Robotics
Fast-dVLA: Accelerating Discrete Diffusion VLA to Real-Time Performance
Wenxuan Song, Jiayi Chen, Shuai Chen et al. (+8 more)
This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning (SFT). Some advanced finetuning methods with auxiliary training objectives can improve performance and reduce the number of convergence steps. However, they typically incur significant computational overhead due to the additional losses from auxiliary tasks. To simultaneously achieve the enhanced capabilities of auxiliary training with the simplicity of standard SFT, we decouple the two objectives of auxiliary task training within the parameter space, namely, enhancing general capabilities and fitting task-specific action distributions. To deliver this goal, we only need to train the model to converge on a small-scale task set using two distinct training strategies. The difference between the resulting model parameters can then be interpreted as capability vectors provided by auxiliary tasks. These vectors are then merged with pretrained parameters to form a capability-enhanced meta model. Moreover, when standard SFT is augmented with a lightweight orthogonal regularization loss, the merged model attains performance comparable to auxiliary finetuned baselines with reduced computational overhead. Experimental results demonstrate that this approach is highly effective across diverse
robot tasks. Project page: https://chris1220313648.github.io/Fast-dVLA/
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All authors Wenxuan Song, Jiayi Chen, Shuai Chen, Jingbo Wang, Pengxiang Ding, Han Zhao, Yikai Qin, Xinhu Zheng, Donglin Wang, Yan Wang, Haoang Li
Robotics
A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots
Giulio Pisaneschi, Pierpaolo Serio, Estelle Gerbier et al. (+3 more)
This paper presents an experimental platform for studying intentional-state attribution toward a non-
humanoid robot. The system combines a simulated
robot, realistic task environments, and large
language model-based explanatory layers that can express the same behavior in mentalistic, teleological, or mechanistic terms. By holding behavior constant while varying the explanatory frame, the platform provides a controlled way to investigate how
language and framing shape the adoption of the intentional stance in robotics.
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All authors Giulio Pisaneschi, Pierpaolo Serio, Estelle Gerbier, Andrea Dan Ryals, Lorenzo Pollini, Mario G. C. A. Cimino
Robotics
Neural Surface and Reflectance Modelling from 3D Radar Data
Judith Treffler, Vladimír Kubelka, Henrik Andreasson et al. (+1 more)
Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. In these conditions, radar has a clear advantage over cameras and
lidars due to its resilience to environmental factors such as fog, smoke, or
dust. However, radar data is inherently sparse and noisy, making reliable 3D surface reconstruction challenging. To address this, we propose a neural implicit approach for 3D mapping from radar point clouds that jointly models scene geometry and view-dependent radar intensities. Our method leverages a
memory-efficient hybrid feature encoding to learn a continuous Signed Distance Field (SDF) for surface reconstruction, while also capturing radar-specific reflective properties. We show that our approach produces smoother, more accurate 3D surface reconstructions compared to existing
lidar-based reconstruction methods applied to radar data and can reconstruct view-dependent radar intensities. We also show that, in general, as input point clouds get sparser, neural implicit representations render more faithful surfaces than traditional explicit SDFs and meshing techniques.
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All authors Judith Treffler, Vladimír Kubelka, Henrik Andreasson, Martin Magnusson
Robotics
Towards Generalizable Robotic Data Flywheel: High-Dimensional Factorization and Composition
Yuyang Xiao, Yifei Zhou, Haoran Wang et al. (+2 more)
The lack of sufficiently diverse data, coupled with limited data efficiency, remains a major bottleneck for generalist robotic models, yet systematic strategies for collecting and curating such data are not fully explored. Task diversity arises from implicit factors that are sparsely distributed across multiple dimensions and are difficult to define explicitly. To address this challenge, we propose F-ACIL, a heuristic factor-aware compositional iterative learning framework that enables structured data factorization and promotes compositional generalization. F-ACIL decomposes the data distribution into structured factor spaces such as object, action, and environment. Based on the factorized formulation, we develop a factor-wise data collection and an iterative training paradigm that promotes compositional generalization over the high-dimensional factor space, leading to more effective utilization of real-world robotic demonstrations. With extensive real-world experiments, we show that F-ACIL can achieve more than 45% performance gains with 5-10$\times$ fewer demonstrations comparing to that of which without the strategy. The results suggest that structured factorization offers a practical pathway toward efficient compositional generalization in real-world robotic learning. We believe F-ACIL can inspire more systematic research on building generalizable robotic data flywheel strategies. More demonstrations can be found at: https://f-acil.github.io/
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All authors Yuyang Xiao, Yifei Zhou, Haoran Wang, Wenxuan Ou, Yuxiao Liu
Robotics
Towards Embodied AI with MuscleMimic: Unlocking full-body musculoskeletal motor learning at scale
Chengkun Li, Cheryl Wang, Bianca Ziliotto et al. (+4 more)
Learning motor control for muscle-driven musculoskeletal models is hindered by the computational cost of biomechanically accurate simulation and the scarcity of validated, open full-body models. Here we present MuscleMimic, an open-source framework for scalable motion imitation learning with physiologically realistic, muscle-actuated
