Robotics · Cross-listed from cs.CV
BRIC: Bridging Kinematic Plans and Physical Control at Test Time
Dohun Lim, Minji Kim, Jaewoon Lim et al. (+1 more)
We propose BRIC, a novel test-time adaptation (TTA) framework that enables long-term human motion generation by resolving execution discrepancies between
diffusion-based kinematic motion planners and reinforcement learning-based physics controllers. While
diffusion models can generate diverse and expressive motions conditioned on text and scene context, they often produce physically implausible outputs, leading to execution drift during simulation. To address this, BRIC dynamically adapts the physics controller to noisy motion plans at test time, while preserving pre-trained skills via a loss function that mitigates catastrophic forgetting. In addition, BRIC introduces a lightweight test-time guidance mechanism that steers the
diffusion model in the signal space without updating its parameters. By combining both adaptation strategies, BRIC ensures consistent and physically plausible long-term executions across diverse environments in an effective and efficient manner. We validate the effectiveness of BRIC on a variety of long-term tasks, including motion composition, obstacle avoidance, and human-scene interaction, achieving state-of-the-art performance across all tasks.
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All authors Dohun Lim, Minji Kim, Jaewoon Lim, Sungchan Kim
Robotics · Cross-listed from cs.AI
VibraVerse: A Large-Scale Geometry-Acoustics Alignment Dataset for Physically-Consistent Multimodal Learning
Bo Pang, Chenxi Xu, Jierui Ren et al. (+2 more)
Understanding the physical world requires perceptual models grounded in physical laws rather than mere statistical correlations. However, existing
multimodal learning frameworks, focused on
vision and
language, lack physical consistency and overlook the intrinsic causal relationships among an object's geometry, material, vibration modes, and the sounds it produces. We introduce VibraVerse, a large-scale geometry-acoustics
alignment dataset that explicitly bridges the causal chain from 3D geometry -> physical attributes -> modal parameters -> acoustic signals. Each 3D model has explicit physical properties (density, Young's modulus, Poisson's ratio) and volumetric geometry, from which modal eigenfrequencies and eigenvectors are computed for impact sound synthesis under controlled excitations. To establish this coherence, we introduce CLASP, a contrastive learning framework for cross-modal
alignment that preserves the causal correspondence between an object's physical structure and its acoustic response. This framework enforces physically consistent
alignment across modalities, ensuring that every sample is coherent, traceable to the governing equations, and embedded within a unified representation space spanning shape, image, and sound. Built upon VibraVerse, we define a suite of
benchmark tasks for geometry-to-sound prediction, sound-guided shape reconstruction, and cross-modal representation learning. Extensive validations on these tasks demonstrate that models trained on VibraVerse exhibit superior accuracy, interpretability, and generalization across modalities. These results establish VibraVerse as a
benchmark for physically consistent and causally interpretable
multimodal learning, providing a foundation for sound-guided
embodied perception and a deeper understanding of the physical world. The
dataset will be open-sourced.
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All authors Bo Pang, Chenxi Xu, Jierui Ren, Guoping Wang, Sheng Li
Robotics
Improved adaptive wind driven optimization algorithm for real-time path planning
Shiqian Liu, Azlan Mohd Zain and Le-le Mao
Recently, path planning has achieved remarkable progress in enhancing global search capability and convergence accuracy through heuristic and learning-inspired optimization frameworks. However, real-time adaptability in dynamic environments remains a critical challenge for autonomous
navigation, particularly when
robots must generate collision-free, smooth, and efficient trajectories under complex constraints. By analyzing the difficulties in dynamic path planning, the Wind Driven Optimization (WDO)
algorithm emerges as a promising framework owing to its physically interpretable search dynamics. Motivated by these observations, this work revisits the WDO principle and proposes a variant formulation, Multi-hierarchical adaptive wind driven optimization(MAWDO), that improves adaptability and robustness in time-varying environments. To mitigate instability and premature convergence, a hierarchical-guidance mechanism divides the population into multiple groups guided by individual, regional, and global leaders to balance exploration and exploitation. Extensive evaluations on sixteen
benchmark functions show that MAWDO achieves superior optimization accuracy, convergence stability, and adaptability over state-of-the art metaheuristics. In dynamic path planning, MAWDO shortens the path length to 469.28 pixels, improving over Multi-strategy ensemble wind driven optimization(MEWDO), Adaptive wind driven optimization(AWDO) and WDO by 3.51%, 11.63% and 14.93%, and achieves the smallest optimality gap (1.01) with smoothness 0.71 versus 13.50 and 15.67 for AWDO and WDO, leading to smoother, shorter, and collision-free trajectories that confirm its effectiveness for real-time path planning in complex environments.
