Robotics
TouchDrive: Electronics-Free Tactile Sensing Interface for Assistive Grasping
Jing Xu, Xuezhi Niu, Didem Gurdur Broo et al. (+1 more)
Assistive robotic grasping plays an important role in enabling safe and adaptive manipulation of diverse objects. However, existing systems often rely on electronic sensing and multi-stage processing pipelines, increasing system complexity and reducing accessibility. To address these limitations, we present TouchDrive, a cost-effective, electronics-free tactile sensing interface for assistive grasping. TouchDrive directly converts contact forces into pneumatic feedback through valve-mediated switching, integrating sensing, signal generation, and feedback within a single passive mechanical loop. The system can be employed using a pneumatic normally closed valve, a compressed air tank, sensing element, and haptic feedback actuator without electronics. By delivering tactile cues, TouchDrive empowers users to modulate grasp forces, enabling precise and robust delicate manipulation of compliant and fragile objects. The interface has been validated across diverse robotic platforms, consistently demonstrating reliable performance and practical applicability in assistive grasping tasks, such as handling fruits and everyday items (up to 20 objects).
Read the full abstract ▾
All authors Jing Xu, Xuezhi Niu, Didem Gurdur Broo, Klas Hjort
Robotics · Cross-listed from cs.CV
Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
Nilaksh, Saurav Jha, Artem Zholus et al. (+1 more)
World model-based policy evaluation is a practical proxy for testing real-world
robot control by rolling out candidate actions in action-conditioned
video diffusion models. As these models increasingly adopt latent
diffusion modeling (LDM), choosing the right latent space becomes critical. While the status quo uses autoencoding latent spaces like VAEs that are primarily trained for pixel reconstruction, recent work suggests benefits from pretrained encoders with representation-aligned semantic latent spaces. We systematically evaluate these latent spaces for action-conditioned LDM by comparing six reconstruction and semantic encoders to train world model variants under a fixed protocol on BridgeV2
dataset, and show effective world model training in high-dimensional representation spaces with and without dimension compression. We then propose three axes to assess robotic world model performance: visual fidelity, planning and downstream policy performance, and latent representation quality. Our results show visual fidelity alone is insufficient for world model selection. While reconstruction encoders like VAE and Cosmos achieve strong pixel-level scores, semantic encoders such as V-JEPA 2.1 (strongest overall on policy), Web-DINO, and SigLIP 2 generally excel across the other two axes at all model scales. Our study advocates semantic latent space as stronger foundation for policy-relevant robotics
diffusion world models.
Read the full abstract ▾
All authors Nilaksh, Saurav Jha, Artem Zholus, Sarath Chandar
Robotics
AssistDLO: Assistive Teleoperation for Deformable Linear Object Manipulation
Berk Guler, Simon Manschitz, Kay Pompetzki et al. (+1 more)
Manipulating Deformable Linear Objects (DLOs) is challenging in robotics due to their infinite-dimensional configuration space and complex nonlinear dynamics. In teleoperation, depth uncertainty hinders state perception and reaction. AssistDLO addresses this challenge as an assistive teleoperation framework for DLO manipulation that combines real-time multi-view state estimation, visual assistance (VA), and a geometry-aware shared-autonomy controller based on Control Barrier Functions (SA-CBF). While traditional shared autonomy methods often rely on simple
geometric attractors and may fail to preserve DLO geometry, SA-CBF acts as a geometry-aware funnel, facilitating precise grasping while preserving the operator's high-level authority. The framework is evaluated in a bimanual
knot-untangling user study (N = 22) using ropes with varying length and
rigidity. Results show that the effectiveness of the assistance depends strongly on operator expertise and DLO properties. SA-CBF provides the strongest gains for naive users, acting as a skill equalizer that increases task success from 71% to 88%, and is effective for stiffer ropes. Conversely, expert users prefer VA, and highly compliant, long ropes benefit more from visual support than localized action assistance. Ultimately, these findings demonstrate that effective DLO teleoperation cannot rely on a fixed strategy, highlighting the critical need for adaptive, user-aware, and material-aware shared autonomy.