humanoids. MuscleMimic provides two validated musculoskeletal embodiments - a fixed-root upper-body model (126 muscles) for bimanual manipulation and a full-body model (416 muscles) for locomotion - together with a retargeting pipeline that maps SMPL-format motion capture data onto musculoskeletal structures while preserving kinematic and dynamic consistency. Leveraging massively parallel GPU simulation, the framework achieves order-of-magnitude training speedups over prior CPU-based approaches while maintaining comprehensive collision handling, enabling a single generalist policy to be trained on hundreds of diverse motions within days. The resulting policy faithfully reproduces a broad repertoire of human movements under full muscular control and can be fine-tuned to novel motions within hours. Biomechanical validation against experimental walking and running data demonstrates strong agreement in joint kinematics (mean correlation r = 0.90), while muscle activation analysis reveals both the promise and fundamental challenges of achieving physiological fidelity through kinematic imitation alone. By lowering the computational and data barriers to musculoskeletal simulation, MuscleMimic enables systematic model validation across diverse dynamic movements and broader participation in neuromuscular control research. Code, models, checkpoints, and retargeted
datasets are available at: https://github.com/amathislab/musclemimic
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All authors Chengkun Li, Cheryl Wang, Bianca Ziliotto, Merkourios Simos, Jozsef Kovecses, Guillaume Durandau, Alexander Mathis
Robotics
LILAC: Language-Conditioned Object-Centric Optical Flow for Open-Loop Trajectory Generation
Motonari Kambara, Koki Seno, Tomoya Kaichi et al. (+2 more)
We address
language-conditioned robotic manipulation using flow-based trajectory generation, which enables training on human and web
videos of object manipulation and requires only minimal embodiment-specific data. This task is challenging, as object trajectory generation from pre-manipulation images and natural
language instructions requires appropriate instruction-flow
alignment. To tackle this challenge, we propose the flow-based
Language Instruction-guided open-Loop ACtion generator (LILAC). This flow-based
Vision-
Language-Action model (VLA) generates object-centric 2D optical flow from an RGB image and a natural
language instruction, and converts the flow into a 6-DoF manipulator trajectory. LILAC incorporates two key components: Semantic
Alignment Loss, which strengthens
language conditioning to generate instruction-aligned optical flow, and
Prompt-Conditioned Cross-Modal Adapter, which aligns learned visual
prompts with image and text features to provide rich cues for flow generation. Experimentally, our method outperformed existing approaches in generated flow quality across multiple
benchmarks. Furthermore, in physical object manipulation experiments using free-form instructions, LILAC demonstrated a superior task success rate compared to existing methods. The project page is available at https://lilac-75srg.kinsta.page/.
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All authors Motonari Kambara, Koki Seno, Tomoya Kaichi, Yanan Wang, Komei Sugiura
Robotics
Temporally Decoupled Diffusion Planning for Autonomous Driving
Xiang Li, Bikun Wang, John Zhang et al. (+1 more)
Motion planning in dynamic urban environments requires balancing immediate safety with long-term goals. While
diffusion models effectively capture multi-modal decision-making, existing approaches treat trajectories as monolithic entities, overlooking heterogeneous temporal dependencies where near-term plans are constrained by instantaneous dynamics and far-term plans by navigational goals. To address this, we propose Temporally Decoupled
Diffusion Model (TDDM), which reformulates trajectory generation via a noise-as-mask paradigm. By partitioning trajectories into segments with independent noise levels, we implicitly treat high noise as information voids and weak noise as contextual cues. This compels the model to reconstruct corrupted near-term states by leveraging internal correlations with better-preserved temporal contexts. Architecturally, we introduce a Temporally Decoupled Adaptive Layer Normalization (TD-AdaLN) to inject segment-specific timesteps. During inference, our Asymmetric Temporal Classifier-Free Guidance utilizes weakly noised far-term priors to guide immediate path generation. Evaluations on the nuPlan
benchmark show TDDM approaches or exceeds state-of-the-art baselines, particularly excelling in the challenging Test14-hard subset.
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All authors Xiang Li, Bikun Wang, John Zhang, Jianjun Wang
Robotics
Visualizing Impedance Control in Augmented Reality for Teleoperation: Design and User Evaluation
Gijs van den Brandt, Femke van Beek and Elena Torta
Teleoperation for contact-rich manipulation remains challenging, especially when using low-cost, motion-only interfaces that provide no haptic feedback. Virtual reality controllers enable intuitive motion control but do not allow operators to directly perceive or regulate contact forces, limiting task performance. To address this, we propose an augmented reality (AR) visualization of the impedance controller's target pose and its displacement from each
robot end effector. This visualization conveys the forces generated by the controller, providing operators with intuitive, real-time feedback without expensive haptic hardware. We evaluate the design in a dual-arm manipulation study with 17 participants who repeatedly reposition a box with and without the AR visualization. Results show that AR visualization reduces completion time by 24% for force-critical lifting tasks, with no significant effect on sliding tasks where precise force control is less critical. These findings indicate that making the impedance target visible through AR is a viable approach to improve human-
robot interaction for contact-rich teleoperation.
