Peer Revenue Returns subject to peer review

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Computer Science Robotics

cs.RO Watch Robotics

Monday 29 December

Robotics · Cross-listed from cs.MA

Assessing behaviour coverage in a multi-agent system simulation for autonomous vehicle testing

Manuel Franco-Vivo

As autonomous vehicle technology advances, ensuring the safety and reliability of these systems becomes paramount. Consequently, comprehensive testing methodologies are essential to evaluate the performance of autonomous vehicles in diverse and complex real-world scenarios. This study focuses on the behaviour coverage analysis of a multi-agent system simulation designed for autonomous vehicle testing, and provides a systematic approach to measure and assess behaviour coverage within the simulation environment. By defining a set of driving scenarios, and agent interactions, we evaluate the extent to which the simulation encompasses a broad range of behaviours relevant to autonomous driving. Our findings highlight the importance of behaviour coverage in validating the effectiveness and robustness of autonomous vehicle systems. Through the analysis of behaviour coverage metrics and coverage-based testing, we identify key areas for improvement and optimization in the simulation framework. Thus, a Model Predictive Control (MPC) pedestrian agent is proposed, where its objective function is formulated to encourage interesting tests while promoting a more realistic behaviour than other previously studied pedestrian agents. This research contributes to advancing the field of autonomous vehicle testing by providing insights into the comprehensive evaluation of system behaviour in simulated environments. The results offer valuable implications for enhancing the safety, reliability, and performance of autonomous vehicles through rigorous testing methodologies.
Read the full abstract

Robotics

Optimal Scalability-Aware Allocation of Swarm Robots: From Linear to Retrograde Performance via Marginal Gains

Simay Atasoy Bingöl, Tobias Töpfer, Sven Kosub et al. (+2 more)

In collective systems, the available agents are a limited resource that must be allocated among tasks to maximize collective performance. Computing the optimal allocation of several agents to numerous tasks through a brute-force approach can be infeasible, especially when each task's performance scales differently with the increase of agents. For example, difficult tasks may require more agents to achieve similar performances compared to simpler tasks, but performance may saturate nonlinearly as the number of allocated agents increases. We propose a computationally efficient algorithm, based on marginal performance gains, for optimally allocating agents to tasks with concave scalability functions, including linear, saturating, and retrograde scaling, to achieve maximum collective performance. We test the algorithm by allocating a simulated robot swarm among collective decision-making tasks, where embodied agents sample their environment and exchange information to reach a consensus on spatially distributed environmental features. We vary task difficulties by different geometrical arrangements of environmental features in space (patchiness). In this scenario, decision performance in each task scales either as a saturating curve (following the Condorcet's Jury Theorem in an interference-free setup) or as a retrograde curve (when physical interference among robots restricts their movement). Using simple robot simulations, we show that our algorithm can be useful in allocating robots among tasks. Our approach aims to advance the deployment of future real-world multi-robot systems.
Read the full abstract

All authors Simay Atasoy Bingöl, Tobias Töpfer, Sven Kosub, Heiko Hamann, Andreagiovanni Reina

Robotics

PCR-ORB: Enhanced ORB-SLAM3 with Point Cloud Refinement Using Deep Learning-Based Dynamic Object Filtering

Sheng-Kai Chen, Jie-Yu Chao, Jr-Yu Chang et al. (+2 more)

Visual Simultaneous Localization and Mapping (vSLAM) systems encounter substantial challenges in dynamic environments where moving objects compromise tracking accuracy and map consistency. This paper introduces PCR-ORB (Point Cloud Refinement ORB), an enhanced ORB-SLAM3 framework that integrates deep learning-based point cloud refinement to mitigate dynamic object interference. Our approach employs YOLOv8 for semantic segmentation combined with CUDA-accelerated processing to achieve real-time performance. The system implements a multi-stage filtering strategy encompassing ground plane estimation, sky region removal, edge filtering, and temporal consistency validation. Comprehensive evaluation on the KITTI dataset (sequences 00-09) demonstrates performance characteristics across different environmental conditions and scene types. Notable improvements are observed in specific sequences, with sequence 04 achieving 25.9% improvement in ATE RMSE and 30.4% improvement in ATE median. However, results show mixed performance across sequences, indicating scenario-dependent effectiveness. The implementation provides insights into dynamic object filtering challenges and opportunities for robust navigation in complex environments.
Read the full abstract

All authors Sheng-Kai Chen, Jie-Yu Chao, Jr-Yu Chang, Po-Lien Wu, Po-Chiang Lin

Robotics

Explainable Neural Inverse Kinematics for Obstacle-Aware Robotic Manipulation: A Comparative Analysis of IKNet Variants

Sheng-Kai Chen, Yi-Ling Tsai, Chun-Chih Chang et al. (+2 more)

