based visual-grounding
prompt with a propose-validate strategy, maximizing VLM's spatial understanding and yielding physically and semantically valid canonical region (i.e., ellipses). For the refinement, we locally adapt the region to surrounding environment without over-relaxed to free space. We construct a new space-grounding
benchmark and compare C2F-Space with five state-of-the-art baselines using success rate and intersection-over-union. Our C2F-Space significantly outperforms all baselines. Our ablation study confirms the effectiveness of each module in the two-step process and their synergistic effect of the combined framework. We finally demonstrate the applicability of C2F-Space to simulated robotic pick-and-place tasks.
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All authors Nayoung Oh, Dohyun Kim, Junhyeong Bang, Rohan Paul, Daehyung Park
Robotics
MSA - Technique for Stiffness Modeling of Manipulators with Complex and Hybrid Structures
Alexandr Klimchik, Anatol Pashkevich and Damien Chablat
The paper presents a systematic approach for stiffness modeling of manipulators with complex and hybrid structures using
matrix structural analysis. In contrast to previous results, it is suitable for mixed architectures containing closed-loops, flexible links, rigid connections, passive and elastic joints with external loadings and preloadings. The proposed approach produces the Cartesian stiffness
matrices in a semi-analytical manner. It presents the manipulator stiffness model as a set of conventional equations describing the link elasticities that are supplemented by a set of constraints describing connections between links. Its allows user straightforward aggregation of stiffness model equations avoiding traditional column/row merging procedures in the extended stiffness
matrix. Advantages of this approach are illustrated by stiffness analysis of NaVaRo manipulator.
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Robotics
Optimizing Robot Positioning Against Placement Inaccuracies: A Study on the Fanuc CRX10iA/L
Nicolas Gautier, Yves Guillermit, Mathieu Porez et al. (+2 more)
This study presents a methodology for determining the optimal base placement of a Fanuc CRX10iA/L collaborative
robot for a desired trajectory corresponding to an industrial task. The proposed method uses a particle swarm optimization
algorithm that explores the search space to find positions for performing the trajectory. An $\alpha$-shape
algorithm is then used to draw the borders of the feasibility areas, and the largest circle inscribed is calculated from the Voronoi diagrams. The aim of this approach is to provide a robustness criterion in the context of
robot placement inaccuracies that may be encountered, for example, if the
robot is placed on a mobile base when the system is deployed by an operator. The approach developed uses an inverse kinematics model to evaluate all initial configurations, then moves the
robot end-effector along the reference trajectory using the Jacobian
matrix and assigns a score to the attempt. For the Fanuc CRX10iA/L
robot, there can be up to 16 solutions to the inverse kinematics model. The calculation of these solutions is not trivial and requires a specific study that planning tools such as MoveIt cannot fully take into account. Additionally, the optimization process must consider constraints such as joint limits, singularities, and workspace limitations to ensure feasible and efficient trajectory execution.
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All authors Nicolas Gautier, Yves Guillermit, Mathieu Porez, David Lemoine, Damien Chablat
Robotics
Path Planning through Multi-Agent Reinforcement Learning in Dynamic Environments
Jonas De Maeyer, Hossein Yarahmadi and Moharram Challenger
Path planning in dynamic environments is a fundamental challenge in intelligent transportation and robotics, where obstacles and conditions change over time, introducing uncertainty and requiring continuous adaptation. While existing approaches often assume complete environmental unpredictability or rely on global planners, these assumptions limit scalability and practical deployment in real-world settings. In this paper, we propose a scalable, region-aware reinforcement learning (RL) framework for path planning in dynamic environments. Our method builds on the observation that environmental changes, although dynamic, are often localized within bounded regions. To exploit this, we introduce a hierarchical decomposition of the environment and deploy distributed RL
agents that adapt to changes locally. We further propose a retraining mechanism based on sub-environment success rates to determine when policy updates are necessary. Two training paradigms are explored: single-
agent Q-learning and multi-
agent federated Q-learning, where local Q-tables are aggregated periodically to accelerate the learning process. Unlike prior work, we evaluate our methods in more realistic settings, where multiple simultaneous obstacle changes and increasing difficulty levels are present. Results show that the
federated variants consistently outperform their single-
agent counterparts and closely approach the performance of A* Oracle while maintaining shorter adaptation times and robust scalability. Although initial training remains time-consuming in large environments, our decentralized framework eliminates the need for a global planner and lays the groundwork for future improvements using deep RL and flexible environment decomposition.