Read the full abstract ▾
All authors Berk Guler, Simon Manschitz, Kay Pompetzki, Jan Peters
Robotics
Toward Visually Realistic Simulation: A Benchmark for Evaluating Robot Manipulation in Simulation
Yixin Zhu, Zixiong Wang, Jian Yang et al. (+4 more)
Reliable simulation evaluation of
robot manipulation policies serves as a high-fidelity proxy for real-world performance. Although existing
benchmarks cover a wide range of task categories, they lack visual realism, creating a large domain gap between simulation and reality. This undermines the reliability of simulation-based evaluation in predicting real-world performance. To mitigate the sim-to-real visual gap, we conduct a systematic analysis to isolate the effects of lighting and material. Our results show that these factors play a critical role in
geometric reasoning and spatial grounding, yet are largely overlooked in existing
benchmarks. Motivated by the analysis, we propose VISER, a visually realistic
benchmark for evaluating
robot manipulation in simulation. VISER features a high-fidelity
dataset of over 1,000 3D assets with physically-based rendering (PBR) materials, along with 3D scenes created from these assets through curated layouts or generation. To this end, we propose an automated pipeline leveraging Multi-modal Large
Language Models (MLLMs) for material-aware part
segmentation and material
retrieval, enabling scalable generation of physically plausible assets. Building on the high-fidelity 3D asset
dataset, we construct diverse evaluation tasks, such as grasping, placing, and long-horizon tasks, enabling scalable and reproducible assessment of
Vision-
Language-Action (VLA) models. Our
benchmark shows a strong correlation between simulation and real-world performance, achieving an average Pearson correlation coefficient of 0.92 across different policies.
Read the full abstract ▾
All authors Yixin Zhu, Zixiong Wang, Jian Yang, Jin Xie, Jingyi Yu, Jiayuan Gu, Beibei Wang
Robotics
CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models
Yuhua Jiang, Yijun Guo, Hongbing Yang et al. (+7 more)
World action models (WAMs) provide a powerful generative framework for
embodied control, yet transferring knowledge across heterogeneous WAMs remains challenging due to mismatched latent interfaces, high adaptation cost, and the
rigidity of conventional
distillation objectives. We propose CKT-WAM, a parameter-efficient Context Knowledge Transfer framework that transfers teacher WAM's knowledge into a student WAM through a compact context in the text
embedding space, rather than output imitation or dense hidden-state matching. Specifically, CKT-WAM extracts intermediate teacher hidden states, reduces the number of
tokens via compressors' learnable-query cross
attention (LQCA), and transforms them through an always-on generalized adapter, a lightweight router, and sparsely activated specialized adapters. The resulting context is then appended to the student's conditioning textual
embeddings, thereby injecting the transferred knowledge into the student with minimal architectural modification. Experiments show that CKT-WAM consistently improves zero-shot generalization and achieves the best overall performance on LIBERO-Plus, reaching 86.1% total success rate with only 1.17% trainable parameters, while approaching full fine-tuning performance. Beyond simulation, CKT-WAM also demonstrates strong real-world long-horizon manipulation ability, achieving the best average success rate of 83.3% across four multi-step and long-horizon tasks. Code is available at https://github.com/YuhuaJiang2002/CKT-WAM.
Read the full abstract ▾
All authors Yuhua Jiang, Yijun Guo, Hongbing Yang, Guojun Lei, Nuo Chen, Yinuo Zhang, Shaoqiang Yan, Bo Lin, Feifei Gao, Biqing Qi
Robotics · Cross-listed from cs.LG
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström et al. (+2 more)
In this paper, we propose Lagrangian Gaussian Processes (LGPs) for probabilistic and data-efficient learning of dynamics via discrete forced Euler-Lagrange equations. Importantly, the
geometric structure of the Lagrange-d'Alembert principle, which governs the motion of dynamical systems, is preserved by construction in the absence of external forces. This allows learning physically consistent models that overcome erroneous drift in the system's energy, thereby providing stable long-term predictions. At the core of our approach lie linear operators for Gaussian process conditioning, constructed from discrete forced Euler-Lagrange equations and variational discretization schemes. Thereby and unlike prior work, the method enables learning dynamics from discrete position snapshots, i.e., without access to a system's velocities or momenta. This is particularly relevant for a large class of practical scenarios where only position measurements are available, for instance, in motion capture or visual servoing applications. We demonstrate the data-efficiency and generalization capabilities of the LGPs in various synthetic and real-world case studies, including a real-world soft
robot with hysteresis. The experimental results underscore that the LGPs learn physically consistent dynamics with uncertainty quantification solely from sparse positional data and enable stable long-term predictions.
Read the full abstract ▾
All authors Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström, Thomas B. Schön, Thomas Seel
Robotics
RobotEQ: Towards Social Proactive Intelligence in Embodied Agents
Kuofei Fang, Zheng Lian, Xinyi Che et al. (+13 more)
Embodied agents represent a prominent research focus across both academia and industry. The prevailing paradigm has gradually shifted from reactive assistance, which requires explicit user queries, to proactive assistance, capable of recognizing human needs and offering support without explicit instructions. Nevertheless, existing studies on proactive assistance remain confined to narrow scenarios and primarily emphasize task completeness, whereas real-world
agents must operate in open-domain environments while adhering to social expectations. To bridge this gap, we extend the concept of proactive assistance to Social Proactive Intelligence (SPI), characterized by diverse scenarios, social understanding, and
robot-centric behaviors. We further introduce RobotEQ, a dedicated
benchmark for SPI. We first define two tasks: behavior judgment, emphasizing global contextual understanding, and spatial grounding, focusing on local perceptual details. Building on these tasks, we construct RobotEQ-Data, a
dataset comprising 1,812 synthetic and 223 real-world scenarios, 7 social facets, 22K+ human annotations, 3K+ behavior judgment questions, and 3K+ spatial grounding questions. Furthermore, we establish RobotEQ-Bench to evaluate the performance of representative models. Experimental results demonstrate that current models fall short of achieving reliable SPI. Further analysis reveals that incorporating external social knowledge yields consistent improvements. This work aims to advance the development of socially desirable
embodied agents in open-domain environments.
