Robotics
Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences
Xuying Huang, Sicong Pan, Delphine Reinhardt et al. (+1 more)
Visual
navigation is a fundamental capability of mobile service
robots, yet the onboard cameras required for such
navigation can capture
privacy-sensitive information and raise user
privacy concerns. Existing approaches to
privacy-preserving
navigation-oriented visual perception have largely been driven by technical considerations, with limited grounding in user
privacy preferences. In this work, we propose a user-centered approach to designing
privacy-preserving visual perception for
robot navigation. To investigate how user
privacy preferences can inform such design, we conducted two user studies. The results show that users prefer
privacy-preserving visual abstractions and capture-time low-resolution preservation mechanisms: their preferred RGB resolution depends both on the desired
privacy level and
robot proximity during
navigation. Based on these findings, we further derive a user-configurable distance-to-resolution
privacy policy for
privacy-preserving
robot visual
navigation.
Read the full abstract ▾
All authors Xuying Huang, Sicong Pan, Delphine Reinhardt, Maren Bennewitz
Robotics
Occlusion Handling by Pushing for Enhanced Fruit Detection
Ege Gursoy, Dana Kulić and Andrea Cherubini
In agricultural robotics, effective observation and localization of fruits present challenges due to occlusions caused by other parts of the tree, such as branches and leaves. These occlusions can result in false fruit localization or impede the
robot from picking the fruit. The objective of this work is to push away branches that block the fruit's view to increase their visibility. Our setup consists of an RGB-D camera and a
robot arm. First, we detect the occluded fruit in the RGB image and estimate its occluded part via a deep learning generative model in the depth space. The direction to push to clear the occlusions is determined using classic image processing techniques. We then introduce a 3D extension of the 2D Hough transform to detect straight line segments in the point cloud. This extension helps detect tree branches and identify the one mainly responsible for the occlusion. Finally, we clear the occlusion by pushing the branch with the
robot arm. Our method uses a combination of deep learning for fruit appearance estimation, classic image processing for push direction determination, and 3D Hough transform for branch detection. We validate our perception methods through real data under different lighting conditions and various types of fruits (i.e. apple, lemon, orange), achieving improved visibility and successful occlusion clearance. We demonstrate the practical application of our approach through a real
robot branch pushing demonstration.
Read the full abstract ▾
Robotics · Cross-listed from cs.CV
Action Images: End-to-End Policy Learning via Multiview Video Generation
Haoyu Zhen, Zixian Gao, Qiao Sun et al. (+7 more)
World action models (WAMs) have emerged as a promising direction for
robot policy learning, as they can leverage powerful
video backbones to model the future states. However, existing approaches often rely on separate action modules, or use action representations that are not pixel-grounded, making it difficult to fully exploit the pretrained knowledge of
video models and limiting transfer across viewpoints and environments. In this work, we present Action Images, a unified world action model that formulates policy learning as multiview
video generation. Instead of encoding control as low-dimensional
tokens, we translate 7-DoF
robot actions into interpretable action images: multi-view action
videos that are grounded in 2D pixels and explicitly track
robot-arm motion. This pixel-grounded action representation allows the
video backbone itself to act as a zero-shot policy, without a separate policy head or action module. Beyond control, the same unified model supports
video-action joint generation, action-conditioned
video generation, and action labeling under a shared representation. On RLBench and real-world evaluations, our model achieves the strongest zero-shot success rates and improves
video-action joint generation quality over prior
video-space world models, suggesting that interpretable action images are a promising route to policy learning.
Read the full abstract ▾
All authors Haoyu Zhen, Zixian Gao, Qiao Sun, Yilin Zhao, Yuncong Yang, Yilun Du, Pengsheng Guo, Tsun-Hsuan Wang, Yi-Ling Qiao, Chuang Gan
Robotics
Delta6: A Low-Cost, 6-DOF Force-Sensing Flexible End-Effector
Yue Feng, Weicheng Huang, Chen Qiu et al. (+2 more)
This paper presents Delta6, a low-cost, six-degree-of-freedom (6-DOF) force/torque end-effector that combines antagonistic springs with
magnetic encoders to deliver accurate wrench sensing while remaining as simple to assemble as flat-pack furniture. A fully 3D-printed prototype, assembled entirely from off-the-shelf parts, withstands peak forces above +/-14.4 N and torques of +/-0.33 N.m per axis; these limits can be further extended by leveraging the proposed parametric analytical model. Without calibration, Delta6 attains a 99th-percentile error of 7% full scale (FS). With lightweight sequence models, the error is reduced to 3.8% FS by the best-performing network.
Benchmarks on multiple computing platforms confirm that the device's bandwidth is adjustable, enabling balanced trade-offs among update rate, accuracy, and cost, while durability, thermal drift, and zero-calibration tests confirm its robustness. With Delta6 mounted on a
robot arm governed by a force-impedance controller, the system successfully performs two contact-rich tasks: buffing curved surfaces and tight assemblies. Experiments validate the design, showing that Delta6 is a robust, low-cost alternative to existing 6-DOF force sensing solutions. Open-source site: https://wings-robotics.github.io/delta6 .