Deep neural networks have accelerated inverse-kinematics (IK) inference to the point where low cost manipulators can execute complex trajectories in real time, yet the opaque nature of these models contradicts the transparency and safety requirements emerging in responsible AI regulation. This study proposes an explainability centered workflow that integrates Shapley-value attribution with physics-based obstacle avoidance evaluation for the ROBOTIS OpenManipulator-X. Building upon the original IKNet, two lightweight variants-Improved IKNet with residual connections and Focused IKNet with position-orientation decoupling are trained on a large, synthetically generated pose-joint dataset. SHAP is employed to derive both global and local importance rankings, while the InterpretML toolkit visualizes partial-dependence patterns that expose non-linear couplings between Cartesian poses and joint angles. To bridge algorithmic insight and robotic safety, each network is embedded in a simulator that subjects the arm to randomized single and multi-obstacle scenes; forward kinematics, capsule-based collision checks, and trajectory metrics quantify the relationship between attribution balance and physical clearance. Qualitative heat maps reveal that architectures distributing importance more evenly across pose dimensions tend to maintain wider safety margins without compromising positional accuracy. The combined analysis demonstrates that explainable AI(XAI) techniques can illuminate hidden failure modes, guide architectural refinements, and inform obstacle aware deployment strategies for learning based IK. The proposed methodology thus contributes a concrete path toward trustworthy, data-driven manipulation that aligns with emerging responsible-AI standards.
Read the full abstract

All authors Sheng-Kai Chen, Yi-Ling Tsai, Chun-Chih Chang, Yan-Chen Chen, Po-Chiang Lin

Robotics

Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?

Socratis Gkelios, Savvas D. Apostolidis, Pavlos Ch. Kapoutsis et al. (+2 more)

Unmanned Aerial Vehicles (UAVs) have revolutionized inspection tasks by offering a safer, more efficient, and flexible alternative to traditional methods. However, battery limitations often constrain their effectiveness, necessitating the development of optimized flight paths and data collection techniques. While existing approaches like coverage path planning (CPP) ensure comprehensive data collection, they can be inefficient, especially when inspecting multiple non connected Regions of Interest (ROIs). This paper introduces the Fast Inspection of Scattered Regions (FISR) problem and proposes a novel solution, the multi UAV Disjoint Areas Inspection (mUDAI) method. The introduced approach implements a two fold optimization procedure, for calculating the best image capturing positions and the most efficient UAV trajectories, balancing data resolution and operational time, minimizing redundant data collection and resource consumption. The mUDAI method is designed to enable rapid, efficient inspections of scattered ROIs, making it ideal for applications such as security infrastructure assessments, agricultural inspections, and emergency site evaluations. A combination of simulated evaluations and real world deployments is used to validate and quantify the method's ability to improve operational efficiency while preserving high quality data capture, demonstrating its effectiveness in real world operations. An open source Python implementation of the mUDAI method can be found on GitHub (https://github.com/soc12/mUDAI) and the collected and processed data from the real world experiments are all hosted on Zenodo (https://zenodo.org/records/13866483). Finally, this online platform (https://sites.google.com/view/mudai-platform/) allows interested readers to interact with the mUDAI method and generate their own multi UAV FISR missions.
Read the full abstract

All authors Socratis Gkelios, Savvas D. Apostolidis, Pavlos Ch. Kapoutsis, Elias B. Kosmatopoulos, Athanasios Ch. Kapoutsis

Robotics

A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict

Qin Wang, Shanmin Pang, Jianwu Fang et al. (+4 more)

Human-vehicle cooperative driving serves as a vital bridge to fully autonomous driving by improving driving flexibility and gradually building driver trust and acceptance of autonomous technology. To establish more natural and effective human-vehicle interaction, we propose a Human-Oriented Cooperative Driving (HOCD) approach that primarily minimizes human-machine conflict by prioritizing driver intention and state. In implementation, we take both tactical and operational levels into account to ensure seamless human-vehicle cooperation. At the tactical level, we design an intention-aware trajectory planning method, using intention consistency cost as the core metric to evaluate the trajectory and align it with driver intention. At the operational level, we develop a control authority allocation strategy based on reinforcement learning, optimizing the policy through a designed reward function to achieve consistency between driver state and authority allocation. The results of simulation and human-in-the-loop experiments demonstrate that our proposed approach not only aligns with driver intention in trajectory planning but also ensures a reasonable authority allocation. Compared to other cooperative driving approaches, the proposed HOCD approach significantly enhances driving performance and mitigates human-machine conflict.The code is available at https://github.com/i-Qin/HOCD.
Read the full abstract

All authors Qin Wang, Shanmin Pang, Jianwu Fang, Shengye Dong, Fuhao Liu, Jianru Xue, Chen Lv

Robotics

Cosmos-H-Surgical: Learning Surgical Robot Policies from Videos via World Modeling

Yufan He, Pengfei Guo, Mengya Xu et al. (+11 more)