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Robotics · Cross-listed from cs.AI
Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation
Roman Küble, Marco Hüller, Mrunmai Phatak et al. (+2 more)
Semantic world models enable
embodied agents to reason about objects, relations, and spatial context beyond purely
geometric representations. In Organic Computing, such models are a key enabler for objective-driven self-adaptation under uncertainty and resource constraints. The core challenge is to acquire observations maximising model quality and downstream usefulness within a limited action budget. Semantic scene graphs (SSGs) provide a structured and compact representation for this purpose. However, constructing them within a finite action horizon requires exploration strategies that trade off information gain against
navigation cost and decide when additional actions yield diminishing returns. This work presents a modular
navigation component for
Embodied Semantic Scene Graph Generation and modernises its decision-making by replacing the policy-optimisation method and revisiting the discrete action formulation. We study compact and finer-grained, larger discrete motion sets and compare a single-head policy over atomic actions with a factorised multi-head policy over action components. We evaluate curriculum learning and optional depth-based collision supervision, and assess SSG completeness, execution safety, and
navigation behaviour. Results show that replacing the optimisation
algorithm alone improves SSG completeness by 21% relative to the baseline under identical reward shaping. Depth mainly affects execution safety (collision-free motion), while completeness remains largely unchanged. Combining modern optimisation with a finer-grained, factorised action representation yields the strongest overall completeness–efficiency trade-off.
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All authors Roman Küble, Marco Hüller, Mrunmai Phatak, Rainer Lienhart, Jörg Hähner
Robotics
MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation
Yang Liu, Pengxiang Ding, Tengyue Jiang et al. (+10 more)
Vision-
Language-Action (VLA) models map visual observations and natural-
language instructions to
robot actions; however, hierarchical and autoregressive paradigms often incur architectural overhead, accumulate long-horizon errors, and require auxiliary modules to capture environment dynamics. To this end, we present MMaDA-VLA, a fully native, pretrained discrete
diffusion VLA that unifies multi-modal understanding and generation. Specifically, MMaDA-VLA uses a shared discrete
token space to jointly denoise a future goal observation and an action chunk, grounding actions in predicted visual outcomes without an auxiliary world model. In this way, parallel, order-free refinement improves long-horizon consistency. Extensive experiments and comprehensive analyses demonstrate that MMaDA-VLA achieves an average success rate of 98.0% on LIBERO and an average successful sequence length of 4.78 on CALVIN, while performing strongly in real-world settings. The project page is available at https://yliu-cs.github.io/MMaDA-VLA.
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All authors Yang Liu, Pengxiang Ding, Tengyue Jiang, Xudong Wang, Wenxuan Song, Minghui Lin, Han Zhao, Hongyin Zhang, Zifeng Zhuang, Wei Zhao, Siteng Huang, Jinkui Shi, Donglin Wang
Robotics
System Design for Maintaining Internal State Consistency in Long-Horizon Robotic Tabletop Games
Guangyu Zhao, Ceyao Zhang, Chengdong Ma et al. (+16 more)
Long-horizon tabletop games pose a distinct systems challenge for robotics: small perceptual or execution errors can invalidate accumulated task state, propagate across decision-making modules, and ultimately derail interaction. This paper studies how to maintain internal state consistency in turn-based, multi-human robotic tabletop games through deliberate system design rather than isolated component improvement. Using Mahjong as a representative long-horizon setting, we present an integrated architecture that explicitly maintains perceptual, execution, and interaction state, partitions high-level semantic
reasoning from time-critical perception and control, and incorporates verified action primitives with tactile-triggered recovery to prevent premature state corruption. We further introduce interaction-level monitoring mechanisms to detect turn violations and hidden-information breaches that threaten execution assumptions. Beyond demonstrating complete-game operation, we provide an empirical characterization of failure modes, recovery effectiveness, cross-module error propagation, and hardware-
algorithm trade-offs observed during deployment. Our results show that explicit partitioning, monitored state transitions, and recovery mechanisms are critical for sustaining executable consistency over extended play, whereas monolithic or unverified pipelines lead to measurable degradation in end-to-end reliability. The proposed system serves as an empirical platform for studying system-level design principles in long-horizon, turn-based interaction.