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Robotics
Quality-guided UAV Surface Exploration for 3D Reconstruction
Benjamin Sportich, Kenza Boubakri, Olivier Simonin et al. (+1 more)
Reasons for mapping an unknown environment with autonomous
robots are wide-ranging, but in practice, they are often overlooked when developing planning strategies. Rapid information gathering and comprehensive structural assessment of buildings have different requirements and therefore necessitate distinct methodologies. In this paper, we propose a novel modular Next-Best-View (NBV) planning framework for aerial
robots that explicitly uses a reconstruction quality objective to guide the exploration planning. In particular, our approach introduces new and efficient methods for view generation and selection of viewpoint candidates that are adaptive to the user-defined quality requirements, fully exploiting the uncertainty encoded in a Truncated Signed Distance field (TSDF) representation of the environment. This results in informed and efficient exploration decisions tailored towards the predetermined objective. Finally, we validate our method via extensive simulations in realistic environments. We demonstrate that it successfully adjusts its behavior to the user goal while consistently outperforming conventional NBV strategies in terms of coverage, quality of the final 3D map and path efficiency.
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All authors Benjamin Sportich, Kenza Boubakri, Olivier Simonin, Alessandro Renzaglia
Robotics · Cross-listed from cs.CV
Material-informed Gaussian Splatting for 3D World Reconstruction in a Digital Twin
Andy Huynh, João Malheiro Silva, Holger Caesar et al. (+1 more)
3D reconstruction for Digital Twins often relies on
LiDAR-based methods, which provide accurate geometry but lack the semantics and textures naturally captured by cameras. Traditional
LiDAR-camera fusion approaches require complex calibration and still struggle with certain materials like glass, which are visible in images but poorly represented in point clouds. We propose a camera-only pipeline that reconstructs scenes using 3D Gaussian Splatting from multi-view images, extracts semantic material masks via
vision models, converts Gaussian representations to mesh surfaces with projected material labels, and assigns physics-based material properties for accurate sensor simulation in modern graphics engines and simulators. This approach combines photorealistic reconstruction with physics-based material assignment, providing sensor simulation fidelity comparable to
LiDAR-camera fusion while eliminating hardware complexity and calibration requirements. We validate our camera-only method using an internal
dataset from an instrumented test vehicle, leveraging
LiDAR as ground truth for reflectivity validation alongside image similarity metrics.
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All authors Andy Huynh, João Malheiro Silva, Holger Caesar, Tong Duy Son
Robotics
ArtiBench and ArtiBrain: Benchmarking Generalizable Vision-Language Articulated Object Manipulation
Yuhan Wu, Tiantian Wei, Shuo Wang et al. (+4 more)
Interactive articulated manipulation requires long-horizon, multi-step interactions with appliances while maintaining physical consistency. Existing
vision-
language and
diffusion-based policies struggle to generalize across parts, instances, and categories. We first introduce ArtiBench, a five-level
benchmark covering kitchen, storage, office, and tool environments. ArtiBench enables structured evaluation from cross-part and cross-instance variation to long-horizon multi-object tasks, revealing the core generalization challenges of articulated object manipulation. Building on this
benchmark, we propose ArtiBrain, a modular framework that unifies high-level
reasoning with adaptive low-level control. ArtiBrain uses a VLM-based Task Reasoner (GPT-4.1) to decompose and validate subgoals, and employs a Hybrid Controller that combines geometry-aware keyframe execution with affordance-guided
diffusion for precise and interpretable manipulation. An Affordance
Memory Bank continually accumulates successful execution episodes and propagates part-level actionable affordances to unseen articulated parts and configurations. Extensive experiments on ArtiBench show that our ArtiBrain significantly outperforms state-of-the-art
multimodal and
diffusion-based methods in robustness and generalization. Code and
dataset will be released upon acceptance.