Read the full abstract ▾
All authors Yue Feng, Weicheng Huang, Chen Qiu, Huixu Dong, I-Ming Chen
Robotics
Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring
Krzysztof Wojciechowski, Ege Gursoy, Arthur Haffemayer et al. (+4 more)
Model Predictive Control (MPC) is widely used for torque-controlled
robots, but classical formulations often neglect real-time force feedback and struggle with contact-rich industrial tasks under collision constraints. Deburring in particular requires precise tool insertion, stable force regulation, and collision-free circular motions in challenging configurations, which exceeds the capability of standard MPC pipelines. We propose a framework that integrates force-feedback MPC with
diffusion-based motion priors to address these challenges. The
diffusion model serves as a
memory of motion strategies, providing robust initialization and adaptation across multiple task instances, while MPC ensures safe execution with explicit force tracking, torque feasibility, and collision avoidance. We validate our approach on a torque-controlled manipulator performing industrial deburring tasks. Experiments demonstrate reliable tool insertion, accurate normal force tracking, and circular deburring motions even in hard-to-reach configurations and under obstacle constraints. To our knowledge, this is the first integration of
diffusion motion priors with force-feedback MPC for collision-aware, contact-rich industrial tasks.
Read the full abstract ▾
All authors Krzysztof Wojciechowski, Ege Gursoy, Arthur Haffemayer, Sebastien Kleff, Vincent Bonnet, Florent Lamiraux, Nicolas Mansard
Robotics · Cross-listed from eess.SY
eVTOL Aircraft Energy Overhead Estimation under Conflict Resolution in High-Density Airspaces
Alex Zongo and Peng Wei
Electric vertical takeoff and landing (eVTOL) aircraft operating in high-density urban airspace must maintain safe separation through tactical conflict resolution, yet the energy cost of such maneuvers has not been systematically quantified. This paper investigates how conflict-resolution maneuvers under the Modified Voltage Potential (MVP)
algorithm affect eVTOL energy consumption. Using a physics-based power model integrated within a traffic simulation, we analyze approximately 71,767 en route sections within a sector, across traffic densities of 10-60 simultaneous aircraft. The main finding is that MVP-based deconfliction is energy-efficient: median energy overhead remains below 1.5% across all density levels, and the majority of en route flights within the sector incur negligible penalty. However, the distribution exhibits pronounced right-skewness, with tail cases reaching 44% overhead at the highest densities due to sustained multi-aircraft conflicts. The 95th percentile ranges from 3.84% to 5.3%, suggesting that a 4-5% reserve margin accommodates the vast majority of tactical deconfliction scenarios. To support operational planning, we develop a machine learning model that estimates energy overhead at mission initiation. Because conflict outcomes depend on future traffic interactions that cannot be known in advance, the model provides both point estimates and uncertainty bounds. These bounds are conservative; actual outcomes fall within the predicted range more often than the stated confidence level, making them suitable for safety-critical reserve planning. Together, these results validate MVP's suitability for energy-constrained eVTOL operations and provide quantitative guidance for reserve energy determination in Advanced Air Mobility.
Read the full abstract ▾
Robotics
Intuitive Human-Robot Interaction: Development and Evaluation of a Gesture-Based User Interface for Object Selection
Bijan Kavousian, Oliver Petrovic and Werner Herfs
Gestures are a natural form of communication between humans and can also be leveraged for human-
robot interaction. This work presents a gesture-based user interface for object selection using pointing and click gestures. An experiment with 20 participants evaluates accuracy and selection time, demonstrating the potential for efficient collaboration.
Read the full abstract ▾
Robotics
HiPolicy: Hierarchical Multi-Frequency Action Chunking for Policy Learning
Jiyao Zhang, Zimu Han, Junhan Wang et al. (+7 more)
Robotic imitation learning faces a fundamental trade-off between modeling long-horizon dependencies and enabling fine-grained closed-loop control. Existing fixed-frequency action chunking approaches struggle to achieve both. Building on this insight, we propose HiPolicy, a hierarchical multi-frequency action chunking framework that jointly predicts action sequences at different frequencies to capture both coarse high-level plans and precise reactive motions. We extract and fuse hierarchical features from history observations aligned to each frequency for multi-frequency chunk generation, and introduce an
entropy-guided execution mechanism that adaptively balances long-horizon planning with fine-grained control based on action uncertainty. Experiments on diverse simulated
benchmarks and real-world manipulation tasks show that HiPolicy can be seamlessly integrated into existing 2D and 3D generative policies, delivering consistent improvements in performance while significantly enhancing execution efficiency.