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All authors Guangyu Zhao, Ceyao Zhang, Chengdong Ma, Tao Wu, Yiyang Song, Haoxuan Ru, Yifan Zhong, Ruilin Yan, Lingfeng Li, Ruochong Li, Yu Li, Xuyuan Han, Yun Ding, Ruizhang Jiang, Xiaochuan Zhang, Yichao Li, Yuanpei Chen, Yaodong Yang, Yitao Liang
Robotics · Cross-listed from cs.CV
LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior
Xinkai Wang, Chenyi Wang, Yifu Xu et al. (+7 more)
We introduce LaMP, a dual-expert
Vision-
Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models regress actions directly from 2D semantic visual features, forcing them to learn complex 3D physical interactions implicitly.This implicit learning strategy degrades under unfamiliar spatial dynamics.LaMP addresses this limitation by aligning a flow-matching Motion Expert with a policy-predicting Action Expert through gated cross-
attention.Specifically, the Motion Expert generates a one-step partially denoised 3D scene flow, and its hidden states condition the Action Expert without full multi-step reconstruction.We evaluate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowX simulation
benchmarks as well as real-world experiments.LaMP consistently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX
benchmarks, achieving the highest reported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an average 9.7% gain over the strongest prior baseline.Our project page is available at https://summerwxk.github.io/lamp-project-page/.
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All authors Xinkai Wang, Chenyi Wang, Yifu Xu, Mingzhe Ye, Fucheng Zhang, Jialin Tian, Xinyu Zhan, Lifeng Zhu, Cewu Lu, Lixin Yang
Robotics
UMBRELLA: Uncertainty-aware Multi-robot Reactive Coordination under Dynamic Temporal Logic Tasks
Qisheng Zhao, Meng Guo, Hengxuan Du et al. (+2 more)
Multi-
robot systems can be extremely efficient for accomplishing team-wise tasks by acting concurrently and collaboratively. However, most existing methods either assume static task features or simply replan when environmental changes occur. This paper addresses the challenging problem of coordinating multi-
robot systems for collaborative tasks involving dynamic and moving targets. We explicitly model the uncertainty in target motion prediction via Conformal Prediction(CP), while respecting the spatial-temporal constraints specified by Linear Temporal Logic (LTL). The proposed framework (UMBRELLA) combines the Monte Carlo Tree Search (MCTS) over partial plans with uncertainty-aware rollouts, and introduces a CP-based metric to guide and accelerate the search. The objective is to minimize the Conditional Value at Risk (CVaR) of the average makespan. For tasks released online, a receding-horizon planning scheme dynamically adjusts the assignments based on updated task specifications and motion predictions. Spatial and temporal constraints among the tasks are always ensured, and only partial synchronization is required for the collaborative tasks during online execution. Extensive large-scale simulations and hardware experiments demonstrate substantial reductions in both the average makespan and its variance by 23% and 71%, compared with static baselines.
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All authors Qisheng Zhao, Meng Guo, Hengxuan Du, Lars Lindemann, Zhongkui Li
Robotics
IntentReact: Guiding Reactive Object-Centric Navigation via Topological Intent
Yanmei Jiao, Anpeng Lu, Wenhan Hu et al. (+4 more)
Object-goal visual
navigation requires
robots to reason over semantic structure and act effectively under partial observability. Recent approaches based on object-level topological maps enable long-horizon
navigation without dense
geometric reconstruction, but their execution remains limited by the gap between global topological guidance and local perception-driven control. In particular, local decisions are made solely from the current
egocentric observation, without access to information beyond the
robot's field of view. As a result, the
robot may persist along its current heading even when initially oriented away from the goal, moving toward directions that do not decrease the global topological distance. In this work, we propose IntentReact, an intent-conditioned object-centric
navigation framework that introduces a compact interface between global topological planning and reactive object-centric control. Our approach encodes global topological guidance as a low-dimensional directional signal, termed intent, which conditions a learned waypoint prediction policy to bias
navigation toward topologically consistent progression. This design enables the
robot to promptly reorient when local observations are misleading, guiding motion toward directions that decrease global topological distance while preserving the reactivity and robustness of object-centric control. We evaluate the proposed framework through extensive experiments, demonstrating improved
navigation success and execution quality compared to prior object-centric
navigation methods.
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All authors Yanmei Jiao, Anpeng Lu, Wenhan Hu, Rong Xiong, Yue Wang, Huajin Tang, Wen-an Zhang
Robotics
Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics
João Castelo-Branco, José Santos-Victor and Alexandre Bernardino
Autonomous object search is challenging for mobile
robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and
navigation efficiency. Classical probabilistic approaches explicitly represent uncertainty but typically rely on handcrafted action-selection heuristics, while deep reinforcement learning enables adaptive policies but often suffers from slow convergence and limited interpretability. This paper proposes a hybrid object-search framework that integrates
Bayesian inference with deep reinforcement learning. The method maintains a spatial belief map over target locations, updated online through
Bayesian inference from calibrated object detections, and trains a reinforcement learning policy to select
navigation actions directly from this probabilistic representation. The approach is evaluated in realistic indoor simulation using Habitat 3.0 and compared against developed baseline strategies. Across two indoor environments, the proposed method improves success rate while reducing search effort. Overall, the results support the value of combining
Bayesian belief estimation with learned action selection to achieve more efficient and reliable objectsearch behavior under partial observability.