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All authors Yuhan Wu, Tiantian Wei, Shuo Wang, ZhiChao Wang, Yanyong Zhang, Daniel Cremers, Yan Xia
Robotics
How Robot Kinematics Influence Human Performance in Virtual Robot-to-Human Handover Tasks
Róisín Keenan and Joost C. Dessing
Recent advancements in robotics have increased the possibilities for integrating robotic systems into human-involved workplaces, highlighting the need to examine and optimize human-
robot coordination in collaborative settings. This study explores human-
robot interactions during handover tasks using Virtual Reality (VR) to investigate differences in human motor performance across various task dynamics and
robot kinematics. A VR-based
robot handover simulation afforded safe and controlled assessments of human-
robot interactions. In separate experiments, four potential influences on human performance were examined (1) control over task initiation and
robot movement synchrony (temporal and spatiotemporal); (2) partner appearance (human versus robotic); (3)
robot velocity profiles (minimum jerk, constant velocity, constant acceleration, and biphasic); and (4) the timing of rotational object motion. Findings across experiments emphasize humans benefit from
robots providing early and salient visual information about task-relevant object motion, and advantages of human-like smooth
robot trajectories. To varying degrees, these manipulations improved predictive accuracy and synchronization during interaction. This suggests that human-
robot interactions should be designed to allow humans to leverage their natural capabilities for detecting biological motion, which conversely may reduce the need for costly robotic computations or added cognitive adaptation on the human side.
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Robotics
Dynamic-ICP: Doppler-Aware Iterative Closest Point Registration for Dynamic Scenes
Dong Wang, Daniel Casado Herraez, Stefan May et al. (+1 more)
Reliable odometry in highly dynamic environments remains challenging when it relies on ICP-based registration: ICP assumes near-static scenes and degrades in repetitive or low-texture geometry. We introduce Dynamic-ICP, a Doppler-aware registration framework. The method (i) estimates ego motion from per-point Doppler velocity via robust regression and builds a velocity filter, (ii) clusters dynamic objects and reconstructs object-wise translational velocities from ego-compensated radial measurements, (iii) predicts dynamic points with a constant-velocity model, and (iv) aligns scans using a compact objective that combines point-to-plane geometry residual with a translation-invariant, rotation-only Doppler residual. The approach requires no external sensors or sensor-vehicle calibration and operates directly on FMCW
LiDAR range and Doppler velocities. We evaluate Dynamic-ICP on three
datasets-HeRCULES, HeLiPR, AevaScenes-focusing on highly dynamic scenes. Dynamic-ICP consistently improves rotational stability and translation accuracy over the state-of-the-art methods. Our approach is also simple to integrate into existing pipelines, runs in real time, and provides a lightweight solution for robust registration in dynamic environments. To encourage further research, the code is available at: https://github.com/JMUWRobotics/Dynamic-ICP.
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All authors Dong Wang, Daniel Casado Herraez, Stefan May, Andreas Nüchter
Robotics
HAFO: A Force-Adaptive Control Framework for Humanoid Robots in Intense Interaction Environments
Chenhui Dong, Haozhe Xu, Wenhao Feng et al. (+4 more)
Reinforcement learning (RL) controllers have made impressive progress in
humanoid locomotion and light-weight object manipulation. However, achieving robust and precise motion control with intense force interaction remains a significant challenge. To address these limitations, this paper proposes HAFO, a dual-
agent reinforcement learning framework that concurrently optimizes both a robust locomotion strategy and a precise upper-body manipulation strategy via coupled training. We employ a constrained residual action space to improve dual-
agent training stability and sample efficiency. The external tension disturbances are explicitly modeled using a spring-damper system, allowing for fine-grained force control through manipulation of the virtual spring. In this process, the reinforcement learning policy autonomously generates a disturbance-rejection response by utilizing environmental feedback. The experimental results demonstrate that HAFO achieves whole-body control for
humanoid robots across diverse force-interaction environments using a single dual-
agent policy, delivering outstanding performance under load-bearing and thrust-disturbance conditions, while maintaining stable operation even under rope suspension state.