Read the full abstract ▾
All authors Jiyao Zhang, Zimu Han, Junhan Wang, Xionghao Wu, Shihong Lin, Jinzhou Li, Hongwei Fan, Ruihai Wu, Dongjiang Li, Hao Dong
Robotics · Cross-listed from eess.SY
Staggered Integral Online Conformal Prediction for Safe Dynamics Adaptation with Multi-Step Coverage Guarantees
Daniel M. Cherenson and Dimitra Panagou
Safety-critical control of uncertain, adaptive systems often relies on conservative, worst-case uncertainty bounds that limit closed-loop performance. Online conformal prediction is a powerful data-driven method for quantifying uncertainty when truth values of predicted outputs are revealed online; however, for systems that adapt the dynamics without measurements of the state derivatives, standard online conformal prediction is insufficient to quantify the model uncertainty. We propose Staggered Integral Online Conformal Prediction (SI-OCP), an
algorithm utilizing an integral score function to quantify the lumped effect of disturbance and learning error. This approach provides long-run coverage guarantees, resulting in long-run safety when synthesized with safety-critical controllers, including robust tube model predictive control. Finally, we validate the proposed approach through a numerical simulation of an all-layer deep neural network (DNN) adaptive quadcopter using robust tube (MPC), highlighting the applicability of our method to complex learning parameterizations and control strategies.
Read the full abstract ▾
Robotics
A Co-Design Framework for High-Performance Jumping of a Five-Bar Monoped with Actuator Optimization
Aastha Mishra, Aman Singh and Shishir Kolathaya
The performance of legged
robots depends strongly on both mechanical design and control, motivating co-design approaches that jointly optimize these parameters. However, most existing co-design studies focus on link dimensions and transmission ratios while neglecting detailed actuator design, particularly motor and gearbox parameter optimization, and are largely limited to serial open-chain mechanisms. In this work, we present a co-design framework for a planar closed-chain five-bar monoped that jointly optimizes mechanical design, motor and gearbox parameters, and control parameters for dynamic jumping. The objective is to maximize jump distance while minimizing mechanical energy consumption. The framework employs a two-stage optimization approach, where actuator optimization generates a mapping from gear ratio to actuator mass, efficiency, and peak torque, which is then incorporated into CMA-ES-based co-design optimization of the
robot design and control parameters. Simulation results demonstrate an improvement of approximately 30.4% in jump distance and an 11.5% reduction in mechanical energy consumption compared to a nominal design, highlighting the effectiveness of the proposed framework for high-performance and energy-efficient planar jumping.
Read the full abstract ▾
Robotics
Force Polytope-Based Cant-Angle Selection for Tilting Hexarotor UAVs
Alberto Piccina, Massimiliano Bertoni, Angelo Cenedese et al. (+1 more)
From a maneuverability perspective, the main advantage of tilting multirotor
UAVs lies in the dynamic variability of the feasible executable wrench, which represents a key asset for physical interaction tasks. Accordingly, cant-angle selection should be optimized to ensure high performance while avoiding abrupt variations and preserving real-world feasibility. In this context, this work proposes a lightweight control framework for
star-shaped interdependent cant-tilting hexarotor
UAVs performing interaction tasks. The method uses an offline-computed look-up table of zero-moment force polytopes to identify feasible cant angles for a desired control force and select the optimal one by balancing efficiency and smoothness. The framework is integrated with a
geometric full-pose controller and validated through Monte Carlo simulations in MATLAB/Simulink and compared against a baseline strategy. The results show a significant reduction in computation time, together with improved pose-tracking performance and competitive actuation efficiency. A final physics-based simulation of a complete wall inspection task in Simscape further confirms the feasibility of the proposed strategy in interacting scenarios.
Read the full abstract ▾
All authors Alberto Piccina, Massimiliano Bertoni, Angelo Cenedese, Giulia Michieletto
Robotics
Automating Manual Tasks through Intuitive Robot Programming and Cognitive Robotics
Bijan Kavousian, Petar Tesic, Oliver Petrovic et al. (+1 more)
This paper presents a novel concept for intuitive end-user programming of
robots, inspired by natural interaction between humans. Natural
language and supportive gestures are translated into
robot programs using large
language models (LLMs) and computer
vision (CV). Through equally natural system feedback in the form of clarification questions and visual representations, the generated program can be reviewed and adjusted, thereby ensuring safety, transparency, and user acceptance.
Read the full abstract ▾
All authors Bijan Kavousian, Petar Tesic, Oliver Petrovic, Christian Brecher
Robotics
You're Pushing My Buttons: Instrumented Learning of Gentle Button Presses
Raman Talwar, Remko Proesmans, Thomas Lips et al. (+2 more)
Learning contact-rich manipulation is difficult from cameras and proprioception alone because contact events are only partially observed. We test whether training-time instrumentation, i.e., object sensorisation, can improve policy performance without creating deployment-time dependencies. Specifically, we study button pressing as a testbed and use a microphone fingertip to capture contact-relevant audio. We use an instrumented button-state signal as privileged supervision to fine-tune an audio encoder into a contact event detector. We combine the resulting representation with imitation learning using three strategies, such that the policy only uses
vision and audio during inference. Button press success rates are similar across methods, but instrumentation-guided audio representations consistently reduce contact force. These results support instrumentation as a practical training-time auxiliary objective for learning contact-rich manipulation policies.