Data scarcity remains a fundamental barrier to achieving fully autonomous surgical robots. While large scale vision language action (VLA) models have shown impressive generalization in household and industrial manipulation by leveraging paired video action data from diverse domains, surgical robotics suffers from the paucity of datasets that include both visual observations and accurate robot kinematics. In contrast, vast corpora of surgical videos exist, but they lack corresponding action labels, preventing direct application of imitation learning or VLA training. In this work, we aim to alleviate this problem by learning policy models from Cosmos-H-Surgical, a world model designed for surgical physical AI. We curated the Surgical Action Text Alignment (SATA) dataset with detailed action description specifically for surgical robots. Then we built Cosmos-H-Surgical based on the most advanced physical AI world model and SATA. It's able to generate diverse, generalizable and realistic surgery videos. We are also the first to use an inverse dynamics model to infer pseudokinematics from synthetic surgical videos, producing synthetic paired video action data. We demonstrate that a surgical VLA policy trained with these augmented data significantly outperforms models trained only on real demonstrations on a real surgical robot platform. Our approach offers a scalable path toward autonomous surgical skill acquisition by leveraging the abundance of unlabeled surgical video and generative world modeling, thus opening the door to generalizable and data efficient surgical robot policies.
Read the full abstract

All authors Yufan He, Pengfei Guo, Mengya Xu, Zhaoshuo Li, Andriy Myronenko, Dillan Imans, Bingjie Liu, Dongren Yang, Mingxue Gu, Yongnan Ji, Yueming Jin, Ren Zhao, Baiyong Shen, Daguang Xu

Robotics · Cross-listed from eess.SY

Breaking Symmetry-Induced Degeneracy in Multi-Agent Ergodic Coverage via Stochastic Spectral Control

Kooktae Lee and Julian Martinez

Multi-agent ergodic coverage via Spectral Multiscale Coverage (SMC) provides a principled framework for driving a team of agents so that their collective time-averaged trajectories match a prescribed spatial distribution. While classical SMC has demonstrated empirical success, it can suffer from gradient cancellation, particularly when agents are initialized near symmetry points of the target distribution, leading to undesirable behaviors such as stalling or motion constrained along symmetry axes. In this work, we rigorously characterize the initial conditions and symmetry-induced invariant manifolds that give rise to such directional degeneracy in first-order agent dynamics. To address this, we introduce a stochastic perturbation combined with a contraction term and prove that the resulting dynamics ensure almost-sure escape from zero-gradient manifolds while maintaining mean-square boundedness of agent trajectories. Simulations on symmetric multi-modal reference distributions demonstrate that the proposed stochastic SMC effectively mitigates transient stalling and axis-constrained motion, while ensuring that all agent trajectories remain bounded within the domain.
Read the full abstract

Robotics

A Sequential Hermaphrodite Coupling Mechanism for Lattice-based Modular Robots

Keigo Torii, Kentaro Uno, Shreya Santra et al. (+1 more)

Lattice-based modular robot systems are envisioned for large-scale construction in extreme environments, such as space. Coupling mechanisms for heterogeneous structural modules should meet all of the following requirements: single-sided coupling and decoupling, flat surfaces when uncoupled, and coupling to passive coupling interfaces as well as coupling behavior between coupling mechanisms. The design requirements for such a coupling mechanism are complex. We propose a novel shape-matching mechanical coupling mechanism that satisfies these design requirements. This mechanism enables controlled, sequential transitions between male and female states. When uncoupled, all mechanisms are in the female state. To enable single-sided coupling, one side of the mechanisms switches to the male state during the coupling process. Single-sided decoupling is possible not only from the male side but also from the female side by forcibly switching the opposite mechanism's male state to the female state. This coupling mechanism can be applied to various modular robot systems and robot arm tool changers.
Read the full abstract

All authors Keigo Torii, Kentaro Uno, Shreya Santra, Kazuya Yoshida

Robotics

Towards the Automation in the Space Station: Feasibility Study and Ground Tests of a Multi-Limbed Intra-Vehicular Robot

Seiko Piotr Yamaguchi, Kentaro Uno, Yasumaru Fujii et al. (+4 more)

This paper presents a feasibility study, including simulations and prototype tests, on the autonomous operation of a multi-limbed intra-vehicular robot (mobile manipulator), shortly MLIVR, designed to assist astronauts with logistical tasks on the International Space Station (ISS). Astronauts spend significant time on tasks such as preparation, close-out, and the collection and transportation of goods, reducing the time available for critical mission activities. Our study explores the potential for a mobile manipulator to support these operations, emphasizing the need for autonomous functionality to minimize crew and ground operator effort while enabling real-time task execution. We focused on the robot's transportation capabilities, simulating its motion planning in 3D space. The actual motion execution was tested with a prototype on a 2D table to mimic a microgravity environment. The results demonstrate the feasibility of performing these tasks with minimal human intervention, offering a promising solution to enhance operational efficiency on the ISS.
Read the full abstract

All authors Seiko Piotr Yamaguchi, Kentaro Uno, Yasumaru Fujii, Masazumi Imai, Kazuki Takada, Taku Okawara, Kazuya Yoshida

Robotics

Pole-centric Descriptors for Robust Robot Localization: Evaluation under Pole-at-Distance (PaD) Observations using the Small Pole Landmark (SPL) Dataset

