Robotics
ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning
Wendi Chen, Han Xue, Yi Wang et al. (+6 more)
Human-level contact-rich manipulation relies on the distinct roles of two key modalities:
vision provides spatially rich but temporally slow global context, while force sensing captures rapid local contact dynamics. Integrating these signals is challenging due to their fundamental frequency and informational disparities. In this work, we propose ImplicitRDP, a unified end-to-end visual-force
diffusion policy that integrates visual planning and reactive force control within a single network. We introduce Structural Slow-Fast Learning, a mechanism utilizing causal
attention to simultaneously process asynchronous visual and force
tokens, allowing the policy to perform rapid force control at the action rate while maintaining the temporal coherence of action chunks. Furthermore, to mitigate modality collapse where end-to-end models fail to adjust the weights across different modalities, we propose Virtual-target-based Representation Regularization. This auxiliary objective maps force feedback into the same space as the action, providing a stronger, physics-grounded learning signal than raw force prediction. Extensive experiments on contact-rich tasks demonstrate that ImplicitRDP significantly outperforms both
vision-only and hierarchical baselines, achieving superior reactivity and success rates with a streamlined training pipeline. Code and
videos are available at https://implicit-rdp.github.io.
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All authors Wendi Chen, Han Xue, Yi Wang, Fangyuan Zhou, Jun Lv, Yang Jin, Shirun Tang, Chuan Wen, Cewu Lu
Robotics · Cross-listed from cs.CV
Any4D: Unified Feed-Forward Metric 4D Reconstruction
Jay Karhade, Nikhil Keetha, Yuchen Zhang et al. (+4 more)
We present Any4D, a scalable multi-view
transformer for metric-scale, dense feed-forward 4D reconstruction. Any4D directly generates per-pixel motion and geometry predictions for N frames, in contrast to prior work that typically focuses on either 2-view dense scene flow or sparse 3D point tracking. Moreover, unlike other recent methods for 4D reconstruction from
monocular RGB
videos, Any4D can process additional modalities and sensors such as RGB-D frames, IMU-based egomotion, and Radar Doppler measurements, when available. One of the key innovations that allows for such a flexible framework is a modular representation of a 4D scene; specifically, per-view 4D predictions are encoded using a variety of
egocentric factors (depthmaps and camera intrinsics) represented in local camera coordinates, and allocentric factors (camera extrinsics and scene flow) represented in global world coordinates. We achieve superior performance across diverse setups - both in terms of accuracy (2-3X lower error) and compute efficiency (15X faster), opening avenues for multiple downstream applications.
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All authors Jay Karhade, Nikhil Keetha, Yuchen Zhang, Tanisha Gupta, Akash Sharma, Sebastian Scherer, Deva Ramanan
Robotics
Curriculum-Based Reinforcement Learning for Autonomous UAV Navigation in Unknown Curved Tubular Conduit
Zamirddine Mari, Jérôme Pasquet and Julien Seinturier
Autonomous drone
navigation in confined tubular environments remains a major challenge due to the constraining geometry of the conduits, the proximity of the walls, and the perceptual limitations inherent to such scenarios. We propose a reinforcement learning approach enabling a drone to navigate unknown three-dimensional tubes without any prior knowledge of their geometry, relying solely on local observations from
LiDAR and a conditional visual detection of the tube center. In contrast, the Pure Pursuit
algorithm, used as a deterministic baseline, benefits from explicit access to the centerline, creating an information asymmetry designed to assess the ability of RL to compensate for the absence of a
geometric model. The
agent is trained through a progressive Curriculum Learning strategy that gradually exposes it to increasingly curved geometries, where the tube center frequently disappears from the visual field. A turning-negotiation mechanism, based on the combination of direct visibility, directional
memory, and
LiDAR symmetry cues, proves essential for ensuring stable
navigation under such partial observability conditions. Experiments show that the PPO policy acquires robust and generalizable behavior, consistently outperforming the deterministic controller despite its limited access to
geometric information. Validation in a high-fidelity 3D environment further confirms the transferability of the learned behavior to a continuous physical dynamics. The proposed approach thus provides a complete framework for autonomous
navigation in unknown tubular environments and opens perspectives for industrial, underground, or medical applications where progressing through narrow and weakly perceptive conduits represents a central challenge.