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Robotics
Bayesian Learning-Enhanced Navigation with Deep Smoothing for Inertial-Aided Navigation
Nadav Cohen and Itzik Klein
Accurate post-processing
navigation is essential for applications such as survey and mapping, where the full measurement history can be exploited to refine past state estimates. Fixed-interval smoothing
algorithms represent the theoretically optimal solution under Gaussian assumptions. However, loosely coupled INS/GNSS systems fundamentally inherit the systematic position bias of raw GNSS measurements, leaving a persistent accuracy gap that model-based smoothers cannot resolve. To address this limitation, we propose BLENDS, which integrates
Bayesian learning with deep smoothing to enhance
navigation performance. BLENDS is a a data-driven post-processing framework that augments the classical two-filter smoother with a
transformer-based neural network. It learns to modify the filter covariance
matrices and apply an additive correction to the smoothed error-state directly within the
Bayesian framework. A novel
Bayesian-consistent loss jointly supervises the smoothed mean and covariance, enforcing minimum-variance estimates while maintaining statistical consistency. BLENDS is evaluated on two real-world
datasets spanning a mobile
robot and a quadrotor. Across all unseen test trajectories, BLENDS achieves horizontal position improvements of up to 63% over the baseline forward EKF.
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Robotics
SafeGuard ASF: SR Agentic Humanoid Robot System for Autonomous Industrial Safety
Thanh Nguyen Canh, Thang Tran Viet, Thanh Tuan Tran et al. (+1 more)
The rise of unmanned “dark factories” operating without human presence demands autonomous safety systems capable of detecting and responding to multiple hazard types. We present SafeGuard ASF (Agentic Security Fleet), a comprehensive framework deploying
humanoid robots for autonomous hazard detection in industrial environments. Our system integrates multi-modal perception (RGB-D imaging), a ReAct-based agentic
reasoning framework, and learned locomotion policies on the Unitree G1
humanoid platform. We address three critical hazard scenarios: fire and smoke detection, abnormal temperature monitoring in pipelines, and intruder detection in restricted zones. Our perception pipeline achieves 94.2% mAP for fire or smoke detection with 127ms latency. We train multiple locomotion policies, including dance motion tracking and velocity control, using Unitree RL Lab with PPO, demonstrating stable convergence within 80,000 training iterations. We validate our system in both simulation and real-world environments, demonstrating autonomous patrol, human detection with visual perception, and obstacle avoidance capabilities. The proposed ToolOrchestra action framework enables structured decision-making through perception,
reasoning, and actuation tools.
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All authors Thanh Nguyen Canh, Thang Tran Viet, Thanh Tuan Tran, Ben Wei Lim
Robotics
Connectivity-Aware Representations for Constrained Motion Planning via Multi-Scale Contrastive Learning
Suhyun Jeon, Yumin Lim, Woo-Jeong Baek et al. (+3 more)
The objective of constrained motion planning is to connect start and goal configurations while satisfying task-specific constraints. Motion planning becomes inefficient or infeasible when the configurations lie in disconnected regions, known as essentially mutually disconnected (EMD) components. Constraints further restrict feasible space to a lower-dimensional submanifold, while redundancy introduces additional complexity because a single end-effector pose admits infinitely many inverse kinematic solutions that may form discrete self-motion
manifolds. This paper addresses these challenges by learning a connectivity-aware representation for selecting start and goal configurations prior to planning. Joint configurations are embedded into a latent space through multi-scale
manifold learning across neighborhood ranges from local to global, and clustering generates pseudo-labels that supervise a contrastive learning framework. The proposed framework provides a connectivity-aware measure that biases the selection of start and goal configurations in connected regions, avoiding EMDs and yielding higher success rates with reduced planning time. Experiments on various manipulation tasks showed that our method achieves 1.9 times higher success rates and reduces the planning time by a factor of 0.43 compared to baselines.
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All authors Suhyun Jeon, Yumin Lim, Woo-Jeong Baek, Hyeonseo Kim, Suhan Park, Jaeheung Park
Robotics
A Minimum-Energy Control Approach for Redundant Mobile Manipulators in Physical Human-Robot Interaction Applications
Davide Tebaldi, Niccolò Paradisi, Fabio Pini et al. (+1 more)
Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which could not be performed using fixed-base manipulators. Within this context, developing suitable control methodologies is essential since mobile manipulators introduce additional degrees of freedom, making the design of control approaches more challenging and more prone to performance optimization. This paper proposes a control approach for a mobile manipulator, composed of a mobile base equipped with a robotic arm mounted on the top, with the objective of minimizing the overall kinetic energy stored in the whole-body mobile manipulator in physical human-
robot interaction applications. The approach is experimentally tested with reference to a peg-in-hole task, and the results demonstrate that the proposed approach reduces the overall kinetic energy stored in the whole-body robotic system and improves the system performance compared with the
benchmark method.