Wuhao Xie and Kanji Tanaka

While pole-like structures are widely recognized as stable geometric anchors for long-term robot localization, their identification reliability degrades significantly under Pole-at-Distance (Pad) observations typical of large-scale urban environments. This paper shifts the focus from descriptor design to a systematic investigation of descriptor robustness. Our primary contribution is the establishment of a specialized evaluation framework centered on the Small Pole Landmark (SPL) dataset. This dataset is constructed via an automated tracking-based association pipeline that captures multi-view, multi-distance observations of the same physical landmarks without manual annotation. Using this framework, we present a comparative analysis of Contrastive Learning (CL) and Supervised Learning (SL) paradigms. Our findings reveal that CL induces a more robust feature space for sparse geometry, achieving superior retrieval performance particularly in the 5–10m range. This work provides an empirical foundation and a scalable methodology for evaluating landmark distinctiveness in challenging real-world scenarios.
Read the full abstract

Robotics

A New Software Tool for Generating and Visualizing Robot Self-Collision Matrices

Roshan Klein-Seetharama and Daniel Rakita

In robotics, it is common to check whether a given robot state results in self-intersection (i.e., a self-collision query) or to assess its distance from such an intersection (i.e., a self-proximity query). These checks are typically performed between pairs of shapes attached to different robot links. However, many of these shape pairs can be excluded in advance, as their configurations are known to always or never result in contact. This information is typically encoded in a self-collision matrix, where each entry (i, j) indicates whether a check should be performed between shape i and shape j. While the MoveIt Setup Assistant is widely used to generate such matrices, current tools are limited by static visualization, lack of proximity support, rigid single-geometry assumptions, and tedious refinement workflows, hindering flexibility and reuse in downstream robotics applications. In this work, we introduce an interactive tool that overcomes these limitations by generating and visualizing self-collision matrices across multiple shape representations, enabling dynamic inspection, filtering, and refinement of shape pairs. Outputs are provided in both JSON and YAML for easy integration. The system is implemented in Rust and uses the Bevy game engine to deliver high-quality visualizations. We demonstrate its effectiveness on multiple robot platforms, showing that matrices generated using diverse shape types yield faster and more accurate self-collision and self-proximity queries.
Read the full abstract

Robotics

Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models

Roshan Klein-Seetharaman and Daniel Rakita

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work.
Read the full abstract

Sunday 28 December

Robotics · Cross-listed from cs.LG

Value-guided action planning with JEPA world models

Matthieu Destrade, Oumayma Bounou, Quentin Le Lidec et al. (+2 more)

Building deep learning models that can reason about their environment requires capturing its underlying dynamics. Joint-Embedded Predictive Architectures (JEPA) provide a promising framework to model such dynamics by learning representations and predictors through a self-supervised prediction objective. However, their ability to support effective action planning remains limited. We propose an approach to enhance planning with JEPA world models by shaping their representation space so that the negative goal-conditioned value function for a reaching cost in a given environment is approximated by a distance (or quasi-distance) between state embeddings. We introduce a practical method to enforce this constraint during training and show that it leads to significantly improved planning performance compared to standard JEPA models on simple control tasks.
Read the full abstract

All authors Matthieu Destrade, Oumayma Bounou, Quentin Le Lidec, Jean Ponce, Yann LeCun

Robotics · Cross-listed from cs.CV

Rethinking Densification in Gaussian SLAM: A Stationary Sampling Perspective

Wei-Tse Cheng, Yen-Jen Chiou and Yuan-Fu Yang

Gaussian Splatting enables high-fidelity differentiable SLAM by representing scenes as anisotropic Gaussian primitives. Most systems rely on residual-driven densification, which couples refinement with representation growth: accuracy saturates at a moderate primitive count, yet memory grows throughout the trajectory, degrading throughput. We introduce TopoGS-SLAM, a stationary-sampling formulation that decouples refinement from capacity through coverage-triggered Gaussian initialization and bounded-topology optimization, turning mapping into a bounded-dimensional problem with stable scaling. On standard indoor benchmarks, TopoGS-SLAM matches or surpasses densification-based pipelines in localization and reconstruction accuracy while substantially improving memory scalability and throughput, showing that iterative topology mutation is not required for accurate Gaussian SLAM in bounded-area static indoor scenes with sufficient multi-view coverage. Additional details and resources are available at this URL: https://breeze1124.github.io/topogs-slam-project-page/
Read the full abstract

Robotics

APOLLO Blender: A Robotics Library for Visualization and Animation in Blender

Peter Messina and Daniel Rakita

High-quality visualizations are an essential part of robotics research, enabling clear communication of results through figures, animations, and demonstration videos. While Blender is a powerful and freely available 3D graphics platform, its steep learning curve and lack of robotics-focused integrations make it difficult and time-consuming for researchers to use effectively. In this work, we introduce a lightweight software library that bridges this gap by providing simple scripting interfaces for common robotics visualization tasks. The library offers three primary capabilities: (1) importing robots and environments directly from standardized descriptions such as URDF; (2) Python-based scripting tools for keyframing robot states and visual attributes; and (3) convenient generation of primitive 3D shapes for schematic figures and animations. Together, these features allow robotics researchers to rapidly create publication-ready images, animations, and explanatory schematics without needing extensive Blender expertise. We demonstrate the library through a series of proof-of-concept examples and conclude with a discussion of current limitations and opportunities for future extensions.
Read the full abstract