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Robotics
Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
Jiashu Yang, Yifan Han, Yucheng Xie et al. (+2 more)
In
embodied AI, visual perception should be active rather than passive: the system must decide where to look and at what scale to sense to acquire maximally informative data under pixel and spatial budget constraints. Existing
vision models coupled with fixed RGB-D cameras fundamentally fail to reconcile wide-area coverage with fine-grained detail acquisition, severely limiting their efficacy in open-world robotic applications. We study the task of
language-guided active visual perception: given a single RGB image and a natural
language instruction, the
agent must output pan, tilt, and zoom adjustments of a real PTZ (pan-tilt-zoom) camera to acquire the most informative view for the specified task. We propose EyeVLA, a unified framework that addresses this task by integrating visual perception,
language understanding, and physical camera control within a single autoregressive
vision-
language-action model. EyeVLA introduces a semantically rich and efficient hierarchical action encoding that compactly tokenizes continuous camera adjustments and embeds them into the VLM vocabulary for joint
multimodal reasoning. Through a data-efficient pipeline comprising pseudo-label generation, iterative IoU-controlled data refinement, and reinforcement learning with Group Relative Policy Optimization (GRPO), we transfer the open-world understanding of a pre-trained VLM to an
embodied active perception policy using only 500 real-world samples. Evaluations on 50 diverse real-world scenes across five independent evaluation runs demonstrate that EyeVLA achieves an average task completion rate of 96%. Our work establishes a new paradigm for instruction-driven active visual information acquisition in
multimodal embodied systems.
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All authors Jiashu Yang, Yifan Han, Yucheng Xie, Ning Guo, Wenzhao Lian
Robotics
Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms
Alexander Boldachev
This paper compares two distinct approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO), implemented through the boldsea framework. BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules. We demonstrate that EO achieves comparable reactivity and modularity to BTs through a fundamentally different architecture: replacing polling-based tick execution with event-driven state propagation. We propose that EO offers an alternative framework, moving from procedural programming to semantic domain modeling, to address the semantic-process gap in traditional robotic control. EO supports runtime model modification, full temporal traceability, and a unified representation of data, logic, and interface - features that are difficult or sometimes impossible to achieve with BTs, although BTs excel in established, predictable scenarios. The comparison is grounded in a practical mobile manipulation task. This comparison highlights the respective operational strengths of each approach in dynamic, evolving robotic systems.
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Robotics
Symmetry-Breaking in Multi-Agent Navigation: Winding Number-Aware MPC with a Learned Topological Strategy
Tomoki Nakao, Kazumi Kasaura and Tadashi Kozuno
In decentralized multi-
agent navigation,
agents that independently compute their controls without communicating goals or intentions can fall into
symmetry-induced deadlocks because each
agent must decide how to pass others. We study this problem under the assumption that each
agent has access to the current observable states of other
agents, including their positions, velocities, and radii, while their goals, intentions, and future trajectories remain unobserved. To address this problem, we propose WNumMPC, a hierarchical
navigation method that quantifies cooperative
symmetry-breaking strategies via a topological invariant, the winding number, and learns such strategies through reinforcement learning. The learning-based Planner outputs continuous-valued signed target winding numbers and dynamic importance weights to prioritize critical interactions in dense crossings. Then, the model-based Controller generates collision-free and efficient motions based on the strategy and weights provided by the Planner. Simulation and real-world
robot experiments indicate that WNumMPC effectively avoids deadlocks and collisions and achieves better performance than the baselines, particularly in dense and
symmetry-prone scenarios. These experiments also suggest that explicitly leveraging winding numbers yields robust sim-to-real transfer with minimal performance degradation. The code for the experiments is available at https://github.com/omron-sinicx/WNumMPC.
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Robotics
Modelling and Model-Checking a ROS2 Multi-Robot System using Timed Rebeca
Hiep Hong Trinh, Marjan Sirjani, Federico Ciccozzi et al. (+2 more)
Model-based development accelerates prototyping, enables earlier experimentation, and ensures rigorous validation of system design intents. In multi-
agent systems with complex asynchronous interactions and concurrency, formal verification, particularly model-checking, offers an automated means of confirming that desired properties hold. Timed Rebeca, an actor-based modelling
language supporting reactive, concurrent, and timed behaviors, together with its model-checking tool, provides a powerful framework for this purpose. By leveraging these capabilities, Timed Rebeca can intuitively capture ROS2 node graphs, recurring physical signals, motion primitives, and other time-convertible behaviors. Nevertheless, modelling and verifying multi-
robot systems entail significant challenges: abstracting intricate information, bridging the gap between discrete models and continuous system dynamics, and managing large state spaces while preserving fidelity. To address these challenges, we propose discretization strategies tailored to various data types and identify thresholds of abstraction that balance accuracy and tractability. We further introduce optimization techniques to accelerate verification. Our work demonstrates how to systematically design and verify multi-
robot systems through Timed Rebeca, efficiently transform continuous dynamics into discrete models for model-checking, and maintain a practical, bidirectional flow between the abstract model and the ROS2 implementation. The accompanying Rebeca and ROS2 codebases, made openly available, serve as a foundational reference for researchers and developers aiming to model and verify advanced autonomous robotic systems.