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Robotics · Cross-listed from cs.LG
Decoupled Q-Chunking
Qiyang Li, Seohong Park and Sergey Levine
Temporal-difference (TD) methods learn state and action values efficiently by bootstrapping from their own future value predictions, but such a self-bootstrapping mechanism is prone to bootstrapping bias, where the errors in the value targets accumulate across steps and result in biased value estimates. Recent work has proposed to use chunked critics, which estimate the value of short action sequences ("chunks") rather than individual actions, speeding up value backup. However, extracting policies from chunked critics is challenging: policies must output the entire action chunk open-loop, which can be sub-optimal for environments that require policy reactivity and also challenging to model especially when the chunk length grows. Our key insight is to decouple the chunk length of the critic from that of the policy, allowing the policy to operate over shorter action chunks. We propose a novel
algorithm that achieves this by optimizing the policy against a distilled critic for partial action chunks, constructed by optimistically backing up from the original chunked critic to approximate the maximum value achievable when a partial action chunk is extended to a complete one. This design retains the benefits of multi-step value propagation while sidestepping both the open-loop sub-optimality and the difficulty of learning action chunking policies for long action chunks. We evaluate our method on challenging, long-horizon offline goal-conditioned tasks and show that it reliably outperforms prior methods. Code: github.com/ColinQiyangLi/dqc.
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Robotics · Cross-listed from cs.LG
Digital Twin Supervised Reinforcement Learning Framework for Autonomous Underwater Navigation
Zamirddine Mari, Mohamad Motasem Nawaf and Pierre Drap
Autonomous
navigation in
underwater environments remains a major challenge due to the absence of GPS, degraded visibility, and the presence of submerged obstacles. This article investigates these issues through the case of the BlueROV2, an open platform widely used for scientific experimentation. We propose a deep reinforcement learning approach based on the Proximal Policy Optimization (PPO)
algorithm, using an observation space that combines target-oriented
navigation information, a virtual occupancy grid, and ray-casting along the boundaries of the operational area. The learned policy is compared against a reference deterministic kinematic planner, the Dynamic Window Approach (DWA), commonly employed as a robust baseline for obstacle avoidance. The evaluation is conducted in a realistic simulation environment and complemented by validation on a physical BlueROV2 supervised by a 3D digital twin of the test site, helping to reduce risks associated with real-world experimentation. The results show that the PPO policy consistently outperforms DWA in highly cluttered environments, notably thanks to better local adaptation and reduced collisions. Finally, the experiments demonstrate the transferability of the learned behavior from simulation to the real world, confirming the relevance of deep RL for autonomous
navigation in
underwater robotics.
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Robotics
Iterative Compositional Data Generation for Robot Control
Anh-Quan Pham, Marcel Hussing, Shubhankar P. Patankar et al. (+3 more)
Collecting robotic manipulation data is expensive, making it impractical to acquire demonstrations for the combinatorially large space of tasks that arise in multi-object, multi-
robot, and multi-environment settings. While recent generative models can synthesize useful data for individual tasks, they do not exploit the compositional structure of robotic domains and struggle to generalize to unseen task combinations. We propose a semantic compositional
diffusion transformer that factorizes transitions into
robot-, object-, obstacle-, and objective-specific components and learns their interactions through
attention. Once trained on a limited subset of tasks, we show that our model can zero-shot generate high-quality transitions from which we can learn control policies for unseen task combinations. Then, we introduce an iterative self-improvement procedure in which synthetic data is validated via offline reinforcement learning and incorporated into subsequent training rounds. Our approach substantially improves zero-shot performance over monolithic and hard-coded compositional baselines, ultimately solving nearly all held-out tasks and demonstrating the emergence of meaningful compositional structure in the learned representations.
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All authors Anh-Quan Pham, Marcel Hussing, Shubhankar P. Patankar, Dani S. Bassett, Jorge Mendez-Mendez, Eric Eaton
Robotics · Cross-listed from cs.AI
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions
Mumuksh Tayal, Manan Tayal, Aditya Singh et al. (+2 more)
Ensuring safety in autonomous systems requires controllers that aim to satisfy state-wise constraints without relying on online interaction.While existing Safe Offline RL methods typically enforce soft expected-cost constraints, they struggle to ensure strict state-wise safety. Conversely, Control Barrier Functions (CBFs) offer a principled mechanism to enforce forward invariance, but often rely on expert-designed barrier functions or knowledge of the system dynamics. We introduce Value-Guided Offline Control Barrier Functions (V-OCBF), a framework that learns a neural CBF entirely from offline demonstrations. Unlike prior approaches, V-OCBF does not assume access to the dynamics model; instead, it derives a recursive finite-difference barrier update, enabling model-free learning of a barrier that propagates safety information over time. Moreover, V-OCBF incorporates an expectile-based objective that avoids querying the barrier on out-of-distribution actions and restricts updates to the
dataset-supported action set. The learned barrier is then used with a Quadratic Program (QP) formulation to synthesize real-time safe control. Across multiple case studies, V-OCBF yields substantially fewer safety violations than baseline methods while maintaining strong task performance, highlighting its scalability for offline synthesis of safety-critical controllers without online interaction or hand-engineered barriers.