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All authors Davide Tebaldi, Niccolò Paradisi, Fabio Pini, Luigi Biagiotti
Robotics · Cross-listed from cs.AI
The Competence Shadow: Theory and Bounds of AI Assistance in Safety Engineering
Umair Siddique
As AI assistants become integrated into safety engineering workflows for Physical AI systems, a critical question emerges: does AI assistance improve safety analysis quality, or introduce systematic blind spots that surface only through post-deployment incidents? This paper develops a formal framework for AI assistance in safety analysis. We first establish why safety engineering resists
benchmark-driven evaluation: safety competence is irreducibly multidimensional, constrained by context-dependent correctness, inherent incompleteness, and legitimate expert disagreement. We formalize this through a five-dimensional competence framework capturing domain knowledge, standards expertise, operational experience, contextual understanding, and judgment. We introduce the competence shadow: the systematic narrowing of human
reasoning induced by AI-generated safety analysis. The shadow is not what the AI presents, but what it prevents from being considered. We formalize four canonical human-AI collaboration structures and derive closed-form performance bounds, demonstrating that the competence shadow compounds multiplicatively to produce degradation far exceeding naive additive estimates. The central finding is that AI assistance in safety engineering is a collaboration design problem, not a software procurement decision. The same tool degrades or improves analysis quality depending entirely on how it is used. We derive non-degradation conditions for shadow-resistant workflows and call for a shift from tool qualification toward workflow qualification for trustworthy Physical AI.
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Robotics
Dissimilarity-Based Persistent Coverage Control of Multi-Robot Systems for Improving Solar Irradiance Prediction Accuracy in Solar Thermal Power Plants
Haruki Kawase, Taiga Sugawara and A. Daniel Carnerero
Accurate
forecasting of future
solar irradiance is essential for the effective control of
solar thermal power plants. Although various kriging-based methods have been proposed to address the prediction problem, these methods typically do not provide an appropriate sampling strategy to dynamically position mobile sensors for optimizing prediction accuracy in real time, which is critical for achieving accurate forecasts with a minimal number of sensors. This paper introduces a dissimilarity map derived from a kriging model and proposes a persistent coverage control
algorithm that effectively guides
agents toward regions where additional observations are required to improve prediction performance. By means of experiments using mobile
robots, the proposed approach was shown to obtain more accurate predictions than the considered baselines under various emulated irradiance fields.
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Robotics
CTS-PLL: A Robust and Anytime Framework for Collaborative Task Sequencing and Multi-Agent Path Finding
Junkai Jiang, Yitao Xu, Ruochen Li et al. (+2 more)
The Collaborative Task Sequencing and Multi-
Agent Path Finding (CTS-MAPF) problem requires
agents to accomplish sequences of tasks while avoiding collisions, posing significant challenges due to its combinatorial complexity. This work introduces CTS-PLL, a hierarchical framework that extends the configuration-based CTS-MAPF planning paradigm with two key enhancements: a lock
agents detection and release mechanism leveraging a complete planning method for local re-planning, and an anytime refinement procedure based on Large Neighborhood Search (LNS). These additions ensure robustness in dense environments and enable continuous improvement of solution quality. Extensive evaluations across sparse and dense
benchmarks demonstrate that CTS-PLL achieves higher success rates and solution quality compared with existing methods, while maintaining competitive runtime efficiency. Real-world
robot experiments further demonstrate the feasibility of the approach in practice.
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All authors Junkai Jiang, Yitao Xu, Ruochen Li, Shaobing Xu, Jianqiang Wang
Robotics
ThermoAct:Thermal-Aware Vision-Language-Action Models for Robotic Perception and Decision-Making
Young-Chae Son, Dae-Kwan Ko, Yoon-Ji Choi et al. (+1 more)
In recent human-
robot collaboration environments, there is a growing focus on integrating diverse sensor data beyond visual information to enable safer and more intelligent task execution. Although thermal data can be crucial for enhancing
robot safety and operational efficiency, its integration has been relatively overlooked in prior research. This paper proposes a novel
Vision-
Language-Action (VLA) framework that incorporates thermal information for
robot task execution. The proposed system leverages a
Vision-
Language Model (VLM) as a high-level planner to interpret complex natural
language commands and decompose them into simpler sub-tasks. This approach facilitates efficient data collection and robust
reasoning for complex operations. Unlike conventional methods that rely solely on visual data, our approach integrates thermal information, enabling the
robot to perceive physical properties and proactively ensure environmental safety. Experimental results from real-world task scenarios validate the feasibility of our proposed framework, suggesting its potential to enhance task success rates and safety compared to existing
vision-based systems.Project page: https://youngchae912.github.io/ThermoAct/
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All authors Young-Chae Son, Dae-Kwan Ko, Yoon-Ji Choi, Soo-Chul Lim
Robotics
$\pi$, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation
Johnathan Tucker, Denis Liu, Aiden Swann et al. (+7 more)
Vision-
Language-Action (VLA) models such as $\pi_0$ have demonstrated remarkable generalization across diverse fixed-base manipulators. However, transferring these foundation models to aerial platforms remains an open challenge due to the fundamental mismatch between the quasi-static dynamics of fixed-base arms and the underactuated, highly dynamic nature of flight. In this work, we introduce AirVLA, a system that investigates the transferability of manipulation-pretrained VLAs to aerial pick-and-place tasks. We find that while visual representations transfer effectively, the specific control dynamics required for flight do not. To bridge this "dynamics gap" without retraining the foundation model, we introduce a Payload-Aware Guidance mechanism that injects payload constraints directly into the policy's flow-matching sampling process. To overcome data scarcity, we further utilize a Gaussian Splatting pipeline to synthesize
navigation training data. We evaluate our method through a cumulative 460 real-world experiments which demonstrate that this synthetic data is a key enabler of performance, unlocking 100% success in
navigation tasks where directly fine-tuning on teleoperation data alone attains 81% success. Our inference-time intervention, Payload-Aware Guidance, increases real-world pick-and-place task success from 23% to 50%. Finally, we evaluate the model on a long-horizon compositional task, achieving a 62% overall success rate. These results suggest that pre-trained manipulation VLAs, with appropriate data augmentation and physics-informed guidance, can transfer to aerial manipulation and
navigation, as well as the composition of these tasks.