Read the full abstract ▾
All authors Raman Talwar, Remko Proesmans, Thomas Lips, Andreas Verleysen, Francis wyffels
Robotics
Dialogue based Interactive Explanations for Safety Decisions in Human Robot Collaboration
Yifan Xu, Xiao Zhan, Akilu Yunusa Kaltungo et al. (+4 more)
As
robots increasingly operate in shared, safety critical environments, acting safely is no longer sufficient
robots must also make their safety decisions intelligible to human collaborators. In human
robot collaboration (HRC), behaviours such as stopping or switching modes are often triggered by internal safety constraints that remain opaque to nearby workers. We present a dialogue based framework for interactive explanation of safety decisions in HRC. The approach tightly couples explanation with constraint based safety evaluation, grounding dialogue in the same state and constraint representations that govern behaviour selection. Explanations are derived directly from the recorded decision trace, enabling users to pose causal ("Why?"), contrastive ("Why not?"), and counterfactual ("What if?") queries about safety interventions. Counterfactual
reasoning is evaluated in a bounded manner under fixed, certified safety parameters, ensuring that interactive exploration does not relax operational guarantees. We instantiate the framework in a construction robotics scenario and provide a structured operational trace illustrating how constraint aware dialogue clarifies safety interventions and supports coordinated task recovery. By treating explanation as an operational interface to safety control, this work advances a design perspective for interactive, safety aware autonomy in HRC.
Read the full abstract ▾
All authors Yifan Xu, Xiao Zhan, Akilu Yunusa Kaltungo, Ming Shan Ng, Tsukasa Ishizawa, Kota Fujimoto, Clara Cheung
Robotics
BiCoord: A Bimanual Manipulation Benchmark towards Long-Horizon Spatial-Temporal Coordination
Xingyu Peng, Chen Gao, Liankai Jin et al. (+2 more)
Bimanual manipulation, i.e., the coordinated use of two robotic arms to complete tasks, is essential for achieving human-level dexterity in robotics. Recent simulation
benchmarks, e.g., RoboTwin and RLBench2, have advanced data-driven learning for bimanual manipulation. However, existing tasks are short-horizon and only loosely coordinated, failing to capture the spatial-temporal coupling inherent in real-world bimanual behaviors. To address this gap, we introduce BiCoord, a
benchmark for long-horizon and tightly coordinated bimanual manipulation. Specifically, BiCoord comprises diverse tasks that require continuous inter-arm dependency and dynamic role exchange across multiple sub-goals. Also, we propose a suite of quantitative metrics that evaluate coordination from temporal, spatial, and spatial-temporal perspectives, enabling systematic measurement of bimanual cooperation. Experimental results show that representative manipulation policies, e.g., DP, RDT, Pi0, and OpenVLA-OFT, struggle with long-duration and highly coupled tasks, revealing fundamental challenges in achieving long-horizon and tight coordination tasks. We hope BiCoord can serve as a foundation for studying long-horizon cooperative manipulation and inspire future research on coordination-aware robotic learning. All
datasets, codes and supplements could be found at https://buaa-colalab.github.io/BiCoord/.
Read the full abstract ▾
All authors Xingyu Peng, Chen Gao, Liankai Jin, Annan Li, Si Liu
Robotics
Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
Tianyue Wu, Guangtong Xu, Zihan Wang et al. (+6 more)
Precise aggressive maneuvers with lightweight onboard sensors remains a key bottleneck in fully exploiting the maneuverability of drones. Such maneuvers are critical for expanding the systems' accessible area by navigating through narrow openings in the environment. Among the most relevant problems, a representative one is aggressive traversal through narrow gaps with quadrotors under SE(3) constraints, which require the quadrotors to leverage a momentary tilted attitude and the asymmetry of the airframe to navigate through gaps. In this paper, we achieve such maneuvers by developing sensorimotor policies directly mapping onboard
vision and proprioception into low-level control commands. The policies are trained using reinforcement learning (RL) with end-to-end policy
distillation in simulation. We mitigate the fundamental hardness of model-free RL's exploration on the restricted solution space with an initialization strategy leveraging trajectories generated by a model-based planner. Careful sim-to-real design allows the policy to control a quadrotor through narrow gaps with low clearances and high repeatability. For instance, the proposed method enables a quadrotor to navigate a rectangular gap at a 5 cm clearance, tilted at up to 90-degree orientation, without knowledge of the gap's position or orientation. Without training on dynamic gaps, the policy can reactively servo the quadrotor to traverse through a moving gap. The proposed method is also validated by training and deploying policies on challenging tracks of narrow gaps placed closely. The flexibility of the policy learning method is demonstrated by developing policies for geometrically diverse gaps, without relying on manually defined traversal poses and visual features.
Read the full abstract ▾
All authors Tianyue Wu, Guangtong Xu, Zihan Wang, Junxiao Lin, Tianyang Chen, Yuze Wu, Zhichao Han, Zhiyang Liu, Fei Gao
Robotics · Cross-listed from eess.SY
Physics-Informed Neural Optimal Control for Precision Immobilization Technique in Emergency Scenarios
Yangye Jiang, Jiachen Wang and Daofei Li
Precision Immobilization Technique (PIT) is a potentially effective intervention maneuver for emergency out-of-control vehicle, but its automation is challenged by highly nonlinear collision dynamics, strict safety constraints, and real-time computation requirements. This work presents a PIT-oriented neural optimal-control framework built around PicoPINN (Planning-Informed Compact Physics-Informed Neural Network), a compact physics-informed surrogate obtained through knowledge
distillation, hierarchical parameter clustering, and relation-
matrix-based parameter reconstruction. A hierarchical neural-OCP (Optimal Control Problem) architecture is then developed, in which an upper virtual decision layer generates PIT decision packages under scenario constraints and a lower coupled-MPC (Model Predictive Control) layer executes interaction-aware control. To evaluate the framework, we construct a PIT Scenario
Dataset and conduct surrogate-model comparison, planning-structure ablation, and multi-fidelity assessment from simulation to scaled by-wire vehicle tests. In simulation, adding the upper planning layer improves PIT success rate from 63.8% to 76.7%, and PicoPINN reduces the original PINN parameter count from 8965 to 812 and achieves the smallest average heading error among the learned surrogates (0.112 rad). Scaled vehicle experiments are further used as evidence of control feasibility, with 3 of 4 low-speed controllable-contact PIT trials achieving successful yaw reversal.