Robotics

Embodied Learning of Reward for Musculoskeletal Control with Vision Language Models

Saraswati Soedarmadji, Yunyue Wei, Chen Zhang et al. (+2 more)

Discovering effective reward functions remains a fundamental challenge in motor control of high-dimensional musculoskeletal systems. While humans can describe movement goals explicitly such as "walking forward with an upright posture," the underlying control strategies that realize these goals are largely implicit, making it difficult to directly design rewards from high-level goals and natural language descriptions. We introduce Motion from Vision-Language Representation (MoVLR), a framework that leverages vision-language models (VLMs) to bridge the gap between goal specification and movement control. Rather than relying on handcrafted rewards, MoVLR iteratively explores the reward space through iterative interaction between control optimization and VLM feedback, aligning control policies with physically coordinated behaviors. Our approach transforms language and vision-based assessments into structured guidance for embodied learning, enabling the discovery and refinement of reward functions for high-dimensional musculoskeletal locomotion and manipulation. These results suggest that VLMs can effectively ground abstract motion descriptions in the implicit principles governing physiological motor control.
Read the full abstract

All authors Saraswati Soedarmadji, Yunyue Wei, Chen Zhang, Yisong Yue, Yanan Sui

Robotics

Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives

Shuanghao Bai, Wenxuan Song, Jiayi Chen et al. (+14 more)

Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic manipulation remaining one of the most challenging embodied tasks. Its difficulty lies in integrating perception, semantic understanding, task reasoning, physically grounded action generation, and reliable execution. This survey examines robotic manipulation from an algorithmic perspective and organizes recent learning-based approaches through a unified abstraction of high-level planning and low-level action modeling. At the high level, we extend the classical notion of task planning to include reasoning over language, code, affordances, geometric constraints, and 3D representations. At the low level, we present a learning-paradigm-oriented taxonomy of learning-based action models, covering input modeling, latent learning, and policy learning. Within this abstraction, foundation models contribute either by generating structured planning artifacts that are instantiated as constraints or latent inputs for downstream action generation, or by directly modeling executable actions and trajectories. Finally, we summarize open challenges and future directions related to scalability, generalization, data efficiency, multimodal physical interaction, and safety. Together, this survey provides a structured view of the design space and emerging trends in foundation models for robotic manipulation.
Read the full abstract

All authors Shuanghao Bai, Wenxuan Song, Jiayi Chen, Yuheng Ji, Zhide Zhong, Jin Yang, Han Zhao, Wanqi Zhou, Zhe Li, Pengxiang Ding, Cheng Chi, Chang Xu, Xiaolong Zheng, Donglin Wang, Haoang Li, Shanghang Zhang, Badong Chen

Robotics

PreGME: Prescribed Performance Control of Aerial Manipulators based on Variable-Gain ESO

Mengyu Ji, Shiliang Guo, Zhengzhen Li et al. (+3 more)

An aerial manipulator, comprising a multirotor base and a robotic arm, is subject to significant dynamic coupling between these two components. Therefore, achieving precise and robust motion control is a challenging yet important objective. Here, we propose a novel prescribed performance motion control framework based on variable-gain extended state observers (ESOs), referred to as PreGME. The method includes variable-gain ESOs for real-time estimation of dynamic coupling and a prescribed performance flight control that incorporates error trajectory constraints. Compared with existing methods, the proposed approach exhibits the following two characteristics. First, the adopted variable-gain ESOs can accurately estimate rapidly varying dynamic coupling. This enables the proposed method to handle manipulation tasks that require aggressive motion of the robotic arm. Second, by prescribing the performance, a preset error trajectory is generated to guide the system evolution along this trajectory. This strategy allows the proposed method to ensure the tracking error remains within the prescribed performance envelope, thereby achieving high-precision control. Experiments on a real platform, including aerial staff twirling, aerial mixology, and aerial cart-pulling experiments, are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that even under the dynamic coupling caused by rapid robotic arm motion (end-effector velocity: 1.02 m/s, acceleration: 5.10 m/s$^2$), the proposed method achieves high tracking performance.
Read the full abstract

All authors Mengyu Ji, Shiliang Guo, Zhengzhen Li, Jiahao Shen, Huazi Cao, Shiyu Zhao

Robotics

P-FABRIK: A General Intuitive and Robust Inverse Kinematics Method for Parallel Mechanisms Using FABRIK Approach