Read the full abstract ▾
All authors Kuofei Fang, Zheng Lian, Xinyi Che, Haomin Ouyang, Shufan Zhang, Xuehao Wang, Qi Liu, Liyi Liu, Chenqi Zhang, Wenxi Cai, Wenyu Dai, Jinyang Wu, Fan Zhang, Haoyu Chen, Xiaobai Li, Bin He
Robotics · Cross-listed from cs.AI
ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
Junhyuk Kwon, Seungjoon Lee, Hyejin Park et al. (+2 more)
Natural-
language instance
navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical
agent should reduce the user's burden by actively asking only the information needed to distinguish the target from similar distractors, rather than requiring a detailed description upfront. Existing approaches often fall short of this goal by mistaking distractors that strongly match the accumulated information about the target provided by the user. As a result, despite the dialogue, the
agent may still fail to distinguish the target from distractors, leading to premature decisions and lengthy user responses. We propose Proactive Instance
Navigation with Comparative Judgment (ProCompNav), a two-stage framework that first constructs a candidate pool and then identifies the target through Recursive Comparative Judgment (RCJ). RCJ iteratively narrows the pool by selecting an attribute-value pair that divides the candidates, asking the user a binary question, and removing inconsistent candidates, without requiring an attribute unique to the target. On CoIN-Bench, ProCompNav outperforms the evaluated baselines in Success Rate while substantially reducing Response Length. On the non-interactive TextNav
benchmark, ProCompNav achieves the highest Success Rate. Two human studies further show that participants prefer ProCompNav's interaction strategies.
Read the full abstract ▾
All authors Junhyuk Kwon, Seungjoon Lee, Hyejin Park, Kyle Min, Jungseul Ok
Robotics
When to Trust Imagination: Adaptive Action Execution for World Action Models
Rui Wang, Yue Zhang, Canyang Chen et al. (+3 more)
World Action Models (WAMs) jointly predict future visual observations and actions, but typically execute a fixed number of actions before replanning, regardless of whether the imagined future remains consistent with reality. We formulate adaptive WAM execution as future–reality verification and propose Future Forward Dynamics Causal
Attention (FFDC), a lightweight verifier that reasons over predicted actions, predicted visual dynamics, real observations, and instruction-conditioned semantics. FFDC allows continued execution when a plan remains reliable and triggers replanning when its validity deteriorates. We combine verification with Mixture-of-Horizon Training to improve long-horizon trajectory coverage. On RoboTwin, FFDC-WAM achieves the highest average success rate among the evaluated methods in both clean and randomized settings. Under randomization, it improves success by 2.54 percentage points over Base-Motus while reducing WAM planning calls by 69.10% and completion time by 33.88%. Notably, across six real-world tasks on a Unitree G1D
robot, FFDC-WAM improves average success by 9.17% over Base-Motus-LC16 while reducing calls by 74.06% and completion time by 51.31%.
Read the full abstract ▾
All authors Rui Wang, Yue Zhang, Canyang Chen, Jiehong Lin, Zhongrui Wang, Xiaojuan Qi
Robotics · Cross-listed from cs.CV
EA-WM: Event-Aware Generative World Model with Structured Kinematic-to-Visual Action Fields
Zhaoyang Yang, Yurun Jin, Lizhe Qi et al. (+2 more)
Pretrained
video diffusion models provide powerful spatiotemporal generative priors, making them a natural foundation for robotic world models. While recent world-action models jointly optimize future
videos and actions, they predominantly treat
video generation as an auxiliary representation for policy learning. Consequently, they insufficiently explore the inverse problem: leveraging action signals to guide
video synthesis, thereby often failing to preserve precise
robot spatial geometry and fine-grained
robot-object interaction dynamics in the generated rollouts. To bridge this gap, we present EA-WM, an Event-Aware Generative World Model that effectively closes the loop between kinematic control and visual perception. Rather than injecting joint or end-effector actions as abstract, low-dimensional
tokens, EA-WM projects actions and kinematic states directly into the target camera view as Structured Kinematic-to-Visual Action Fields. To fully exploit this geometrically grounded representation, we introduce event-aware bidirectional fusion blocks that modulate cross-branch
attention, capturing object state changes and interaction dynamics. Evaluated on the comprehensive WorldArena
benchmark, EA-WM achieves state-of-the-art performance, outperforming existing baselines by a significant margin.