Read the full abstract ▾
Robotics
Hazard Management in Robot-Assisted Mammography Support
Ioannis Stefanakos, Roisin Bradley, Radu Calinescu et al. (+3 more)
Robotic and
embodied-AI systems have the potential to improve accessibility and quality of care in clinical settings, but their deployment in close physical contact with vulnerable patients introduces significant safety risks. This paper presents a hazard management methodology for MammoBot, an assistive robotic system designed to support patients during X-ray mammography. To ensure safety from early development stages, we combine stakeholder-guided process modelling with Software Hazard Analysis and Resolution in Design (SHARD) and System-Theoretic Process Analysis (STPA). The
robot-assisted workflow is defined collaboratively with clinicians, roboticists, and patient representatives to capture key human-
robot interactions. SHARD is applied to identify technical and procedural deviations, while STPA is used to analyse unsafe control actions arising from user interaction. The results show that many hazards arise not from component failures, but from timing mismatches, premature actions, and misinterpretation of system state. These hazards are translated into refined and additional safety requirements that constrain system behaviour and reduce reliance on correct human timing or interpretation alone. The work demonstrates a structured and traceable approach to safety-driven design with potential applicability to assistive robotic systems in clinical environments.
Read the full abstract ▾
All authors Ioannis Stefanakos, Roisin Bradley, Radu Calinescu, Beverley Townsend, Tianyuan Wang, Jihong Zhu
Robotics
GraspSense: Physically Grounded Grasp and Grip Planning for a Dexterous Robotic Hand via Language-Guided Perception and Force Maps
Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina et al. (+3 more)
Dexterous robotic manipulation requires more than geometrically valid grasps: it demands physically grounded contact strategies that account for the spatially non-uniform mechanical properties of the object. However, existing grasp planners typically treat the surface as structurally homogeneous, even though contact in a weak region can damage the object despite a geometrically perfect grasp. We present a pipeline for grasp selection and force regulation in a five-fingered robotic hand, based on a map of locally admissible contact loads. From an operator command, the system identifies the target object, reconstructs its 3D geometry using SAM3D, and imports the model into Isaac Sim. A physics-informed
geometric analysis then computes a force map that encodes the maximum lateral contact force admissible at each surface location without deformation. Grasp candidates are filtered by
geometric validity and task-goal consistency. When multiple candidates are comparable under classical metrics, they are re-ranked using a force-map-aware criterion that favors grasps with contacts in mechanically admissible regions. An impedance controller scales the stiffness of each finger according to the locally admissible force at the contact point, enabling safe and reliable grasp execution. Validation on paper, plastic, and glass cups shows that the proposed approach consistently selects structurally stronger contact regions and keeps grip forces within safe bounds. In this way, the work reframes dexterous manipulation from a purely
geometric problem into a physically grounded joint planning problem of grasp selection and grip execution for future
humanoid systems.
Read the full abstract ▾
All authors Elizaveta Semenyakina, Ivan Snegirev, Mariya Lezina, Miguel Altamirano Cabrera, Safina Gulyamova, Dzmitry Tsetserukou
Robotics
Dynamic Control Allocation for Dual-Tilt UAV Platforms
Marcello Sorge, Federico Ciresola, Giulia Michieletto et al. (+1 more)
This paper focuses on dynamic control allocation for a hexarotor
UAV platform, considering a trajectory tracking task as as case study. It is assumed that the platform is dual-tilting, meaning that it is able to tilt each propeller independently during flight, along two orthogonal axis. We present a hierarchical control structure composed of a high-level controller generating the required wrench for the tracking task, and a control allocation law ensuring that the actuators produce such wrench. The allocator imposes desired first-order dynamics on the actuators set, and exploits system redundancy to optimize the actuators state with respect to a given objective function. Unlike other studies on the subject, we explicitly model actuator saturation and provide theoretical insights on its effect on control performances. We also investigate the role of propeller tilt angles, by imposing asymmetric shapes in the objective function. Numerical simulations are presented to validate the allocation strategy.