Daqian Cao, Quan Yuan and Weibang Bai

Traditional geometric inverse kinematics methods for parallel mechanisms rely on specific spatial geometry constraints. However, their application to redundant parallel mechanisms is challenged due to the increased constraint complexity. Moreover, it will output no solutions and cause unpredictable control problems when the target pose lies outside its workspace. To tackle these challenging issues, this work proposes P-FABRIK, a general, intuitive, and robust inverse kinematics method to find one feasible solution for diverse parallel mechanisms based on the FABRIK algorithm. By decomposing the general parallel mechanism into multiple serial sub-chains using a new topological decomposition strategy, the end targets of each sub-chain can be subsequently revised to calculate the inverse kinematics solutions iteratively. Multiple case studies involving planar, standard, and redundant parallel mechanisms demonstrated the proposed method's generality across diverse parallel mechanisms. Furthermore, numerical simulation studies verified its efficacy and computational efficiency, as well as its robustness ability to handle out-of-workspace targets.
Read the full abstract

Robotics

The body is not there to compute: Comment on "Informational embodiment: Computational role of information structure in codes and robots" by Pitti et al

Matej Hoffmann

Applying the lens of computation and information has been instrumental in driving the technological progress of our civilization as well as in empowering our understanding of the world around us. The digital computer was and for many still is the leading metaphor for how our mind operates. Information theory (IT) has also been important in our understanding of how nervous systems encode and process information. The target article deploys information and computation to bodies: to understand why they have evolved in particular ways (animal bodies) and to design optimal bodies (robots). In this commentary, I argue that the main role of bodies is not to compute.
Read the full abstract

2512.22868 · 28 Dec · Comment on Pitti, A., Austin, M., Nakajima, K., & Kuniyoshi, Y. (2025). Informational Embodiment: Computational role of information structure in codes and robots. Physics of Life Reviews 53, 262-276. https://doi.org/10.1016/j.plrev.2025.03.018. Also available as arXiv:2408.12950

Abstract PDF $BOT ×2

Robotics · Cross-listed from cs.CV

MUSON: A Reasoning-oriented Multimodal Dataset for Socially Compliant Navigation in Urban Environments

Zhuonan Liu, Xinyu Zhang, Zishuo Wang et al. (+8 more)

Socially compliant navigation requires structured reasoning about dynamic pedestrians and physical constraints to ensure safe and interpretable decisions. Vision-language models (VLMs) provide a promising foundation for this task because they can integrate visual observations with language-based social knowledge. However, existing untuned VLMs still struggle to reliably understand fine-grained social norms, making task-specific fine-tuning essential. At the same time, no large-scale egocentric dataset is available this task. To address these challenges, we introduce MUSON, a multimodal dataset for short-horizon social navigation containing 10,110 egocentric samples collected across diverse indoor and outdoor social scenes. MUSON adopts a structured five-step chain-of-thought annotation framework comprising perception, prediction, reasoning, action, and explanation. It explicitly models static physical constraints and employs a standardized six-action decision space. Compared with existing social-navigation datasets, MUSON provides consistent annotations for reasoning, actions, and explanations. We evaluate ten representative small-to-medium VLMs on MUSON. Qwen3-VL-8B achieves the strongest decision-level performance, attaining the highest action accuracy of 0.7765 and Macro-F1 score of 0.7490, as well as the lowest collision rate of 0.0609. These results demonstrate that MUSON is an effective and reusable benchmark for advancing socially compliant navigation. The dataset is publicly available at https://github.com/MUSON-dataset/MUSON/releases/tag/v1.0.
Read the full abstract

All authors Zhuonan Liu, Xinyu Zhang, Zishuo Wang, Runji Cai, Tomohito Kawabata, Qianyi Li, Xuance Peng, Tianze Yu, Zhen Xiong, Xuesu Xiao, Ling Xiao

Robotics

Two-Robot Computational Landscape: A Complete Characterization of Model Power in Minimal Mobile Robot Systems

Naoki Kitamura, Yuichi Sudo and Koichi Wada

The computational power of autonomous mobile robots under the Look-Compute-Move (LCM) model has been widely studied through an extensive hierarchy of robot models defined by the presence of memory, communication, and synchrony assumptions. While the general n-robot landscape has been largely established, the exact structure for two robots has remained unresolved. This paper presents the first complete characterization of the computational power of two autonomous robots across all major models, namely OBLOT, FSTA, FCOM, and LUMI, under the full spectrum of schedulers (FSYNCH, SSYNCH, ASYNCH, and their atomic variants). Our results reveal a landscape that fundamentally differs from the general case. Most notably, we prove that FSTA^F and LUMI^F coincide under full synchrony, a surprising collapse indicating that perfect synchrony can substitute both memory and communication when only two robots exist. We also show that FSTA and FCOM are orthogonal: there exists a problem solvable in the weakest communication model but impossible even in the strongest finite-state model, completing the bidirectional incomparability. All equivalence and separation results are derived through a novel simulation-free method, providing a unified and constructive view of the two-robot hierarchy. This yields the first complete and exact computational landscape for two robots, highlighting the intrinsic challenges of coordination at the minimal scale.
Read the full abstract