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All authors Chenhui Dong, Haozhe Xu, Wenhao Feng, Zhipeng Wang, Yanmin Zhou, Yifei Zhao, Bin He
Robotics
Toward generic control for soft robotic systems
Yu Sun, Yaosheng Deng, Wenjie Mei et al. (+9 more)
Soft robotics has advanced rapidly, yet its control methods remain fragmented: different morphologies and actuation schemes still require task-specific controllers, hindering theoretical integration and large-scale deployment. A generic control framework is therefore essential, and a key obstacle lies in the persistent use of rigid-body control logic, which relies on precise models and strict low-level execution. Such a paradigm is effective for rigid
robots but fails for soft
robots, where the ability to tolerate and exploit approximate action representations, i.e., control compliance, is the basis of robustness and adaptability rather than a disturbance to be eliminated. Control should thus shift from suppressing compliance to explicitly exploiting it. Human motor control exemplifies this principle: instead of computing exact dynamics or issuing detailed muscle-level commands, it expresses intention through high-level movement tendencies, while reflexes and biomechanical mechanisms autonomously resolve local details. This architecture enables robustness, flexibility, and cross-task generalization. Motivated by this insight, we propose a generic soft-
robot control framework grounded in control compliance and validate it across
robots with diverse morphologies and actuation mechanisms. The results demonstrate stable, safe, and cross-platform transferable behavior, indicating that embracing control compliance, rather than resisting it, may provide a widely applicable foundation for unified soft-
robot control.
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All authors Yu Sun, Yaosheng Deng, Wenjie Mei, Xiaogang Xiong, Yang Bai, Masaki Ogura, Zeyu Zhou, Mir Feroskhan, Michael Yu Wang, Qiyang Zuo, Yao Li, Yunjiang Lou
Robotics · Cross-listed from cs.AI
CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents
Haebin Seong, Sungmin Kim, Yongjun Cho et al. (+20 more)
Current
navigation benchmarks focus on task success but do not capture the economic constraints essential for commercializing autonomous delivery systems. We introduce CostNav, an Economic
Navigation Benchmark that evaluates physical AI
agents on a cost-revenue and break-even analysis, pairing Isaac Sim's collision and cargo dynamics with industry-standard data such as Securities and Exchange Commission (SEC) filings and Abbreviated Injury Scale (AIS) injury reports. To our knowledge, CostNav is the first physics-grounded economic
benchmark to use regulatory and financial data to quantify the gap between
navigation metrics and commercial deployment, revealing that high task-success rates alone do not ensure economic viability. Evaluating seven baselines (two rule-based and five imitation-learning methods), we find no method economically viable: all yield negative contribution margins. CANVAS, using only an RGB camera and GPS, attains the highest task success and the least-negative margin among methods with non-zero Service-Level Agreement (SLA) compliance (-\$28.40/run), outperforming LiDAR-equipped Nav2 w/ GPS (-\$37.34/run). A sim-trained policy evaluated on a real delivery
robot yields SLA compliance close to its simulation result, indicating that policy performance in CostNav's simulation transfers to real-world deployment. We challenge the community to achieve economic viability on CostNav, which scores methods by cost-revenue outcomes. All resources are available at https://github.com/worv-ai/CostNav.
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All authors Haebin Seong, Sungmin Kim, Yongjun Cho, Myunchul Joe, Geunwoo Kim, Yubeen Park, Sunhoo Kim, Samwoo Seong, Yoonshik Kim, Suhwan Choi, Jaeyoon Jung, Jiyong Youn, Jinmyung Kwak, Sunghee Ahn, Jaemin Lee, Younggil Do, Seungyeop Yi, Woojin Cheong, Minhyeok Oh, Minchan Kim, Seongjae Kang, Youngjae Yu, Yunsung Lee
Robotics
Hibikino-Musashi@Home 2025 Team Description Paper
Ryohei Kobayashi, Kosei Isomoto, Kosei Yamao et al. (+19 more)
This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team developed a
dataset generator for training a
robot vision system and an open-source development environment running on a Human Support
Robot simulator. The large-
language-model-powered task planner selects appropriate primitive skills to perform the task requested by the user. Moreover, the team has focused on research involving
brain-inspired
memory models for adaptation to individual home environments. This approach aims to provide intuitive and personalized assistance. Additionally, the team contributed to the reusability of the
navigation system developed by Pumas in RoboCup2024. The team aimed to design a home service
robot to assist humans in their homes and continuously attend competitions to evaluate and improve the developed system.