Read the full abstract ▾
All authors Marcello Sorge, Federico Ciresola, Giulia Michieletto, Angelo Cenedese
Robotics
Rectified Schrödinger Bridge Matching for Few-Step Visual Navigation
Wuyang Luan, Junhui Li, Weiguang Zhao et al. (+3 more)
Visual
navigation is a core challenge in
Embodied AI, requiring autonomous
agents to translate high-dimensional sensory observations into continuous, long-horizon action trajectories. While generative policies based on
diffusion models and Schrödinger Bridges (SB) effectively capture
multimodal action distributions, they require dozens of integration steps due to high-variance
stochastic transport, posing a critical barrier for real-time robotic control. We propose Rectified Schrödinger Bridge Matching (RSBM), a framework that exploits a shared velocity-field structure between standard Schrödinger Bridges ($\varepsilon=1$, maximum-
entropy transport) and deterministic Optimal Transport ($\varepsilon\to 0$, as in Conditional Flow Matching), controlled by a single entropic regularization parameter $\varepsilon$. We prove two key results: (1) the conditional velocity field's functional form is invariant across the entire $\varepsilon$-spectrum (Velocity Structure Invariance), enabling a single network to serve all regularization strengths; and (2) reducing $\varepsilon$ linearly decreases the conditional velocity variance, enabling more stable coarse-step ODE integration. Anchored to a learned conditional prior that shortens transport distance, RSBM operates at an intermediate $\varepsilon$ that balances
multimodal coverage and path straightness. Empirically, while standard bridges require $\geq 10$ steps to converge, RSBM achieves over 94% cosine similarity and 92% success rate in merely 3 integration steps – without
distillation or multi-stage training – substantially narrowing the gap between high-fidelity generative policies and the low-latency demands of
Embodied AI.
Read the full abstract ▾
All authors Wuyang Luan, Junhui Li, Weiguang Zhao, Wenjian Zhang, Tieru Wu, Rui Ma
Robotics
A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
Kaidong Zhang, Jian Zhang, Rongtao Xu et al. (+20 more)
Vision-
Language-Action (VLA) models have emerged as a powerful paradigm for open-world
robot manipulation, but their practical deployment is often constrained by cost: billion-scale VLM backbones and iterative
diffusion/flow-based action heads incur high latency and compute, making real-time control expensive on commodity hardware. We present A1, a fully open-source and transparent VLA framework designed for low-cost, high-throughput inference without sacrificing manipulation success; Our approach leverages pretrained VLMs that provide implicit affordance priors for action generation. We release the full training stack (training code, data/data-processing pipeline, intermediate checkpoints, and evaluation scripts) to enable end-to-end reproducibility. Beyond optimizing the VLM alone, A1 targets the full inference pipeline by introducing a budget-aware adaptive inference scheme that jointly accelerates the backbone and the action head. Specifically, we monitor action consistency across intermediate VLM layers to trigger early termination, and propose Inter-Layer Truncated Flow Matching that warm-starts denoising across layers, enabling accurate actions with substantially fewer effective denoising iterations. Across simulation
benchmarks (LIBERO, VLABench) and real
robots (Franka, AgiBot), A1 achieves state-of-the-art success rates while significantly reducing inference cost (e.g., up to 72% lower per-episode latency for flow-matching inference and up to 76.6% backbone computation reduction with minor performance degradation). On RoboChallenge, A1 achieves an average success rate of 29.00%, outperforming baselines including pi0(28.33%), X-VLA (21.33%), and RDT-1B (15.00%).
Read the full abstract ▾
All authors Kaidong Zhang, Jian Zhang, Rongtao Xu, Yu Sun, Shuoshuo Xue, Youpeng Wen, Xiaoyu Guo, Minghao Guo, Weijia Liufu, Liu Zihou, Kangyi Ji, Yangsong Zhang, Jiarun Zhu, Jingzhi Liu, Zihang Li, Ruiyi Chen, Meng Cao, Jingming Zhang, Shen Zhao, Xiaojun Chang, Feng Zheng, Ivan Laptev, Xiaodan Liang
Robotics
Leaderless Collective Motion in Affine Formation Control over the Complex Plane
Jesus Bautista, Enric Morella, Lili Wang et al. (+1 more)
We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the
Laplacian matrix used to achieve static formations of
robot swarms. Specifically, the resulting collective motion is characterized as a time-varying affine transformation of a reference configuration, or shape. Unlike the traditional leader-follower strategy, our leaderless scheme allows
agents to maintain distinct and possibly time-varying velocities, enabling a broader range of collective motions, including all the linear combinations of translations, rotations, scaling and shearing of a reference shape. Our analysis provides the analytic solution governing the resulting collective motion, explicitly designing the eigenvectors and
eigenvalues that define this motion as a function of the modified weights in the new
Laplacian matrix. To facilitate a more tractable analysis and design of affine formations in 2D, we propose the use of complex numbers to represent all relevant information. Simulations with up to 20
agents validate the theoretical results.