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All authors Mumuksh Tayal, Manan Tayal, Aditya Singh, Shishir Kolathaya, Ravi Prakash
Robotics
AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators
Samaksh Ujjawal, Shivansh Pratap Singh, Naveen Sudheer Nair et al. (+3 more)
Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces, making accurate dynamics modeling a core challenge for reliable control. Analytical models lose fidelity under these nonlinear and nonstationary effects, while standard data-driven methods such as deep neural networks and Gaussian processes cannot represent the diverse residual behaviors that arise across different operating conditions. We propose a regime-conditioned
diffusion framework that models the full distribution of residual forces using a conditional
diffusion process and a lightweight temporal encoder. The encoder extracts a compact summary of recent motion and configuration, enabling consistent residual predictions even through abrupt transitions or unseen payloads. When combined with an adaptive controller, the framework enables dynamics uncertainty compensation and yields markedly improved tracking accuracy in real-world tests.
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All authors Samaksh Ujjawal, Shivansh Pratap Singh, Naveen Sudheer Nair, Rishabh Dev Yadav, Wei Pan, Spandan Roy
Robotics · Cross-listed from eess.SY
Conformal Prediction-Based MPC for Stochastic Linear Systems
Lukas Vogel, Andrea Carron, Eleftherios E. Vlahakis et al. (+1 more)
We propose a
stochastic model predictive control (MPC) framework for linear systems subject to joint-in-time chance constraints under unknown disturbance distributions. Unlike existing approaches that rely on parametric or Gaussian assumptions, or require expensive offline computation, the method uses conformal prediction to construct finite-sample confidence regions for the system's error trajectories with minimal computational effort. These probabilistic sets enable relaxation of the joint-in-time chance constraints into a deterministic closed-loop formulation based on indirect feedback, ensuring recursive feasibility and chance constraint satisfaction. Further, we extend to the output feedback setting and establish analogous guarantees from output measurements alone, given access to noise samples. Numerical examples demonstrate the effectiveness and advantages compared to existing approaches.
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All authors Lukas Vogel, Andrea Carron, Eleftherios E. Vlahakis, Dimos V. Dimarogonas
Robotics
On the Stabilization of Rigid Formations on Regular Curves
Mohamed Elobaid, Shinkyu Park and Eric Feron
This work deals with the problem of stabilizing a multi-
agent rigid formation on a general class of planar curves. Namely, we seek to stabilize an equilateral polygonal formation on closed planar differentiable curves after a path sweep. The task of finding an inscribed regular polygon centered at the point of interest is solved via a randomized multi-start Newton-Like
algorithm for which one is able to ascertain the existence of a minimizer. Then we design a continuous feedback law that guarantees convergence to, and sufficient sweeping of the curve, followed by convergence to the desired formation vertices while ensuring inter-
agent avoidance. The proposed approach is validated through numerical simulations for different classes of curves and different rigid formations. Code: https://github.com/mebbaid/paper-elobaid-ifacwc-2026
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Robotics
How to Brake? Ethical Emergency Braking with Deep Reinforcement Learning
Jianbo Wang, Galina Sidorenko and Johan Thunberg
Connected and automated vehicles (CAVs) have the potential to enhance driving safety, for example by enabling safe vehicle following and more efficient traffic scheduling. For such future deployments, safety requirements should be addressed, where the primary such are avoidance of vehicle collisions and substantial mitigating of harm when collisions are unavoidable. However, conservative worst-case-based control strategies come at the price of reduced flexibility and may compromise overall performance. In light of this, we investigate how Deep Reinforcement Learning (DRL) can be leveraged to improve safety in multi-vehicle-following scenarios involving emergency braking. Specifically, we investigate how DRL with vehicle-to-vehicle communication can be used to ethically select an emergency breaking profile in scenarios where overall, or collective, three-vehicle harm reduction or collision avoidance shall be obtained instead of single-vehicle such. As an
algorithm, we provide a hybrid approach that combines DRL with a previously published method based on analytical expressions for selecting optimal constant deceleration. By combining DRL with the previous method, the proposed hybrid approach increases the reliability compared to standalone DRL, while achieving superior performance in terms of overall harm reduction and collision avoidance.
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Robotics
Evaluating Gemini Robotics Policies in a Veo World Simulator
Gemini Robotics Team, Krzysztof Choromanski, Coline Devin et al. (+20 more)
Generative world models hold significant potential for simulating interactions with visuomotor policies in varied environments. Frontier
video models can enable generation of realistic observations and environment interactions in a scalable and general manner. However, the use of
video models in robotics has been limited primarily to in-distribution evaluations, i.e., scenarios that are similar to ones used to train the policy or fine-tune the base
video model. In this report, we demonstrate that
video models can be used for the entire spectrum of policy evaluation use cases in robotics: from assessing nominal performance to out-of-distribution (OOD) generalization, and probing physical and semantic safety. We introduce a generative evaluation system built upon a frontier
video foundation model (Veo). The system is optimized to support
robot action conditioning and multi-view consistency, while integrating generative image-editing and multi-view completion to synthesize realistic variations of real-world scenes along multiple axes of generalization. We demonstrate that the system preserves the base capabilities of the
video model to enable accurate simulation of scenes that have been edited to include novel interaction objects, novel visual backgrounds, and novel distractor objects. This fidelity enables accurately predicting the relative performance of different policies in both nominal and OOD conditions, determining the relative impact of different axes of generalization on policy performance, and performing red teaming of policies to expose behaviors that violate physical or semantic safety constraints. We validate these capabilities through 1600+ real-world evaluations of eight Gemini Robotics policy checkpoints and five tasks for a bimanual manipulator.