Read the full abstract ▾
All authors Zhaoyang Yang, Yurun Jin, Lizhe Qi, Cong Huang, Kai Chen
Robotics
VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
Yuhua Jiang, Junjie Lu, Xinyao Qin et al. (+4 more)
Vision-
language-action (VLA) models inherit rich visual-semantic priors from pre-trained
vision-
language backbones, but adapting them to robotic control remains challenging. Full fine-tuning (FFT) is prone to overfitting on downstream robotic data and catastrophic forgetting of pretrained
vision-
language capabilities. Parameter-efficient fine-tuning (PEFT) better preserves pre-trained knowledge, yet existing PEFT methods still struggle to adapt effectively to
robot control tasks. To address this gap, we propose VLA-GSE, a parameter-efficient VLA fine-tuning framework that improves control adaptation while retaining PEFT's knowledge preservation advantage. Specifically, VLA-GSE (Generalized and Specialized Experts) is initialized by spectrally decomposing the frozen backbone, assigning leading singular components to generalized experts (shared experts) and disjoint residual components to specialized experts (routed experts). This decomposition improves adaptation capacity under a fixed trainable-parameter budget. Under a comparable parameter budget, VLA-GSE updates only 2.51% of the full model parameters and consistently outperforms strong FFT and PEFT baselines. It achieves 81.2% average zero-shot success on LIBERO-Plus, preserves pre-trained VLM capability comparably to LoRA on
multimodal understanding
benchmarks, and improves real-world manipulation success under multiple distribution shifts. Code is available at: https://github.com/YuhuaJiang2002/VLA-GSE
Read the full abstract ▾
All authors Yuhua Jiang, Junjie Lu, Xinyao Qin, Xiaoyu Chen, Kaixin Wang, Feifei Gao, Li Zhao
Robotics · Cross-listed from eess.SP
CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
Liang Qian, Penggao Yan, Penghui Xu et al. (+1 more)
Global
navigation satellite system (GNSS) positioning is widely used for urban
navigation, but the covariance reported by the GNSS solver is often unreliable in urban canyons. Existing differentiable factor graph optimization (DFGO) methods learn measurement weighting through the solver, but they still use position-only objectives. As a result, the position estimate may improve while the reported covariance remains too small, too large, or incorrectly oriented. We propose CredibleDFGO (CDFGO), a differentiable GNSS factor graph framework that makes covariance credibility an explicit training target. A Weighting Generation Network (WGN) predicts per-satellite reliability weights, and a differentiable Gauss-Newton solver maps these weights to a position estimate and a Hessian-derived posterior covariance. We use proper scoring rules to supervise the East-North predictive distribution end to end. We study negative log-likelihood (NLL), the energy score (ES), and their combination. Results on three UrbanNav test scenes show consistent gains in covariance credibility. Positioning accuracy also improves on the medium-urban and harsh-urban scenes; on the deep-urban scene, both the mean horizontal error and the 95th-percentile error improve. On the harsh-urban Mong Kok (MK) scene, CDFGO-Combined reduces the mean horizontal error from 13.77 m to 11.68 m, reduces NLL from 40.63 to 6.59, and reduces ES from 12.31 to 9.05 relative to DFGO (MAE). Case studies link the MK improvement to better axis-wise consistency, more credible local covariance ellipses, and satellite-level reweighting.
Read the full abstract ▾
All authors Liang Qian, Penggao Yan, Penghui Xu, Li-Ta Hsu
Robotics
Monitoring autonomous persistent surveillance missions using invariance
Vladislav Nenchev and Prodromos Sotiriadis
This paper studies runtime monitoring for persistent surveillance by autonomous
robots when the autonomy stack is a black box. The environment is partitioned into finitely many parts, each carrying an uncertainty state that decreases when observed and increases otherwise. We model the closed loop as a state-dependent hybrid system with linear parameter varying dynamics and design a monitor based on an invariant computed offline. As this invariant is typically hard to obtain for large to-be-surveyed spaces, we propose a compositional monitor obtained by decentralized computation of low-dimensional invariant sets for each uncertainty region, and checking their conjunction online. Under common independence assumptions, the compositional monitor is sound and complete with respect to the full-system invariant. The approach is applied in a case study with a real
robot persistently monitoring a labyrinth, emphasizing its applicability in practice.