Read the full abstract ▾
All authors Jesus Bautista, Enric Morella, Lili Wang, Hector Garcia de Marina
Robotics
Grounding Hierarchical Vision-Language-Action Models Through Explicit Language-Action Alignment
Theodor Wulff, Federico Tavella, Rahul Singh Maharjan et al. (+2 more)
Achieving
robot transparency is a critical step toward effective human-
robot collaboration. To be transparent, a
robot's natural
language communication must be consistent with its actions and explicitly grounded in the task and environment. Existing hierarchical
Vision-
Language-Action (VLA) models can generate
language (e.g., through chain-of-thought) and low-level actions. However, current work does not consider explicit
alignment between these modalities during training. To address this crucial gap, we propose a novel training framework that explicitly grounds hierarchical VLA sub-task descriptions with respect to the visual observation and action space. Our framework uses a contrastive model to assess the
alignment between generated
language and corresponding action trajectories. This contrastive model enables direct ranking of different
language-trajectory pairs based on their
alignment, allowing us to refine the grounding of our hierarchical VLA through offline preference learning. We apply our framework to the LanguageTable
dataset, a
benchmark dataset of human
language-annotated trajectories, and provide critical insights into
multimodal grounding representations, all while establishing a strong baseline that achieves performance comparable to fully supervised fine-tuning and minimizing the need for costly data annotations.
Read the full abstract ▾
All authors Theodor Wulff, Federico Tavella, Rahul Singh Maharjan, Manith Adikari, Angelo Cangelosi
Robotics
Control Architecture and experimental validation of a Novel Surgical Robotic Instrument
Doina Pisla, Ionut Zima, Calin Vaida et al. (+7 more)
Minimally invasive surgery (MIS) reduces patient trauma and shortens recovery time; however, conventional laparoscopic instruments remain constrained by limited range of movements. This work presents the control architecture of a 4-DOF flexible laparoscopic instrument integrating distal bending, independent distal head rotation, shaft rotation, and a gripper, while maintaining a 10 mm diameter compatible with standard trocars. The actuation unit and SpaceMouse teleoperation are implemented on Raspberry Pi 5 with Motoron controllers. An analytical scissor-linkage model is derived and parameterized. The predicted jaw opening corresponds to CAD measurements (MAE 0.13{\textdegree}) and OptiTrack motion capture (MAE 1.43{\textdegree}). Integration with the ATHENA parallel
robot is validated through a simulated pancreatic surgery procedure.
Read the full abstract ▾
All authors Doina Pisla, Ionut Zima, Calin Vaida, Andrei Cailean, Marius Miclaus, Adrian Pisla, Andrei Caprariu, Vasile Bulbucan, Bogdan Gherman, Damien Chablat
Robotics
Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
Baoshun Tong, Haoran He, Ling Pan et al. (+2 more)
Vision-
Language-Action (VLA) models have achieved remarkable success in robotic manipulation. However, their robustness to linguistic nuances remains a critical, under-explored safety concern, posing a significant safety risk to real-world deployment. Red teaming, or identifying environmental scenarios that elicit catastrophic behaviors, is an important step in ensuring the safe deployment of
embodied AI
agents. Reinforcement learning (RL) has emerged as a promising approach in automated red teaming that aims to uncover these vulnerabilities. However, standard RL-based adversaries often suffer from severe mode collapse due to their reward-maximizing nature, which tends to converge to a narrow set of trivial or repetitive failure patterns, failing to reveal the comprehensive landscape of meaningful risks. To bridge this gap, we propose a novel Diversity-Aware
Embodied Red Teaming (DAERT) framework, to expose the vulnerabilities of VLAs against linguistic variations. Our design is based on evaluating a uniform policy, which is able to generate a diverse set of challenging instructions while ensuring its attack effectiveness, measured by execution failures in a physical simulator. We conduct extensive experiments across different robotic
benchmarks against two state-of-the-art VLAs, including $\pi_0$ and OpenVLA. Our method consistently discovers a wider range of more effective
adversarial instructions that reduce the average task success rate from 93.33% to 5.85%, demonstrating a scalable approach to stress-testing VLA
agents and exposing critical safety blind spots before real-world deployment.
Read the full abstract ▾
All authors Baoshun Tong, Haoran He, Ling Pan, Yang Liu, Liang Lin
Robotics
Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
Jiahua Ma, Yiran Qin, Xin Wen et al. (+5 more)
This paper addresses a fundamental problem of visuomotor policy learning for robotic manipulation: how to enhance robustness in out-of-distribution execution errors or dynamically re-routing trajectories, where the model relies solely on the original expert demonstrations for training. We introduce the Referring-Aware Visuomotor Policy (ReV), a closed-loop framework that can adapt to unforeseen circumstances by instantly incorporating sparse referring points provided by a human or a high-level
reasoning planner. Specifically, ReV leverages the coupled
diffusion heads to preserve standard task execution patterns while seamlessly integrating sparse referring via a trajectory-steering strategy. Upon receiving a specific referring point, the global
diffusion head firstly generates a sequence of globally consistent yet temporally sparse action anchors, while identifies the precise temporal position for the referring point within this sequence. Subsequently, the local
diffusion head adaptively interpolates adjacent anchors based on the current temporal position for specific tasks. This closed-loop process repeats at every execution step, enabling real-time trajectory replanning in response to dynamic changes in the scene. In practice, rather than relying on elaborate annotations, ReV is trained only by applying targeted perturbations to expert demonstrations. Without any additional data or fine-tuning scheme, ReV achieve higher success rates across challenging simulated and real-world tasks.