Robotics

Active Constraint Learning in High Dimensions from Demonstrations

Zheng Qiu, Chih-Yuan Chiu and Glen Chou

We present an iterative active constraint learning (ACL) algorithm, within the learning from demonstrations (LfD) paradigm, which intelligently solicits informative demonstration trajectories for inferring an unknown constraint in the demonstrator's environment. Our approach iteratively trains a Gaussian process (GP) on the available demonstration dataset to represent the unknown constraints, uses the resulting GP posterior to query start/goal states, and generates informative demonstrations which are added to the dataset. Across simulation and hardware experiments using high-dimensional nonlinear dynamics and unknown nonlinear constraints, our method outperforms a baseline, random-sampling based method at accurately performing constraint inference from an iteratively generated set of sparse but informative demonstrations.
Read the full abstract

Robotics

Sistema de navegación de cobertura para vehículos no holonómicos en ambientes de exterior

Michelle Valenzuela, Francisco Leiva and Javier Ruiz-del-Solar

In mobile robotics, coverage navigation refers to the deliberate movement of a robot with the purpose of covering a certain area or volume. Performing this task properly is fundamental for the execution of several activities, for instance, cleaning a facility with a robotic vacuum cleaner. In the mining industry, it is required to perform coverage in several unit processes related with material movement using industrial machinery, for example, in cleaning tasks, in dumps, and in the construction of tailings dam walls. The automation of these processes is fundamental to enhance the security associated with their execution. In this work, a coverage navigation system for a non-holonomic robot is presented. This work is intended to be a proof of concept for the potential automation of various unit processes that require coverage navigation like the ones mentioned before. The developed system includes the calculation of routes that allow a mobile platform to cover a specific area, and incorporates recovery behaviors in case that an unforeseen event occurs, such as the arising of dynamic or previously unmapped obstacles in the terrain to be covered, e.g., other machines or pedestrians passing through the area, being able to perform evasive maneuvers and post-recovery to ensure a complete coverage of the terrain. The system was tested in different simulated and real outdoor environments, obtaining results near 90% of coverage in the majority of experiments. The next step of development is to scale up the utilized robot to a mining machine/vehicle whose operation will be validated in a real environment. The result of one of the tests performed in the real world can be seen in the video available in https://youtu.be/gK7_3bK1P5g.
Read the full abstract

Saturday 27 December

Robotics

Low-Latency Quasi-Static Modeling of UAV Tether Aerodynamics

Max Beffert and Andreas Zell

One of the main limitations of multirotor UAVs is their short flight time due to battery constraints. A practical solution for continuous operation is to power the drone from the ground via a tether. While this approach has been demonstrated for stationary systems, scenarios with a fast-moving base vehicle or strong wind conditions require modeling the tether forces, including aerodynamic effects. In this work, we propose two complementary approaches for low-latency quasi-static tether modeling with aerodynamics. The first is an analytical method based on catenary theory with a uniform drag assumption, achieving very fast solve times below 1 ms. The second is a numerical method that discretizes the tether into segments and lumped masses, solving the equilibrium equations using CasADi and IPOPT. By leveraging initialization strategies, such as warm starting and analytical initialization, low-latency performance was achieved with a solve time of 5 ms, while allowing for flexible force formulations. Both approaches were validated in real-world tests using a load cell to measure the tether force. The results show that the analytical method provides sufficient accuracy for most tethered UAV applications with minimal computational cost, while the numerical method offers higher flexibility and physical accuracy when required. These approaches form a lightweight and extensible framework for low-latency tether simulation, applicable to both offline optimization and online tasks such as simulation, control, and trajectory planning.
Read the full abstract

Robotics

ParaMaP: Parallel Mapping and Collision-free Motion Planning for Reactive Robot Manipulation

Xuewei Zhang, Bailing Tian, Kai Zheng et al. (+3 more)

Real-time and collision-free motion planning remains challenging for robotic manipulation in unknown environments due to continuous perception updates and the need for frequent online replanning. To address these challenges, we propose a parallel mapping and motion planning framework that tightly integrates Euclidean Distance Transform (EDT)-based environment representation with a sampling-based model predictive control (SMPC) planner. On the mapping side, a dense distance-field-based representation is constructed using a GPU-based EDT and augmented with a robot-masked update mechanism to prevent false self-collision detections during online perception. On the planning side, motion generation is formulated as a stochastic optimization problem with a unified objective function and efficiently solved by evaluating large batches of candidate rollouts in parallel within a SMPC framework, in which a geometrically consistent pose tracking metric defined on SE(3) is incorporated to ensure fast and accurate convergence to the target pose. The entire mapping and planning pipeline is implemented on the GPU to support high-frequency replanning. The effectiveness of the proposed framework is validated through extensive simulations and real-world experiments on a 7-DoF robotic manipulator. More details are available at: https://zxw610.github.io/ParaMaP.
Read the full abstract

All authors Xuewei Zhang, Bailing Tian, Kai Zheng, Yulin Hui, Junjie Lu, Zhiyu Li

Robotics

VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models

Borong Zhang, Jiahao Li, Jiachen Shen et al. (+8 more)