Read the full abstract ▾
Robotics
Accurate Trajectory Tracking with Model Predictive Contouring Control for Bird-Scale Flapping-Wing MAVs
Charbel Toumieh, Jack Zeng, Niel Mistry et al. (+1 more)
Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scale ornithopters remains challenging: lift, airspeed, and turning authority are tightly coupled and governed by only a few control inputs. Conventional cascaded controllers treat altitude, speed, and heading independently, producing persistent tracking errors during complex maneuvers, while time-parameterized trajectory tracking requires predefined speed profiles that existing methods cannot robustly produce for these coupled dynamics. We address both limitations simultaneously with a Model Predictive Contouring Control (MPCC) approach that tracks arc-length-parameterized trajectories while optimizing progress online, eliminating the need for predefined timing. However, MPCC requires a dynamical model that captures the coupled aerodynamics without exceeding the computational budget of real-time nonlinear optimization. Here, we propose a compact, continuously differentiable model that captures the dominant couplings of bird-scale ornithopters, enabling real-time predictive control. We validated the method with the XFly ornithopter flying along circular and three-dimensional racing trajectories and achieved a mean deviation from the reference trajectory between 6.5 and 9 cm at speeds up to 3 m/s, which represents an 8.5x improvement over prior ornithopter control methods.
Read the full abstract ▾
All authors Charbel Toumieh, Jack Zeng, Niel Mistry, Dario Floreano
Robotics
Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation
Zhixuan Shen, Yijie Zeng, Shengxiang Luo et al. (+2 more)
In
embodied vision, Goal-Oriented
Navigation (GON) requires
robots to locate a specific goal within an unexplored environment. The primary challenge of GON arises from the need to construct a Bird's-Eye-View (BEV) map to understand the environment while simultaneously localizing an unobserved goal. Existing map-based methods typically employ self-centered semantic maps, often facing challenges such as reliance on complete maps or inconsistent semantic association. To this end, we propose Plug-and-Play Label Map
Diffusion (PLMD), which defines a novel map completion
diffusion model based on Denoising
Diffusion Probabilistic Models (DDPM). PLMD generates obstacle and semantic labels for unobserved regions through a
diffusion-based completion process, thereby enabling goal localization even in partially observed environments. Moreover, it mitigates inconsistent semantic association by leveraging structural consistency between known and unknown obstacle layouts and integrating obstacle priors into the semantic denoising process. By substituting predicted labels for unobserved regions,
robots can accurately localize the specified objects. Extensive experiments demonstrate that PLMD (I) effectively expands the region of unknown maps, (II) integrates seamlessly into existing
navigation strategies that rely on semantic maps, (III) achieves state-of-the-art performance on three GON tasks.
Read the full abstract ▾
All authors Zhixuan Shen, Yijie Zeng, Shengxiang Luo, Tianrui Li, Haonan Luo
Robotics
DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions
Hyesung Lee, Hyunwoo Jung, Si-Hwan Heo et al. (+1 more)
Learning dexterous manipulation from human-object interaction (HOI) data offers a scalable alternative to
robot teleoperation, but HOI demonstrations are typically sparse and purely kinematic, making direct retargeting unreliable under embodiment mismatch and contact-rich dynamics. We present DexSynRefine, a coupled framework that treats HOI data as structured motion priors rather than executable
robot actions. DexSynRefine first synthesizes hand-object trajectories conditioned on the task and initial object state using HOI Motion
Manifold Flow Primitives (HOI-MMFP), a motion prior for coupled hand-object motion. It then physically grounds them with task-space residual reinforcement learning and adapts execution by inferring missing contact-dynamics context from proprioceptive history. Across five dexterous manipulation tasks, each stage addresses a complementary bottleneck: HOI-MMFP improves trajectory consistency and smoothness, task-space residuals provide the strongest grounding representation among the tested alternatives, and contact-dynamics adaptation enables robust real-world execution. Together, DexSynRefine improves real-world success rates over kinematic retargeting by 50-70~percentage points.
Read the full abstract ▾
All authors Hyesung Lee, Hyunwoo Jung, Si-Hwan Heo, Sungwook Yang
Robotics
Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis
Yuhan Xia, Runxin Zhao, Hanyang Zhuang et al. (+2 more)
Intelligent Transportation Systems (ITS) require reliable environmental perception to support safe and efficient transportation. With the rapid development of Vehicle-to-everything (V2X), roadside perception has become an effective means to extend sensing coverage and improve traffic safety. However, the scarcity of large-scale annotated roadside
LiDAR datasets poses a major challenge for training high-performance roadside perception models. In this paper, we introduce Vehicle-to-Roadside
LiDAR Synthesis (VRS), a data synthesis framework that generates labeled roadside
LiDAR datasets from vehicle-side
datasets via
LiDAR novel view synthesis. To mitigate the vehicle-to-roadside domain gap, VRS employs vehicle point cloud completion to compensate for missing geometry in vehicle-side observations, and introduces an occupancy-based visibility constraint to handle large viewpoint changes during cross-view rendering. The proposed framework enables flexible multi-view rendering for scalable roadside data generation. Extensive experiments on roadside 3D object detection demonstrate that the synthesized data effectively complements real roadside data, mitigates the limitations of limited real-world roadside data, and improves generalization to unseen roadside viewpoints.