Read the full abstract ▾
All authors Jiahua Ma, Yiran Qin, Xin Wen, Yixiong Li, Yuyu Sun, Yulan Guo, Liang Lin, Ruimao Zhang
Robotics
Simulation-Driven Evolutionary Motion Parameterization for Contact-Rich Granular Scooping with a Soft Conical Robotic Hand
Yongliang Wang, Cristian C. Beltran-Hernandez, Tomoya Takahashi et al. (+1 more)
Tool-based scooping is vital in
robot-assisted tasks, enabling interaction with objects of varying sizes, shapes, and material states. Recent studies have shown that flexible, reconfigurable soft robotic end-effectors can adapt their shape to maintain consistent contact with container surfaces during scooping, improving efficiency compared to rigid tools. These soft tools can adjust to varying container sizes and materials without requiring complex sensing or control. However, the inherent compliance and complex deformation behavior of soft robotics introduce significant control complexity that limits practical applications. To address this challenge, this paper presents the development of a physics-based simulation model of a deformable soft conical robotic hand that captures its passive reconfiguration dynamics and enables systematic trajectory optimization for scooping tasks. We propose a novel physics-based simulation approach that accurately models the soft tool's morphing behavior from flat sheets to adaptive conical structures, combined with an evolutionary strategy framework that automatically optimizes scooping trajectories without manual parameter tuning. We validate the optimized trajectories through both simulation and real-
robot experiments. The results demonstrate strong generalization and successfully address a range of challenging tasks previously beyond the reach of existing approaches.
Videos of our experiments are available online: https://sites.google.com/view/scoopsh
Read the full abstract ▾
All authors Yongliang Wang, Cristian C. Beltran-Hernandez, Tomoya Takahashi, Masashi Hamaya
Robotics
MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems
Rui Huang, Zhiqian Cai, Siyu Tang et al. (+6 more)
Modular Aerial
Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations, providing high adaptability to diverse mission scenarios, fault conditions, and payload capacities. However, existing control
algorithms for MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands. This leads to attitude error accumulation as the number of drone units scales, ultimately causing severe oscillations during docking, separation, and waypoint tracking. To address these limitations, we first design a compact mechanical system that enables passive docking, detection-free passive locking, and
magnetic-assisted separation using a single micro servo. Second, we introduce a force-torque-equivalent and polytope-constraint virtual quadrotor that explicitly models feasible wrench sets. Together, these abstractions capture the full MARS dynamics and enable existing quadrotor controllers to be applied across different configurations. We further optimize the yaw angle that maximizes control authority to enhance agility. Third, building on this abstraction, we design a two-stage predictive-allocation pipeline: a constrained predictive tracker computes virtual inputs while respecting force/torque bounds, and a dynamic allocator maps these inputs to individual modules with balanced objectives to produce smooth, trackable motor commands. Simulations across over 10 configurations and real-world experiments demonstrate stable docking, locking, and separation, as well as effective control performance. To our knowledge, this is the first real-world demonstration of MARS achieving agile flight and transport with 40 deg peak pitch while maintaining an average position error of 0.0896 m. The
video is available at: https://youtu.be/yqjccrIpz5o
Read the full abstract ▾
All authors Rui Huang, Zhiqian Cai, Siyu Tang, Pengxuan Wei, Lidong Li, Xin Chen, Wenhan Cao, Zhenyu Zhang, Lin Zhao
Robotics
JailWAM: Jailbreaking World Action Models in Robot Control
Hanqing Liu, Songping Wang, Jiahuan Long et al. (+9 more)
World Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation, enabling physical interaction across diverse tasks and environments. However, their ability to directly follow high-level instructions and execute physical actions also creates potential safety risks, as adversarially designed instructions may induce unsafe
robot behaviors. To systematically assess these risks, we propose JailWAM, the first jailbreak evaluation framework for WAMs. In JailWAM, we integrate three key innovations: Firstly, to address the difficulty of evaluating heterogeneous low-level action outputs, we introduce Visual-Trajectory Mapping, which transforms model-specific actions into unified visual trajectory representations, thereby facilitating consistent risk assessment across WAM architectures. Secondly, to provide efficient and fine-grained assessment of physical risks, we develop a Risk Discriminator supervised by three safety levels ordered according to physical consequence: Safety Compliance, Motion Failure, and Catastrophic Risk. This severity-aware formulation enables the risk discriminator to distinguish different physical outcomes from visual trajectories and support scalable risk screening. Thirdly, to reduce the cost of exhaustively executing
adversarial candidates, we design a Dual-Path Verification Strategy that combines rapid risk screening with closed-loop physical simulation, restricting computationally expensive verification to candidates with potential safety risks. Extensive experiments in the RoboTwin simulation environment show that JailWAM achieves an 84.2% attack success rate on LingBot-VA, which indicates that WAMs may be susceptible to jailbreak attacks that induce unsafe physical behaviors. Our findings may motivate further research on the safety evaluation and
alignment of future
embodied robotic systems.
Read the full abstract ▾
All authors Hanqing Liu, Songping Wang, Jiahuan Long, Jiacheng Hou, Jialiang Sun, Chao Li, Yang Yang, Wei Peng, Xu Liu, Tingsong Jiang, Yao Mu, Wen Yao