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All authors Ryohei Kobayashi, Kosei Isomoto, Kosei Yamao, Soma Fumoto, Koshun Arimura, Naoki Yamaguchi, Akinobu Mizutani, Tomoya Shiba, Kouki Kimizuka, Yuta Ohno, Ryo Terashima, Hiromasa Yamaguchi, Tomoaki Fujino, Ryoga Maruno, Wataru Yoshimura, Kazuhito Mine, Tang Phu Thien Nhan, Yuga Yano, Yuichiro Tanaka, Takeshi Nishida, Takashi Morie, Hakaru Tamukoh
Robotics · Cross-listed from cs.CV
Map-World: Masked Action planning and Path-Integral World Model for Autonomous Driving
Bin Hu, Zijian Lu, Haicheng Liao et al. (+7 more)
Motion planning for autonomous driving must handle multiple plausible futures while remaining computationally efficient. Recent end-to-end systems and world-model-based planners predict rich multi-modal trajectories, but typically rely on handcrafted anchors or reinforcement learning to select a single best mode for training and control. This selection discards information about alternative futures and complicates optimization. We propose MAP-World, a prior-free multi-modal planning framework that couples masked action planning with a path-weighted world model. The Masked Action Planning (MAP) module treats future ego motion as masked sequence completion: past waypoints are encoded as visible
tokens, future waypoints are represented as mask
tokens, and a driving-intent path provides a coarse scaffold. A compact latent planning state is expanded into multiple trajectory queries with injected noise, yielding diverse, temporally consistent modes without anchor libraries or teacher policies. A lightweight world model then rolls out future BEV semantics conditioned on each candidate trajectory. During training, semantic losses are computed as an expectation over modes, using trajectory probabilities as discrete path weights, so the planner learns from the full distribution of plausible futures instead of a single selected path. On NAVSIM, our method matches anchor-based approaches and achieves state-of-the-art performance among world-model-based methods, while avoiding reinforcement learning and maintaining real-time inference latency.
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All authors Bin Hu, Zijian Lu, Haicheng Liao, Chengran Yuan, Bin Rao, Yongkang Li, Guofa Li, Zhiyong Cui, Cheng-zhong Xu, Zhenning Li
Robotics
Active3D: Active High-Fidelity 3D Reconstruction via Hierarchical Uncertainty Quantification
Yan Li, Yingzhao Li and Gim Hee Lee
In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. We introduce a hybrid implicit-explicit representation that fuses neural fields with Gaussian primitives to jointly capture global structural priors and locally observed details. Based on this hybrid state, we derive a hierarchical uncertainty volume that quantifies both implicit global structure quality and explicit local surface confidence. To focus optimization on the most informative regions, we propose an uncertainty-driven keyframe selection strategy that anchors high-
entropy viewpoints as sparse
attention nodes, coupled with a viewpoint-space sliding window for uncertainty-aware local refinement. The planning module formulates next-best-view selection as an Expected Hybrid Information Gain problem and incorporates a risk-sensitive path planner to ensure efficient and safe exploration. Extensive experiments on challenging
benchmarks demonstrate that our approach consistently achieves state-of-the-art accuracy, completeness, and rendering quality, highlighting its effectiveness for real-world active reconstruction and robotic perception tasks.