Read the full abstract ▾
All authors Yuhan Xia, Runxin Zhao, Hanyang Zhuang, Chunxiang Wang, Ming Yang
Robotics
Cycle-resolved Cephalopod-Inspired Pulsed-Jet Robot With High-Volume Expulsion and Drag-Reduced Gliding
Yiyuan Zhang, Anye Zhong, Junkai Chen et al. (+1 more)
Cephalopod pulsed-jet locomotion is not a single isolated expulsion event, but a coordinated cycle involving jet expulsion, passive gliding, and mantle refilling. Inspired by this cycle-resolved biological strategy, this paper presents a cephalopod-inspired pulsed-jet
robot with a rigid-soft hybrid origami mantle that enables large, actively driven, and geometry-guided body deformation. The proposed mantle integrates rigid folding panels with a compliant silicone framework, allowing a 75% effective cavity-volume reduction during expulsion and reducing the projected cross-sectional drag area by approximately 75.7% in the contracted gliding configuration. Using this platform, we formulate a cycle-resolved framework to separately investigate how expelled volume, glide duration, and refill pathway influence whole-cycle locomotion performance. Experiments show that the
robot reaches a peak speed of approximately 0.5 m/s (3.8 BL/s) and an average speed exceeding 0.2 m/s (1.5 BL/s) within the first jetting cycle. The results further demonstrate the roles of high expelled-volume-ratio contraction in speed generation, reduced-drag-area gliding under different glide durations, and mantle-aperture-inspired passive inlet valves in assisting refill. This work provides both a robotic implementation of actively deformable cephalopod-like jet propulsion and a unified experimental platform for studying expulsion-gliding-refilling dynamics in pulsed-jet locomotion.
Read the full abstract ▾
All authors Yiyuan Zhang, Anye Zhong, Junkai Chen, Wenci Xin
Robotics
A Comparative Study of INDI and NDI with Nonlinear Disturbance Observer for Aerial Robotics
Benedetta Rota, Mirko Mizzoni, Amr Afifi et al. (+2 more)
This work presents a simulation-based comparative robustness analysis of Incremental Nonlinear Dynamic Inversion (INDI) and Nonlinear Dynamic Inversion augmented with a nonlinear disturbance observer (NDI+NDO) for fully actuated aerial
robots. A systematic simulation campaign across representative operating scenarios is conducted, where we compare tracking performance, robustness, control effort, under parametric variations, external disturbances, and measurement noise. Results show that INDI demonstrates stronger robustness in several model-mismatch and combined-stress cases, while NDI+NDO primarily matches nominal performance but exhibits greater sensitivity under several non-ideal conditions. These findings provide practical guidance on the relative strengths and limitations of incremental and observer-based inversion strategies for aerial robotic applications.
Read the full abstract ▾
All authors Benedetta Rota, Mirko Mizzoni, Amr Afifi, Pieter van Goor, Antonio Franchi
Robotics
Resource-Constrained Robotic Planning in the face of Mixed Uncertainty
Yihao Yin, Pian Yu, Andrea Turrini et al. (+3 more)
Robots operate under significant uncertainty, from quantifiable noise to unquantifiable unknowns, and must account for strict operational constraints, such as limited resources. In this paper, we consider the problem of synthesizing robust strategies to guide a
robot's actions in fulfilling a given task, while ensuring the system never exhausts its resources. To solve this problem, we first model the robotic system as a Consumption Markov Decision Process with Set-valued Transitions(CMDPST), a unified framework modelling nondeterministic actions, quantifiable and unquantifiable uncertainty, and resource consumption. Then, we combine the CMDPST with the task specification, expressed as a Linear Temporal Logic over finite traces (LTLf ) formula. Lastly, we address the resource constrained optimal robust strategy synthesis problem, which aims to synthesize a strategy that maximizes the probability of satisfying the LTLf objective without resource exhaustion. Our solution involves two techniques: a direct unrolling-based method and a more efficient, optimized approach that leverages state-space
pruning for better performance. Experiments on a warehouse transportation network show the effectiveness of the proposed solutions.