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All authors Johnathan Tucker, Denis Liu, Aiden Swann, Allen Ren, Javier Yu, Jiankai Sun, Brandon Kim, Lachlain McGranahan, Quan Vuong, Mac Schwager
Robotics
Learning Rollout from Sampling:An R1-Style Tokenized Traffic Simulation Model
Ziyan Wang, Peng Chen, Ding Li et al. (+4 more)
Learning diverse and high-fidelity traffic simulations from human driving demonstrations is crucial for autonomous driving evaluation. The recent next-
token prediction (NTP) paradigm, widely adopted in large
language models (LLMs), has been applied to traffic simulation and achieves iterative improvements via supervised fine-tuning (SFT). However, such methods limit active exploration of potentially valuable motion
tokens, particularly in suboptimal regions.
Entropy patterns provide a promising perspective for enabling exploration driven by motion
token uncertainty. Motivated by this insight, we propose a novel tokenized traffic simulation policy, R1Sim, which represents an initial attempt to explore reinforcement learning based on motion
token entropy patterns, and systematically analyzes the impact of different motion
tokens on simulation outcomes. Specifically, we introduce an
entropy-guided adaptive sampling mechanism that focuses on previously overlooked motion
tokens with high uncertainty yet high potential. We further optimize motion behaviors using Group Relative Policy Optimization (GRPO), guided by a safety-aware reward design. Overall, these components enable a balanced exploration-exploitation trade-off through diverse high-uncertainty sampling and group-wise comparative estimation, resulting in realistic, safe, and diverse multi-
agent behaviors. Extensive experiments on the Waymo Sim
Agent benchmark demonstrate that R1Sim achieves competitive performance compared to state-of-the-art methods.
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All authors Ziyan Wang, Peng Chen, Ding Li, Chiwei Li, Qichao Zhang, Zhongpu Xia, Guizhen Yu
Robotics
Wireless bioelectronic control architectures for biohybrid robotic systems
Hiroyuki Tetsuka and Minoru Hirano
Wireless bioelectronic interfaces are increasingly used to control tissue-engineered biohybrid robotic systems. However, a unifying engineering framework linking device design to system-level control remains underdeveloped. Here, we propose that wireless control in biohybrid robotics can be formulated as a coupled co-design problem of integrating signal delivery, spatial selectivity, scalability, and interface stability. We analyze three representative control strategies, wireless electrical stimulation, wireless optoelectronic stimulation, and neuromuscular integration, which operates within a distinct regime with characteristic trade-offs. Across these modalities, the tissue-device interface emerges as a key constraint, governing the interplay between electromagnetic coupling,
circuit performance, and biomechanical response. Based on this framework, we outline practical design principles spanning electromagnetic field distribution,
circuit architecture, and actuator mechanics. We further propose a transition from open-loop stimulation to closed-loop biohybrid autonomy enabled by organoid-integrated bioelectronics and bidirectional microelectrode interfaces. This work establishes a system-level perspective on wireless bioelectronic control and provides design guidelines for developing stable, scalable, and autonomous biohybrid robotic systems.
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Robotics
SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models
Xiyang Wu, Guangyao Shi, Qingzi Wang et al. (+3 more)
Vision-
language-action (VLA) models enable
robots to follow natural-
language instructions grounded in visual observations, but the instruction channel also introduces a critical vulnerability: small textual perturbations can alter downstream
robot behavior. Systematic robustness evaluation therefore requires a black-box attacker that can generate minimal yet effective instruction edits across diverse VLA models. To this end, we present SABER, an
agent-centric approach for automatically generating instruction-based
adversarial attacks on VLA models under bounded edit budgets. SABER uses a GRPO-trained ReAct attacker to generate small, plausible
adversarial instruction edits using character-,
token-, and
prompt-level tools under a bounded edit budget that induces targeted behavioral degradation, including task failure, unnecessarily long execution, and increased constraint violations. On the LIBERO
benchmark across six state-of-the-art VLA models, SABER reduces task success by 20.6%, increases action-sequence length by 55%, and raises constraint violations by 33%, while requiring 21.1% fewer tool calls and 54.7% fewer character edits than strong GPT-based baselines. These results show that small, plausible instruction edits are sufficient to substantially degrade
robot execution, and that an agentic black-box pipeline offers a practical, scalable, and adaptive approach for red-teaming robotic foundation models. The codebase is publicly available at https://github.com/wuxiyang1996/SABER.