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All authors Gemini Robotics Team, Krzysztof Choromanski, Coline Devin, Yilun Du, Debidatta Dwibedi, Ruiqi Gao, Abhishek Jindal, Thomas Kipf, Sean Kirmani, Isabel Leal, Fangchen Liu, Anirudha Majumdar, Andrew Marmon, Carolina Parada, Yulia Rubanova, Dhruv Shah, Vikas Sindhwani, Jie Tan, Fei Xia, Ted Xiao, Sherry Yang, Wenhao Yu, Allan Zhou
Robotics
LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator
Lihuang Chen, Xiangyu Luo and Jun Meng
We propose LEO-RobotAgent, a general-purpose
language-driven intelligent
agent framework for
robots. Under this framework, LLMs can operate different types of
robots to complete unpredictable complex tasks across various scenarios. This framework features strong generalization, robustness, and efficiency. The application-level system built around it can fully enhance bidirectional human-
robot intent understanding and lower the threshold for human-
robot interaction. Regarding
robot task planning, the vast majority of existing studies focus on the application of large models in single-task scenarios and for single
robot types. These
algorithms often have complex structures and lack generalizability. Thus, the proposed LEO-RobotAgent framework is designed with a streamlined structure as much as possible, enabling large models to independently think, plan, and act within this clear framework. We provide a modular and easily registrable toolset, allowing large models to flexibly call various tools to meet different requirements. Meanwhile, the framework incorporates a human-
robot interaction mechanism, enabling the
algorithm to collaborate with humans like a partner. Experiments have verified that this framework can be easily adapted to mainstream
robot platforms including unmanned aerial vehicles (
UAVs), robotic arms, and wheeled
robot, and efficiently execute a variety of carefully designed tasks with different complexity levels. Our code is available at https://github.com/LegendLeoChen/LEO-RobotAgent.
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Robotics
Motion Planning for Safe Landing of a Human-Piloted Parafoil
Maximillian Fainkich, Kiril Solovey and Anna Clarke
Most skydiving accidents occur during the parafoil-piloting and landing stages and result from human lapses in judgment while piloting the parafoil. Training of novice pilots is protracted due to the lack of functional and easily accessible training simulators. Moreover, work on parafoil trajectory planning suitable for aiding human training remains limited. To bridge this gap, we study the problem of computing safe trajectories for human-piloted parafoil flight and examine how such trajectories fare against human-generated solutions. For the algorithmic part, we adapt the sampling-based motion planner Stable Sparse RRT (SST) by Li et al., to cope with the problem constraints while minimizing the bank angle (control effort) as a proxy for safety. We then compare the computer-generated solutions with data from human-generated parafoil flight, where the
algorithm offers a relative cost improvement of 20%-80% over the performance of the human pilot. We observe that human pilots tend to, first, close the horizontal distance to the landing area, and then address the vertical gap by spiraling down to the suitable altitude for starting a landing maneuver. The
algorithm considered here makes smoother and more gradual descents, arriving at the landing area at the precise altitude necessary for the final approach while maintaining safety constraints. Overall, the study demonstrates the potential of computer-generated guidelines, rather than traditional rules of thumb, which can be integrated into future simulators to train pilots for safer and more cost-effective flights.
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Robotics
Mr. Virgil: Learning Multi-robot Visual-range Relative Localization
Si Wang, Zhehan Li, Jiadong Lu et al. (+3 more)
Ultra-wideband (UWB)-
vision fusion localization has achieved extensive applications in the domain of multi-
agent relative localization. The challenging matching problem between
robots and visual detection renders existing methods highly dependent on identity-encoded hardware or delicate tuning
algorithms. Overconfident yet erroneous matches may bring about irreversible damage to the localization system. To address this issue, we introduce Mr. Virgil, an end-to-end learning multi-
robot visual-range relative localization framework, consisting of a graph neural network for data association between UWB rangings and visual detections, and a differentiable pose graph optimization (PGO) back-end. The graph-based front-end supplies robust matching results, accurate initial position predictions, and credible uncertainty estimates, which are subsequently integrated into the PGO back-end to elevate the accuracy of the final pose estimation. Additionally, a decentralized system is implemented for real-world applications. Experiments spanning varying
robot numbers, simulation and real-world, occlusion and non-occlusion conditions showcase the stability and exactitude under various scenes compared to conventional methods. Our code is available at: https://github.com/HiOnes/Mr-Virgil.