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All authors Hiep Hong Trinh, Marjan Sirjani, Federico Ciccozzi, Abu Naser Masud, Mikael Sjödin
Robotics
A Class of Dual-Frame Passively-Tilting Fully-Actuated Hexacopter
Jiajun Liu, Yimin Zhu, Xiaorui Liu et al. (+3 more)
This paper proposed a novel fully-actuated hexacopter. It features a dual-frame passive tilting structure and achieves independent control of translational motion and attitude with minimal actuators. Compared to previous fully-actuated
UAVs, it liminates internal force cancellation, resulting in higher flight efficiency and endurance under equivalent payload conditions. Based on the dynamic model of fully-actuated hexacopter, a full-actuation controller is designed to achieve efficient and stable control. Finally, simulation is conducted, validating the superior fully-actuated motion capability of fully-actuated hexacopter and the effectiveness of the proposed control strategy.
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All authors Jiajun Liu, Yimin Zhu, Xiaorui Liu, Mingye Cao, Mingchao Li, Lixian Zhang
Robotics
VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation
Tairan He, Zi Wang, Haoru Xue et al. (+11 more)
A key barrier to the real-world deployment of
humanoid robots is the lack of autonomous loco-manipulation skills. We introduce VIRAL, a visual sim-to-real framework that learns
humanoid loco-manipulation entirely in simulation and deploys it zero-shot to real hardware. VIRAL follows a teacher-student design: a privileged RL teacher, operating on full state, learns long-horizon loco-manipulation using a delta action space and reference state initialization. A
vision-based student policy is then distilled from the teacher via large-scale simulation with tiled rendering, trained with a mixture of online DAgger and behavior cloning. We find that compute scale is critical: scaling simulation to tens of GPUs (up to 64) makes both teacher and student training reliable, while low-compute regimes often fail. To bridge the sim-to-real gap, VIRAL combines large-scale visual domain randomization over lighting, materials, camera parameters, image quality, and sensor delays–with real-to-sim
alignment of the dexterous hands and cameras. Deployed on a Unitree G1
humanoid, the resulting RGB-based policy performs continuous loco-manipulation for up to 54 cycles, generalizing to diverse spatial and appearance variations without any real-world fine-tuning, and approaching expert-level teleoperation performance. Extensive ablations dissect the key design choices required to make RGB-based
humanoid loco-manipulation work in practice.
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All authors Tairan He, Zi Wang, Haoru Xue, Qingwei Ben, Zhengyi Luo, Wenli Xiao, Ye Yuan, Xingye Da, Fernando Castañeda, Shankar Sastry, Changliu Liu, Guanya Shi, Linxi Fan, Yuke Zhu
Robotics
Eq.Bot: Enhance Robotic Manipulation Learning via Group Equivariant Canonicalization
Jian Deng, Yuandong Wang, Yangfu Zhu et al. (+3 more)
Robotic manipulation systems are increasingly deployed across diverse domains. Yet existing multi-modal learning frameworks lack inherent guarantees of
geometric consistency, struggling to handle spatial transformations such as rotations and translations. While recent works attempt to introduce equivariance through bespoke architectural modifications, these methods suffer from high implementation complexity, computational cost, and poor portability. Inspired by human cognitive processes in spatial
reasoning, we propose Eq.Bot, a universal canonicalization framework grounded in SE(2) group equivariant theory for robotic manipulation learning. Our framework transforms observations into a canonical space, applies an existing policy, and maps the resulting actions back to the original space. As a model-agnostic solution, Eq.Bot aims to endow models with spatial equivariance without requiring architectural modifications. Extensive experiments demonstrate the superiority of Eq.Bot under both CNN-based (e.g., CLIPort) and
Transformer-based (e.g., OpenVLA-OFT) architectures over existing methods on various robotic manipulation tasks, where the most significant improvement can reach 50.0%.
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All authors Jian Deng, Yuandong Wang, Yangfu Zhu, Tao Feng, Tianyu Wo, Zhenzhou Shao
Robotics · Cross-listed from eess.SY
Nonholonomic Robot Parking by Feedback – Part I: Modular Strict CLF Designs
Velimir Todorovski, Kwang Hak Kim, Alessandro Astolfi et al. (+1 more)
It has been known in the robotics literature since about 1995 that, in polar coordinates, the nonholonomic unicycle is asymptotically stabilizable by smooth feedback, even globally. We introduce a modular design framework that selects the forward velocity to decouple the radial coordinate, allowing the steering subsystem to be stabilized independently. Within this structure, we develop families of feedback laws using passivity, backstepping, and integrator forwarding. Each law is accompanied by a strict control Lyapunov function, including barrier variants that enforce angular constraints. These strict CLFs provide constructive class KL convergence estimates and enable
eigenvalue assignment at the target
equilibrium. The framework generalizes and extends prior modular and nonmodular approaches, while preparing the ground for inverse optimal and adaptive redesigns in the sequel paper.
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All authors Velimir Todorovski, Kwang Hak Kim, Alessandro Astolfi, Miroslav Krstic