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Robotics
ShapeForce: Low-Cost Soft Robotic Wrist for Contact-Rich Manipulation
Jinxuan Zhu, Zihao Yan, Yangyu Xiao et al. (+5 more)
Contact feedback is essential for contact-rich robotic manipulation, as it allows the
robot to detect subtle interaction changes and adjust its actions accordingly. Six-axis force-torque sensors are commonly used to obtain contact feedback, but their high cost and fragility have discouraged many researchers from adopting them in contact-rich tasks. To offer a more cost-efficient and easy-accessible source of contact feedback, we present ShapeForce, a low-cost, plug-and-play soft wrist that provides force-like signals for contact-rich robotic manipulation. Inspired by how humans rely on relative force changes in contact rather than precise force magnitudes, ShapeForce converts external force and torque into measurable deformations of its compliant core, which are then estimated via marker-based pose tracking and converted into force-like signals. Our design eliminates the need for calibration or specialized electronics to obtain exact values, and instead focuses on capturing force and torque changes sufficient for enabling contact-rich manipulation. Extensive experiments across diverse contact-rich tasks and manipulation policies demonstrate that ShapeForce delivers performance comparable to six-axis force-torque sensors at an extremely low cost.
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All authors Jinxuan Zhu, Zihao Yan, Yangyu Xiao, Jingxiang Guo, Chenrui Tie, Xinyi Cao, Yuhang Zheng, Lin Shao
Robotics
Collaborate sim and real: Robot Bin Packing Learning in Real-world and Physical Engine
Lidi Zhang, Han Wu, Liyu Zhang et al. (+6 more)
The 3D bin packing problem, with its diverse industrial applications, has garnered significant research
attention in recent years. Existing approaches typically model it as a discrete and static process, while real-world applications involve continuous
gravity-driven interactions. This idealized simplification leads to infeasible deployments (e.g., unstable packing) in practice. Simulations with physical engine offer an opportunity to emulate continuous
gravity effects, enabling the training of reinforcement learning (RL)
agents to address such limitations and improve packing stability. However, a simulation-to-reality gap persists due to dynamic variations in physical properties of real-world objects, such as various friction coefficients, elasticity, and non-uniform weight distributions. To bridge this gap, we propose a hybrid RL framework that collaborates with physical simulation with real-world data feedback. Firstly, domain randomization is applied during simulation to expose
agents to a spectrum of physical parameters, enhancing their generalization capability. Secondly, the RL
agent is fine-tuned with real-world deployment feedback, further reducing collapse rates. Extensive experiments demonstrate that our method achieves lower collapse rates in both simulated and real-world scenarios. Large-scale deployments in logistics systems validate the practical effectiveness, with a 35% reduction in packing collapse compared to baseline methods.
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All authors Lidi Zhang, Han Wu, Liyu Zhang, Ruofeng Liu, Haotian Wang, Chao Li, Desheng Zhang, Yunhuai Liu, Tian He
Robotics
CoC-VLA: Delving into Adversarial Domain Transfer for Explainable Autonomous Driving via Chain-of-Causality Visual-Language-Action Model
Dapeng Zhang, Fei Shen, Rui Zhao et al. (+5 more)
Autonomous driving represents a prominent application of artificial intelligence. Recent approaches have shifted from focusing solely on common scenarios to addressing complex, long-tail situations such as subtle human behaviors, traffic accidents, and non-compliant driving patterns. Given the demonstrated capabilities of large
language models (LLMs) in understanding visual and natural
language inputs and following instructions, recent methods have integrated LLMs into autonomous driving systems to enhance
reasoning, interpretability, and performance across diverse scenarios. However, existing methods typically rely either on real-world data, which is suitable for industrial deployment, or on simulation data tailored to rare or hard case scenarios. Few approaches effectively integrate the complementary advantages of both data sources. To address this limitation, we propose a novel VLM-guided, end-to-end
adversarial transfer framework for autonomous driving that transfers long-tail handling capabilities from simulation to real-world deployment, named CoC-VLA. The framework comprises a teacher VLM model, a student VLM model, and a discriminator. Both the teacher and student VLM models utilize a shared base architecture, termed the Chain-of-Causality Visual-
Language Model (CoC VLM), which integrates temporal information via an end-to-end text adapter. This architecture supports chain-of-thought
reasoning to infer complex driving logic. The teacher and student VLM models are pre-trained separately on simulated and real-world
datasets. The discriminator is trained adversarially to facilitate the transfer of long-tail handling capabilities from simulated to real-world environments by the student VLM model, using a novel backpropagation strategy.