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All authors Si Wang, Zhehan Li, Jiadong Lu, Rong Xiong, Yanjun Cao, Yue Wang
Robotics
Neural Ranging Inertial Odometry
Si Wang, Bingqi Shen, Fei Wang et al. (+3 more)
Ultra-wideband (UWB) has shown promising potential in GPS-denied localization thanks to its lightweight and drift-free characteristics, while the accuracy is limited in real scenarios due to its sensitivity to sensor arrangement and non-Gaussian pattern induced by multi-path or multi-signal interference, which commonly occurs in many typical applications like long tunnels. We introduce a novel neural fusion framework for ranging inertial odometry which involves a graph
attention UWB network and a recurrent neural inertial network. Our graph net learns scene-relevant ranging patterns and adapts to any number of anchors or tags, realizing accurate positioning without calibration. Additionally, the integration of least squares and the incorporation of nominal frame enhance overall performance and scalability. The effectiveness and robustness of our methods are validated through extensive experiments on both public and self-collected
datasets, spanning indoor, outdoor, and tunnel environments. The results demonstrate the superiority of our proposed IR-ULSG in handling challenging conditions, including scenarios outside the convex envelope and cases where only a single anchor is available.
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All authors Si Wang, Bingqi Shen, Fei Wang, Yanjun Cao, Rong Xiong, Yue Wang
Robotics
Contact SLAM: An Active Tactile Exploration Policy Based on Physical Reasoning Utilized in Robotic Fine Blind Manipulation Tasks
Gaozhao Wang, Xing Liu, Zhenduo Ye et al. (+2 more)
Contact-rich manipulation is difficult for
robots to execute and requires accurate perception of the environment. In some scenarios,
vision is occluded. The
robot can then no longer obtain real-time scene state information through visual feedback. This is called “blind manipulation". In this manuscript, a novel physically-driven contact cognition method, called “Contact SLAM", is proposed. It estimates the state of the environment and achieves manipulation using only tactile sensing and prior knowledge of the scene. To maximize exploration efficiency, this manuscript also designs an active exploration policy. The policy gradually reduces uncertainties in the manipulation scene. The experimental results demonstrated the effectiveness and accuracy of the proposed method in several contact-rich tasks, including the difficult and delicate socket assembly task and block-pushing task.
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All authors Gaozhao Wang, Xing Liu, Zhenduo Ye, Zhengxiong Liu, Panfeng Huang
Robotics
Seamless Outdoor-Indoor Pedestrian Positioning System with GNSS/UWB/IMU Fusion: A Comparison of EKF, FGO, and PF
Jiaqiang Zhang, Xianjia Yu, Sier Ha et al. (+5 more)
Accurate and continuous pedestrian positioning across outdoor-indoor environments remains challenging because GNSS, UWB, and inertial PDR are complementary yet individually fragile under signal blockage, multipath, and drift. This paper presents a unified GNSS/UWB/IMU fusion framework for seamless pedestrian localization and provides a controlled comparison of three probabilistic back-ends: an error-state extended Kalman filter, sliding-window factor graph optimization, and a particle filter. The system uses chest-mounted IMU-based PDR as the motion backbone and integrates absolute updates from GNSS outdoors and UWB indoors. To enhance transition robustness and mitigate urban GNSS degradation, we introduce a lightweight map-based feasibility constraint derived from OpenStreetMap building footprints, treating most building interiors as non-navigable while allowing motion inside a designated UWB-instrumented building. The framework is implemented in ROS 2 and runs in real time on a wearable platform, with visualization in Foxglove. We evaluate three scenarios: indoor (UWB+PDR), outdoor (GNSS+PDR), and seamless outdoor-indoor (GNSS+UWB+PDR). Results show that the ESKF provides the most consistent overall performance in our implementation.