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All authors Xiyang Wu, Guangyao Shi, Qingzi Wang, Zongxia Li, Amrit Singh Bedi, Dinesh Manocha
Robotics
COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
Yifeng Zhang, Jieming Chen, Tingguang Zhou et al. (+4 more)
Multi-
Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency in future intelligent transportation systems. Multi-
Agent Reinforcement Learning (MARL) has emerged as a promising approach for developing advanced end-to-end MASD systems. However, achieving efficient and safe collaboration in dynamic MASD systems remains a significant challenge in dense scenarios with complex
agent interactions. To address this challenge, we propose a novel collaborative(CO-) interaction-aware(-IN) MARL framework, named COIN. Specifically, we develop a new counterfactual individual-global twin delayed deep deterministic policy gradient (CIG-TD3)
algorithm, crafted in a "centralized training, decentralized execution" (CTDE) manner, which aims to jointly optimize the individual objectives (
navigation) and the global objectives (collaboration) of
agents. We further introduce a dual-level interaction-aware centralized critic architecture that captures both local pairwise interactions and global system-level dependencies, enabling more accurate global value estimation and improved credit assignment for collaborative policy learning. We conduct extensive simulation experiments in dense urban traffic environments, which demonstrate that COIN consistently outperforms other advanced baseline methods in both safety and efficiency across various system sizes. These results highlight its superiority in complex and dynamic MASD scenarios, as further validated through real-world
robot demonstrations. Supplementary
videos are available at https://marmotlab.github.io/COIN/
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All authors Yifeng Zhang, Jieming Chen, Tingguang Zhou, Tanishq Duhan, Jianghong Dong, Yuhong Cao, Guillaume Sartoretti
Robotics
CROSS: A Mixture-of-Experts Reinforcement Learning Framework for Generalizable Large-Scale Traffic Signal Control
Xibei Chen, Yifeng Zhang, Yuxiang Xiao et al. (+3 more)
Recent advances in robotics, automation, and artificial intelligence have enabled urban traffic systems to operate with increasing autonomy towards future smart cities, powered in part by the development of adaptive traffic signal control (ATSC), which dynamically optimizes signal phases to mitigate congestion and optimize traffic. However, achieving effective and generalizable large-scale ATSC remains a significant challenge due to the diverse intersection
topologies and highly dynamic, complex traffic demand patterns across the network. Existing RL-based methods typically use a single shared policy for all scenarios, whose limited representational capacity makes it difficult to capture diverse traffic dynamics and generalize to unseen environments. To address these challenges, we propose CROSS, a novel Mixture-of-Experts (MoE)-based decentralized RL framework for generalizable ATSC. We first introduce a Predictive Contrastive Clustering (PCC) module that forecasts short-term state transitions to identify latent traffic patterns, followed by clustering and contrastive learning to enhance pattern-level representation. We further design a Scenario-Adaptive MoE module that augments a shared policy with multiple experts, thus enabling adaptive specialization and more flexible scenario-specific strategies. We conduct extensive experiments in the SUMO simulator on both synthetic and real-world traffic
datasets. Compared with state-of-the-art baselines, CROSS achieves superior performance and generalization through improved representation of diverse traffic scenarios.
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All authors Xibei Chen, Yifeng Zhang, Yuxiang Xiao, Mingfeng Fan, Maonan Wang, Guillaume Sartoretti
Robotics
Integrated Multi-Drone Task Allocation, Sequencing, and Optimal Trajectory Generation in Obstacle-Rich 3D Environments
Yunes Alqudsi and Murat Makaraci
Coordinating teams of aerial
robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which
robot serves which goals and in what order – with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-Drone Task Allocation and Path Planning), an end-to-end framework that jointly addresses multi-goal allocation, tour sequencing, and safe trajectory generation for quadrotor teams operating in obstacle-rich spaces. IMD–TAPP first discretizes the workspace into a 3D
navigation graph and computes obstacle-aware
robot-to-goal and goal-to-goal travel costs via graph-search-based pathfinding. These costs are then embedded within an Injected Particle Swarm Optimization (IPSO) scheme, guided by multiple linear assignment, to efficiently explore coupled assignment/ordering alternatives and to minimize mission makespan. Finally, the resulting waypoint tours are transformed into time-parameterized minimum-snap trajectories through a generation-and-optimization routine equipped with iterative validation of obstacle clearance and inter-
robot separation, triggering re-planning when safety margins are violated. Extensive MATLAB simulations across cluttered 3D scenarios demonstrate that IMD–TAPP consistently produces dynamically feasible, collision-free trajectories while achieving competitive completion times. In a representative case study with two drones serving multiple goals, the proposed approach attains a minimum mission time of 136~s while maintaining the required safety constraints throughout execution.
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