Read the full abstract ▾
All authors Yihao Yin, Pian Yu, Andrea Turrini, Zhiming Chi, Yong Li, Lijun Zhang
Robotics
MaMi-HOI: Harmonizing Global Kinematics and Local Geometry for Human-Object Interaction Generation
Hao Wang, Shiqi Wang and Qi Liu
Generating realistic 3D Human-Object Interactions (HOI) is a fundamental task for applications ranging from
embodied AI to virtual content creation, which requires harmonizing high-level semantic intent with strict low-level physical constraints. Existing methods excel at semantic
alignment, however, they struggle to maintain precise object contact. We reveal a key finding termed
Geometric Forgetting: as
diffusion model depth increases, semantic feature tend to overshadow object geometry feature, causing the model to lose its perception to object geometry. To address this, we propose MaMi-HOI, a hierarchical framework reconciling Macro-level kinematic fluidity with Micro-level spatial precision. First, to counteract
geometric forgetting, we introduce the Geometry-Aware Proximity Adapter (GAPA), which explicitly re-injects dense object details to perform residual snapping corrections for precise contact. Nevertheless, such aggressive local enforcement can disrupt global dynamics, leading to robotic stiffness. In response, we introduce the Kinematic Harmony Adapter (KHA), which proactively aligns whole-body posture with spatial objectives, ensuring the skeleton actively accommodates constraints without compromising naturalness. Extensive experiments validate that MaMi-HOI simultaneously achieves natural motion and precise contact. Crucially, it extends generation capabilities to long-term tasks with complex trajectories, effectively bridging the gap between global
navigation and high-fidelity manipulation in 3D scenes. Code is available at https://github.com/DON738110198/MaMi-HOI.git
Read the full abstract ▾
Robotics · Cross-listed from cs.CV
TriRelVLA: Triadic Relational Structure for Generalizable Embodied Manipulation
Hanyu Zhou, Chuanhao Ma and Gim Hee Lee
Vision-
language-action (VLA) models perform well on training-seen robotic tasks but struggle to generalize to unseen scenes and objects. A key limitation lies in their implicit visual representations, which entangle object appearance, background, and scene layout. This makes policies sensitive to visual variations. Prior work improves transferability through structured intermediate representations that objectify visual content. However, these representations mainly capture scene semantics instead of action-relevant relations. As a result, action prediction remains tied to appearance statistics. We observe that manipulation actions depend on the object-hand-task relational structure, which governs interactions among task requirements,
robot states, and object properties. Based on this observation, we propose TriRelVLA, a triadic relational VLA framework for generalizable
embodied manipulation. Our approach consists of three components: 1) We construct explicit object-hand-task triadic representations from
multimodal inputs as relational primitives. 2) We build a task-grounded relational graph. Task-guided cross-
attention forms nodes, and a relation-aware graph
transformer models interactions among them. 3) We perform relation-conditioned action generation. The relational structure is compressed into a bottleneck space and projected into the LLM for action prediction. This triadic relational bottleneck reduces reliance on appearance statistics and enables transfer across scenes, objects, and task compositions. We further introduce a real-world robotic
dataset for fine-tuning. Experiments show strong performance on fine-tuned tasks and clear gains in cross-scene, cross-object, and cross-task generalization.
Read the full abstract ▾
Robotics
On the Emergence of Pendular Structure in Multi-Contact Locomotion
Lingxue Lyu and Zihui Liu
LIPM is everywhere in legged-locomotion control, but almost always as a modeling choice rather than as something the controller's cost actually prefers. This note tries to make that link more explicit. Working from a small centroidal OCP that penalizes the rate of angular momentum, we look at what its optimum tends to look like. Three things come out. With full-rank stance, the optimum drifts toward a pendular force pattern at a rate determined by the SVD of the moment Jacobian; the constant is set by foot-span geometry and matches the experiments to within 16%. With N=2 stance, as in trot, the friction cone introduces a lower bound on $\|\dot{H}_G\|$ that no amount of weight tuning fixes; we also see a non-smooth feasibility kink at a critical horizontal acceleration that we can write in closed form. Adding a task term that asks for a nonzero $\dot{H}_G$ moves the optimum off the pendular set in a predictable way. None of this is far from the classical ZMP/DCM picture. We test these claims on a point-mass quadruped and on the Unitree Go1 in MuJoCo (open-loop QP and a torque-level closed-loop controller), and we note where the asymptotic story stops being a good description of what the closed loop actually does.