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All authors Jiaqiang Zhang, Xianjia Yu, Sier Ha, Paola Torrico Moron, Sahar Salimpour, Farhad Kerama, Haizhou Zhang, Tomi Westerlund
Robotics
Symphony: A Heuristic Normalized Calibrated Advantage Actor and Critic Algorithm in application for Humanoid Robots
Timur Ishuov, Michele Folgheraiter, Madi Nurmanov et al. (+3 more)
In our work we implicitly suggest that it is a misconception to think that humans learn fast. The learning process takes time. Babies start learning to move in the restricted fluid environment of the womb. Children are often limited by underdeveloped body. Even adults are not allowed to participate in complex competitions right away. However, with
robots, when learning from scratch, we often don't have the privilege of waiting for tens of millions of steps. "Swaddling" regularization is responsible for restraining an
agent in rapid but unstable development penalizing action strength in a specific way not affecting actions directly. The Symphony, Transitional-policy Deterministic Actor and Critic
algorithm, is a concise combination of different ideas for possibility of training
humanoid robots from scratch with Sample Efficiency, Sample Proximity and Safety of Actions in mind. It is well known that continuous increase in Gaussian noise without appropriate smoothing is harmful for motors and gearboxes. Compared to
Stochastic algorithms, we set limited parametric noise and promote a reduced strength of actions, safely increasing
entropy, since the actions are submerged in weaker noise. When actions require more extreme values, actions rise above the weak noise. Training becomes empirically much safer for both the environment around and the
robot's mechanisms. We use Fading Replay Buffer: using a fixed formula containing the
hyperbolic tangent, we adjust the batch sampling probability: the
memory contains a recent
memory and a long-term
memory trail. Fading Replay Buffer allows us to use Temporal Advantage when we improve the current Critic Network prediction compared to the exponential moving average. Temporal Advantage allows us to update the Actor and Critic in one pass, as well as combine the Actor and Critic in one Object and implement their Losses in one line.
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All authors Timur Ishuov, Michele Folgheraiter, Madi Nurmanov, Goncalo Gordo, Richárd Farkas, József Dombi
Robotics · Cross-listed from cs.ET
Design and Implementation of a High-Precision Wind-Estimation UAV with Onboard Sensors
Haowen Yu, Na Fan, Xing Liu et al. (+1 more)
Accurate real-time wind vector estimation is essential for enhancing the safety,
navigation accuracy, and energy efficiency of unmanned aerial vehicles (
UAVs). Traditional approaches rely on external sensors or simplify vehicle dynamics, which limits their applicability during agile flight or in resource-constrained platforms. This paper proposes a real-time wind estimation method based solely on onboard sensors. The approach first estimates external aerodynamic forces using a disturbance observer (DOB), and then maps these forces to wind vectors using a thin-plate spline (TPS) model. A custom-designed wind barrel mounted on the
UAV enhances aerodynamic sensitivity, further improving estimation accuracy. The system is validated through comprehensive experiments in wind tunnels, indoor and outdoor flights. Experimental results demonstrate that the proposed method achieves consistently high-accuracy wind estimation across controlled and real-world conditions, with speed RMSEs as low as \SI{0.06}{m/s} in wind tunnel tests, \SI{0.22}{m/s} during outdoor hover, and below \SI{0.38}{m/s} in indoor and outdoor dynamic flights, and direction RMSEs under \ang{7.3} across all scenarios, outperforming existing baselines. Moreover, the method provides vertical wind estimates – unavailable in baselines – with RMSEs below \SI{0.17}{m/s} even during fast indoor translations.
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All authors Haowen Yu, Na Fan, Xing Liu, Ximin Lyu
Robotics
RoboNeuron: A Middle-Layer Infrastructure for Agent-Driven Orchestration in Embodied AI
Weifan Guan, Qinghao Hu, Huasen Xi et al. (+3 more)
Vision-
language-action (VLA) models and LLM
agents have advanced rapidly, yet reliable deployment on physical
robots is often hindered by an interface mismatch between
agent tool APIs and
robot middleware. Current implementations typically rely on ad-hoc wrappers that are difficult to reuse, and changes to the VLA backend or serving stack often necessitate extensive re-integration. We introduce RoboNeuron, a middleware layer that connects the Model Context Protocol (MCP) for LLM
agents with
robot middleware such as ROS2. RoboNeuron bridges these ecosystems by deriving
agent-callable tools directly from ROS schemas, providing a unified execution abstraction that supports both direct commands and modular composition, and localizing backend, runtime, and acceleration-preset changes within a stable inference boundary. We evaluate RoboNeuron in simulation and on hardware through multi-platform base control, arm motion, and VLA-based grasping tasks, demonstrating that it enables modular system orchestration under a unified interface while supporting backend transitions without system rewiring. The full code implementation of this work is available at github repo: https://github.com/guanweifan/RoboNeuron
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All authors Weifan Guan, Qinghao Hu, Huasen Xi, Chenxiao Zhang, Aosheng Li, Jian Cheng
Robotics
CLASH: Collaborative Large-Small Hierarchical Framework for Continuous Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Jinlong Li et al. (+6 more)
Vision-and-
Language Navigation (VLN) requires
robots to follow natural
language instructions and navigate complex environments without prior maps. While recent
vision-
language large models demonstrate strong
reasoning abilities, they often underperform task-specific panoramic small models in VLN tasks. To address this, we propose CLASH (Collaborative Large-Small Hierarchy), a VLN-CE framework that integrates a reactive small-model planner (RSMP) with a reflective large-model reasoner (RLMR). RSMP adopts a causal-learning-based dual-branch architecture to enhance generalization, while RLMR leverages panoramic visual prompting with chain-of-thought
reasoning to support interpretable spatial understanding and
navigation. We further introduce an uncertainty-aware collaboration mechanism (UCM) that adaptively fuses decisions from both models. For obstacle avoidance, in simulation, we replace the rule-based controller with a fully learnable point-goal policy, and in real-world deployment, we design a
LiDAR-based clustering module for generating navigable waypoints and pair it with an online SLAM-based local controller. CLASH achieves state-of-the-art (SoTA) results (ranking 1-st) on the VLN-CE leaderboard, significantly improving SR and SPL on the test-unseen set over the previous SoTA methods. Real-world experiments demonstrate CLASH's strong robustness, validating its effectiveness in both simulation and deployment scenarios.