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All authors Dapeng Zhang, Fei Shen, Rui Zhao, Yinda Chen, Peng Zhi, Chenyang Li, Rui Zhou, Qingguo Zhou
Robotics · Cross-listed from cs.CV
Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen et al. (+5 more)
Vision-
Language-Action (VLA) models have recently shown strong decision-making capabilities in autonomous driving. However, existing VLAs often struggle with achieving efficient inference and generalizing to novel autonomous vehicle configurations and driving scenarios. In this paper, we propose
Reasoning-VLA, a general and fast action-generation VLA framework. The proposed model employs a set of learnable action queries, initialized via Gaussian sampling from ground-truth trajectories within the training corpus. These learnable queries interact with
reasoning-enhanced
vision-
language features to generate continuous action trajectories in parallel. To promote robust generalization, we consolidate eight publicly available autonomous driving
datasets into a standardized, Chain-of-Thought
reasoning-based, and easy-to-use data format for model training. Leveraging both supervised learning and reinforcement learning fine-tuning, extensive empirical evaluations across multiple
benchmarks demonstrate that
Reasoning-VLA achieves state-of-the-art performance, superior generalization capability, and the excellent inference speed reported to date.
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All authors Dapeng Zhang, Zhenlong Yuan, Zhangquan Chen, Chih-Ting Liao, Yinda Chen, Fei Shen, Qingguo Zhou, Tat-Seng Chua
Robotics · Cross-listed from cs.DC
Improved Linear-Time Construction of Minimal Dominating Set via Mobile Agents
Prabhat Kumar Chand and Anisur Rahaman Molla
Mobile
agents have emerged as a powerful framework for solving fundamental graph problems in distributed settings in recent times. These
agents, modelled as autonomous physical or software entities, possess local computation power, finite
memory and have the ability to traverse a graph, offering efficient solutions to a range of classical problems. In this work, we focus on the problem of computing a minimal dominating set (mDS) in anonymous graphs using mobile
agents. Building on the recently proposed optimal dispersion
algorithm on the synchronous mobile
agent model, we design two new
algorithms that achieve a linear-time solution for this problem in the synchronous setting. Specifically, given a connected $n$-node graph with $n$
agents initially placed in either rooted or arbitrary configurations, we show that an mDS can be computed in $O(n)$ rounds using only $O(\log n)$ bits of
memory per
agent, without using any prior knowledge of any global parameters. This improves upon the best-known complexity results in the literature over the same model. In addition, as natural by-products of our methodology, our
algorithms also construct a spanning tree and elect a unique leader in $O(n)$ rounds, which are also important results of independent interest in the mobile-
agent framework.
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Robotics · Cross-listed from cs.CV
MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization
Chengyue Huang, Mellon M. Zhang, Robert Azarcon et al. (+2 more)
Vision-
Language-Action (VLA) models inherit strong priors from pretrained
Vision-
Language Models (VLMs), but naive fine-tuning often disrupts these representations and harms generalization. Existing fixes – freezing modules or applying uniform regularization – either overconstrain adaptation or ignore the differing roles of VLA components. We present MAPS (Module-Wise Proximity Scheduling), the first robust fine-tuning framework for VLAs. Through systematic analysis, we uncover an empirical order in which proximity constraints should be relaxed to balance stability and flexibility. MAPS linearly schedules this relaxation, enabling visual encoders to stay close to their pretrained priors while action-oriented
language layers adapt more freely. MAPS introduces no additional parameters or data, and can be seamlessly integrated into existing VLAs. Across MiniVLA-VQ, MiniVLA-OFT, OpenVLA-OFT, and challenging
benchmarks such as SimplerEnv, CALVIN, LIBERO, as well as real-world evaluations on the Franka Emika Panda platform, MAPS consistently boosts both in-distribution and out-of-distribution performance (up to +30%). Our findings highlight empirically guided proximity to pretrained VLMs as a simple yet powerful principle for preserving broad generalization in VLM-to-VLA transfer.