While Vision-Language-Action models (VLAs) are rapidly advancing toward generalist robot policies, quantitatively characterizing their capability boundaries and failure modes remains challenging. To address this, we introduce VLA-Arena, a comprehensive benchmark. It features a novel structured task design framework to quantify difficulty across three orthogonal axes: (1) Task Structure, (2) Language Command, and (3) Visual Observation. This allows us to systematically design tasks with fine-grained difficulty levels, enabling a precise measurement of model capability frontiers. For task structure, VLA-Arena comprises 11 task suites organized into four dimensions: Safety, Distractor, Extrapolation, and Long Horizon, totaling 170 tasks. Each suite spans three difficulty levels (L0-L2), with fine-tuning restricted to L0 to rigorously assess generalization. Orthogonal to this, language (W0-W4) and visual (V0-V4) perturbations can be applied to any task as diagnostic probes to distinguish robust grounding from superficial pattern matching. Our extensive evaluation of state-of-the-art VLAs reveals critical limitations: memorization over generalization, superficial visual perception, and a neglect of safety constraints. Additionally, model rank reversals across L0-L2 validate that each level provides non-redundant insights. To foster research addressing these model limitations and ensure reproducibility, we provide the complete VLA-Arena framework, including an end-to-end toolchain from task definition to automated evaluation and the VLA-Arena-S/M/L datasets for fine-tuning. Our benchmark, datasets, models, and leaderboard are publicly available at https://vla-arena.github.io.
Read the full abstract

All authors Borong Zhang, Jiahao Li, Jiachen Shen, Yuhao Zhang, Yishuai Cai, Lu Liu, Hailu Ji, Yuanpei Chen, Juntao Dai, Jiaming Ji, Yaodong Yang

Robotics

Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding

Khoa Vo, Taisei Hanyu, Yuki Ikebe et al. (+8 more)

Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-Language Models (VLMs) for action prediction. Yet most VLAs entangle perception and control in a monolithic pipeline optimized purely for action, which can erode language-conditioned grounding. In our real-world tabletop tests, policies over-grasp when the target is absent, are distracted by clutter, and overfit to background appearance. To address these issues, we propose OBEYED-VLA (OBject-centric and gEometrY groundED VLA), a framework that explicitly disentangles perceptual grounding from action reasoning. Instead of operating directly on raw RGB, OBEYED-VLA augments VLAs with a perception module that grounds multi-view inputs into task-conditioned, object-centric, and geometry-aware observations. This module includes a VLM-based object-centric grounding stage that selects task-relevant object regions across camera views, along with a complementary geometric grounding stage that emphasizes the 3D structure of these objects over their appearance. The resulting grounded views are then fed to a pretrained VLA policy, which we fine-tune exclusively on single-object demonstrations collected without environmental clutter or non-target objects. On a real-world UR10e tabletop setup, OBEYED-VLA substantially improves robustness over strong VLA baselines across four challenging regimes and multiple difficulty levels: distractor objects, absent-target rejection, background appearance changes, and cluttered manipulation of unseen objects. Ablation studies confirm that both semantic grounding and geometry-aware grounding are critical to these gains. Overall, the results indicate that making perception an explicit, object-centric component is an effective way to strengthen and generalize VLA-based robotic manipulation.
Read the full abstract

All authors Khoa Vo, Taisei Hanyu, Yuki Ikebe, Trong Thang Pham, Nhat Chung, Minh Nhat Vu, Duy Nguyen Ho Minh, Anh Nguyen, Anthony Gunderman, Chase Rainwater, Ngan Le

Robotics

Topology-Preserving Scalar Field Optimization for Boundary-Conforming Spiral Toolpaths on Multiply Connected Freeform Surfaces

Shen Changqing, Xu Bingzhou, Qi Bosong et al. (+3 more)

Multiply connected freeform surface features are widely encountered in industrial components, where toolpath generation often suffers from discontinuities, sharp turns, non-uniform scallop heights, and incomplete boundary coverage. This paper proposes a scalar-field variational optimization method for milling that produces continuous, boundary-conforming, and non-self-intersecting toolpaths with smoother transitions, more uniform spacing, and reduced redundant path length. A feasible singularity-free initial scalar field with boundary-conforming iso-level sets is first constructed via conformal slit mapping. The optimization is then reformulated as a topology-preserving mesh deformation process governed by boundary-synchronous updates, whereby the continuity, boundary-conformity, and non-self-intersection requirements of the toolpath are converted into mesh-shape constraints maintained throughout the iterative optimization. As a result, the proposed method achieves globally optimized path spacing and improved scallop-height uniformity while preserving trajectory smoothness. Milling experiments show that, compared with a state-of-the-art conformal slit mapping-based method, the proposed approach improves machining efficiency by 14.24%, enhances scallop-height uniformity by 5.70%, and reduces milling impact-induced vibrations by over 10%. The proposed strategy provides an effective solution for high-performance machining of complex multiply connected freeform components.
Read the full abstract

All authors Shen Changqing, Xu Bingzhou, Qi Bosong, Zhang Xiaojian, Yan Sijie, Ding Han