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All authors Liuyi Wang, Zongtao He, Jinlong Li, Ruihao Xia, Mengxian Hu, Chenpeng Yao, Chengju Liu, Yang Tang, Qijun Chen
Robotics
Design and Validation of an Under-actuated Robotic Finger with Synchronous Tendon Routing
Quan Yuan, Zhenting Du, Daqian Cao et al. (+1 more)
Tendon-driven under-actuated robotic fingers provide advantages for dexterous manipulation through reduced actuator requirements and simplified mechanical design. However, achieving both high load capacity and adaptive compliance in a compact form remains challenging. This paper presents an under-actuated tendon-driven robotic finger (UTRF) featuring a synchronous tendon routing that mechanically couples all joints with fixed angular velocity ratios, enabling the entire finger to be actuated by a single actuator. This approach significantly reduces the number of actuators required in multi-finger hands, resulting in a lighter and more compact structure without sacrificing stiffness or compliance. The kinematic and static models of the finger are derived, incorporating tendon elasticity to predict structural stiffness. A single-finger prototype was fabricated and tested under static loading, showing an average deflection prediction error of 1.0 mm (0.322% of total finger length) and a measured stiffness of 1.2x10^3 N/m under a 3 kg tip load. Integration into a five-finger robotic hand (UTRF-RoboHand) demonstrates effective object manipulation across diverse scenarios, confirming that the proposed routing achieves predictable stiffness and reliable grasping performance with a minimal actuator count.
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All authors Quan Yuan, Zhenting Du, Daqian Cao, Weibang Bai
Robotics
Design of a six wheel suspension and a three-axis linear actuation mechanism for a laser weeding robot
Muhammad Usama, Muhammad Ibrahim Khan, Ahmad Hasan et al. (+5 more)
Mobile
robots are increasingly utilized in agriculture to automate labor-intensive tasks such as weeding, sowing, harvesting and soil analysis. Recently, agricultural
robots have been developed to detect and remove weeds using mechanical tools or precise herbicide sprays. Mechanical weeding is inefficient over large fields, and herbicides harm the soil ecosystem.
Laser weeding with mobile
robots has emerged as a sustainable alternative in precision farming. In this paper, we present an autonomous weeding
robot that uses controlled exposure to a low energy
laser beam for weed removal. The proposed
robot is six-wheeled with a novel double four-bar suspension for higher stability. The
laser is guided towards the detected weeds by a three-dimensional linear actuation mechanism. Field tests have demonstrated the
robot's capability to navigate agricultural terrains effectively by overcoming obstacles up to 15 cm in height. At an optimal speed of 42.5 cm/s, the
robot achieves a weed detection rate of 86.2% and operating time of 87 seconds per meter. The
laser actuation mechanism maintains a minimal mean positional error of 1.54 mm, combined with a high hit rate of 97%, ensuring effective and accurate weed removal. This combination of speed, accuracy, and efficiency highlights the
robot's potential for significantly enhancing precision farming practices.
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All authors Muhammad Usama, Muhammad Ibrahim Khan, Ahmad Hasan, Muhammad Shaaf Nadeem, Khawaja Fahad Iqbal, Jawad Aslam, Mian Ashfaq Ali, Asad Nisar Awan
Robotics
Lies We Can Trust: Quantifying Action Uncertainty with Inaccurate Stochastic Dynamics through Conformalized Nonholonomic Lie groups
Luís Marques, Maani Ghaffari and Dmitry Berenson
We propose Conformal Lie-group Action Prediction Sets (CLAPS), a
symmetry-aware conformal prediction-based
algorithm that constructs, for a given action, a set guaranteed to contain the resulting system configuration at a user-defined probability. Our assurance holds under both aleatoric and epistemic uncertainty, non-asymptotically, and does not require strong assumptions about the true system dynamics, the uncertainty sources, or the quality of the approximate dynamics model. Typically, uncertainty quantification is tackled by making strong assumptions about the error distribution or magnitude, or by relying on uncalibrated uncertainty estimates - i.e., with no link to frequentist probabilities - which are insufficient for safe control. Recently, conformal prediction has emerged as a statistical framework capable of providing distribution-free probabilistic guarantees on test-time prediction accuracy. While current conformal methods treat
robot configurations as Euclidean points, many systems have non-Euclidean configurations, e.g., some mobile
robots have SE(2). In this work, we rigorously analyze configuration errors using Lie groups, extending previous Euclidean space theoretical guarantees to SE(2). Our experiments on a simulated JetBot, and on a real MBot, suggest that by considering the configuration space's structure, our
symmetry-informed nonconformity score leads to more volume-efficient prediction regions which represent the underlying uncertainty better than existing approaches.