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All authors Chengyue Huang, Mellon M. Zhang, Robert Azarcon, Glen Chou, Zsolt Kira
Robotics
Human-Centered Cooperative Control Coupling Autonomous and Haptic Shared Control via Control Barrier Function
Eito Sato and Takahiro Wada
Haptic shared control (HSC) is effective in teleoperation when full autonomy is limited by uncertainty or sensing constraints. However, autonomous control performance achieved by maximizing HSC strength is limited because the dynamics of the joystick and human arm affect the
robot's behavior. We propose a cooperative framework coupling a joystick-independent autonomous controller with HSC. A control barrier function ignores joystick inputs within a safe region determined by the human operator in real-time, while HSC is engaged otherwise. A pilot experiment on simulated tasks with tele-operated
underwater robot in virtual environment demonstrated improved accuracy and reduced required time over conventional HSC.
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Robotics · Cross-listed from cs.CV
GigaWorld-0: World Models as Data Engine to Empower Embodied AI
GigaWorld Team, Angen Ye, Boyuan Wang et al. (+22 more)
World models are emerging as a foundational paradigm for scalable, data-efficient
embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explicitly as a data engine for
Vision-
Language-Action (VLA) learning. GigaWorld-0 integrates two synergistic components: GigaWorld-0-
Video, which leverages large-scale
video generation to produce diverse, texture-rich, and temporally coherent
embodied sequences under fine-grained control of appearance, camera viewpoint, and action semantics; and GigaWorld-0-3D, which combines 3D generative modeling, 3D Gaussian Splatting reconstruction, physically differentiable system identification, and executable motion planning to ensure
geometric consistency and physical realism. Their joint optimization enables the scalable synthesis of
embodied interaction data that is visually compelling, spatially coherent, physically plausible, and instruction-aligned. Training at scale is made feasible through our efficient GigaTrain framework, which exploits FP8-precision and sparse
attention to drastically reduce
memory and compute requirements. We conduct comprehensive evaluations showing that GigaWorld-0 generates high-quality, diverse, and controllable data across multiple dimensions. Critically, VLA model (e.g., GigaBrain-0) trained on GigaWorld-0-generated data achieve strong real-world performance, significantly improving generalization and task success on physical
robots without any real-world interaction during training.
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All authors GigaWorld Team, Angen Ye, Boyuan Wang, Chaojun Ni, Guan Huang, Guosheng Zhao, Haoyun Li, Jiagang Zhu, Kerui Li, Mengyuan Xu, Qiuping Deng, Siting Wang, Wenkang Qin, Xinze Chen, Xiaofeng Wang, Yankai Wang, Yu Cao, Yifan Chang, Yuan Xu, Yun Ye, Yang Wang, Yukun Zhou, Zhengyuan Zhang, Zhehao Dong, Zheng Zhu
Robotics
Unifying Perception and Action: A Hybrid-Modality Pipeline with Implicit Visual Chain-of-Thought for Robotic Action Generation
Xiangkai Ma, Lekai Xing, Han Zhang et al. (+2 more)
Vision-
Language-Action (VLA) models built upon Chain-of-Thought (CoT) have achieved remarkable success in advancing general-purpose robotic
agents, owing to its significant perceptual comprehension. Recently, since text-only CoT struggles to adequately capture scene details in complex spatial environments, a highly promising strategy involves leveraging visual priors to guide robotic action generation. Nevertheless, these strategies face two inherent challenges: (i) a modality gap between visual observations and low-level actions, and (ii) unstable training due to competing objectives between visual prediction and action generation. To address these challenges, we propose a
Vision-Integrated Trajectory
Alignment (VITA) framework that learns a shared discrete latent space for
vision and action, enabling joint modeling of perception and motor control. VITA introduces a implicit visual CoT: autoregressively generated
tokens is simultaneously decoded into future frames predictions and
robot actions, thereby internalizing visual dynamics as an inductive bias for motion planning. Extensive experiments on simulated and real-world environments demonstrate state-of-the-art performance. VITA improves 14.5%, 9.6% and 12.1% over existing baselines on CALVIN, LIBERO and SimplerEnv. Furthermore, VITA attains an average success rate of 80.5% across six real-world tasks, demonstrating its potential as a generalist robotic manipulation model.
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All authors Xiangkai Ma, Lekai Xing, Han Zhang, Wenzhong Li, Sanglu Lu