Read the full abstract ▾
Robotics · Cross-listed from cs.CV
Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping
Gabriel Jeanson, David-Alexandre Duclos, William Larrivée-Hardy et al. (+5 more)
Sustainable forest management relies on precise species composition mapping, yet traditional ground surveys are labour-intensive and geographically constrained. While Uncrewed Aerial Vehicles (
UAVs) offer scalable data collection, the transition to deep learning-based interpretation is bottlenecked by the severe scarcity of expert-annotated imagery, particularly in complex, visually heterogeneous regeneration zones. This paper addresses the dual challenges of data scarcity and extreme class imbalance in the fine-grained semantic
segmentation of plants by providing a scalable framework that reduces reliance on manual photo-interpretation for high-resolution, millimetre-level aerial imagery. Importantly, we leverage the large-scale Nano Banana Pro model to simultaneously generate high-fidelity images and their corresponding pixel-aligned semantic masks from
prompts. We introduce WilDReF-Q-V2, an expansion of a natural forest
dataset with 13 977 new unlabelled and 50 hand-labelled real images, as well as the Gen4Regen
dataset, featuring 2101 pairs of synthetic images and semantic masks. Our methodology integrates real-world data with AI-generated images, highlighting that AI-generated data is highly complementary to real-world data, with unified training yielding an F1 score improvement of over 15 %pt compared to purely supervised baselines. Furthermore, we demonstrate that even small quantities of
prompt-generated data significantly improve performance for underrepresented classes, some of which see per-class F1 score gains of over 30 %pt. We conclude that large-scale
vision models can serve as agile data generators, effectively bootstrapping perception tasks for niche AI domains where expert labels are scarce or unavailable. Our
datasets, source code, and models will be available at https://norlab-ulaval.github.io/gen4regen.
Read the full abstract ▾
All authors Gabriel Jeanson, David-Alexandre Duclos, William Larrivée-Hardy, Noé Cochet, Matěj Boxan, Anthony Deschênes, François Pomerleau, Philippe Giguère
Robotics · Cross-listed from eess.SY
Maximal Controlled Invariant-MPC: Enhancing Feasibility and Reducing Conservatism through Terminal CBF Constraint in Safety-Critical Control
Tanmay Dokania and Yashwanth Kumar Nakka
Optimal control for safety-critical systems is often dependent on the conservativeness of constraints. Control Barrier Functions (CBFs) serve as a medium to represent such constraints, but constructing a minimally conservative CBF is a computationally intractable problem. Therefore, approaches that can guarantee safety while reducing conservatism will help improve the optimality of the system under consideration. Here, we present a Model Predictive Control (MPC) formulation using CBF as a terminal constraint, which is proven to improve feasibility and reachable sets with increasing prediction horizon. The constructive nature of the proofs allows for warm-starting the nonlinear optimization problem, thereby reducing the computational time substantially. Simulations are set up for a simple nonholonomic system to numerically validate the results, and it is observed that the number of infeasible points decreased by a factor of 1.7 to 2.7. The increase in reachable state space was demonstrated by the ability of the system to track trajectories that are entirely inside the unsafe region of the control barrier function.
Read the full abstract ▾
Robotics · Cross-listed from cs.LG
Adaptive Q-Chunking for Offline-to-Online Reinforcement Learning
Nandiraju Gireesh, Yuanliang Ju and He Wang
Offline-to-online reinforcement learning with action chunking eliminates multi-step off-policy bias and enables temporally coherent exploration, but all existing methods use a fixed chunk size across every state. This is suboptimal: near contact events the
agent needs short chunks for reactive control, while during free-space motion long chunks provide better credit assignment. The natural solution is to train critics for several chunk sizes and select the best one at each state, but naive comparison of learned critic values systematically collapses to the shortest chunk due to discount-scale mismatch, and degrades to noise in low-value states. We propose Adaptive Q-Chunking (AQC), which resolves both failures by comparing the advantage of each chunk size relative to a per-horizon baseline, normalized by the discount factor. This criterion converts biased wrong answers into unbiased near-random choices when no genuine signal exists, and becomes discriminative when a particular scale enables better planning. We prove theoretical bounds on the advantage selector's noise immunity and on the value dominance of adaptive chunking over any fixed chunk size. We demonstrate that AQC achieves state-of-the-art offline and online success rates on OGBench and Robomimic, and can be applied to enhance the performance of large-scale VLA models that predict action sequences, significantly boosting performance on RoboCasa-GR1 tasks.
Read the full abstract ▾
Robotics
Real-world Latency Analysis of Vehicular Visible Light Communication with Multiple LED Transmitters and an Event-Based Camera
Ryota Soga, Tsukasa Shimizu, Shintaro Shiba et al. (+3 more)
Event cameras offer high temporal resolution, low latency, and wide dynamic range, making them promising receivers for visible light communication (VLC) in vehicle-to-everything (V2X) applications. This work presents an event-camera-based VLC system addressing three key challenges: bandwidth saturation, multi-transmitter reception, and latency characterization. We adopt a positive-event-only mode and design a protocol that suppresses event generation while maintaining communication distance and a wide field of view. We also propose a method to identify multiple transmitters and demonstrate simultaneous reception from up to three LEDs. Finally, we evaluate end-to-end latency in real vehicular scenarios and show that the system meets cooperative perception requirements. These results demonstrate that event-camera-based VLC is a feasible complement to existing V2X technologies (e.g., RF).
Read the full abstract ▾
All authors Ryota Soga, Tsukasa Shimizu, Shintaro Shiba, Quan Kong, Shan Lu, Takaya Yamazato