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Robotics
Task-Oriented Grasping Using Reinforcement Learning with a Contextual Reward Machine
Hui Li, Akhlak Uz Zaman, Fujian Yan et al. (+1 more)
This paper presents a reinforcement learning framework that incorporates a Contextual Reward Machine for task-oriented grasping. The Contextual Reward Machine reduces task complexity by decomposing grasping tasks into manageable sub-tasks. Each sub-task is associated with a stage-specific context, including a reward function, an action space, and a state abstraction function. This contextual information enables efficient intra-stage guidance and improves learning efficiency by reducing the state-action space and guiding exploration within clearly defined boundaries. In addition, transition rewards are introduced to encourage or penalize transitions between stages which guides the model toward desirable stage sequences and further accelerates convergence. When integrated with the Proximal Policy Optimization
algorithm, the proposed method achieved a 95% success rate across 1,000 simulated grasping tasks encompassing diverse objects, affordances, and grasp
topologies. It outperformed the state-of-the-art methods in both learning speed and success rate. The approach was transferred to a real
robot, where it achieved a success rate of 83.3% in 60 grasping tasks over six affordances. These experimental results demonstrate superior accuracy, data efficiency, and learning efficiency. They underscore the model's potential to advance task-oriented grasping in both simulated and real-world settings.
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All authors Hui Li, Akhlak Uz Zaman, Fujian Yan, Hongsheng He
Robotics · Cross-listed from cs.CV
Latent Chain-of-Thought World Modeling for End-to-End Driving
Shuhan Tan, Kashyap Chitta, Yuxiao Chen et al. (+8 more)
Recent
Vision-
Language-Action (VLA) models for autonomous driving explore inference-time
reasoning as a way to improve driving performance and safety in challenging scenarios. Most prior work uses natural
language to express chain-of-thought (CoT)
reasoning before producing driving actions. However, text may not be the most efficient representation for
reasoning. In this work, we present Latent-CoT-Drive (LCDrive): a model that expresses CoT in a latent
language that captures possible outcomes of the driving actions being considered. Our approach unifies CoT
reasoning and decision making by representing both in an action-aligned latent space. Instead of natural
language, the model reasons by interleaving (1) action-proposal
tokens, which use the same vocabulary as the model's output actions; and (2) world model
tokens, which are grounded in a learned latent world model and express future outcomes of these actions. We cold start latent CoT by supervising the model's action proposals and world model
tokens based on ground-truth future rollouts of the scene. We then post-train with closed-loop reinforcement learning to strengthen
reasoning capabilities. On a large-scale end-to-end driving
benchmark, LCDrive achieves faster inference, better trajectory quality, and larger improvements from interactive reinforcement learning compared to both non-
reasoning and text-
reasoning baselines.
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All authors Shuhan Tan, Kashyap Chitta, Yuxiao Chen, Ran Tian, Yurong You, Yan Wang, Wenjie Luo, Yulong Cao, Philipp Krahenbuhl, Marco Pavone, Boris Ivanovic
Robotics · Cross-listed from eess.IV
Active Optics for Hyperspectral Imaging of Reflective Agricultural Leaf Sensors
Dexter Burns and Sanjeev Koppal
Monitoring plant health increasingly relies on leaf-mounted sensors that provide real-time physiological data, yet efficiently locating and sampling these sensors in complex agricultural environments remains a major challenge. We present an integrated, adaptive, and scalable system that autonomously detects and interrogates plant sensors using a coordinated suite of low-cost optical components including a
LiDAR,
liquid lens, monochrome camera, filter wheel, and Fast Steering Mirror (FSM). The system first uses
LiDAR to identify the distinct reflective signatures of sensors within the field, then dynamically redirects the camera s field of view via the FSM to target each sensor for hyperspectral imaging. The
liquid lens continuously adjusts focus to maintain image sharpness across varying depths, enabling precise spectral measurements. We validated the system in controlled indoor experiments, demonstrating accurate detection and tracking of reflective plant sensors and successful acquisition of their spectral data. To our knowledge, no other system currently integrates these sensing and optical modalities for agricultural monitoring. This work establishes a foundation for adaptive, low-cost, and automated plant sensor interrogation, representing a significant step toward scalable, real-time plant health monitoring in precision agriculture.
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