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Computer Science Robotics

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Wednesday 15 April

Robotics

Self-adaptive Multi-Access Edge Architectures: A Robotics Case

Mahyar T Moghaddam, Joakim Leed and Anders Frandsen

The growth of compute-intensive AI tasks highlights the need to mitigate the processing costs and improve performance and energy efficiency. This necessitates the integration of intelligent agents as architectural adaptation supervisors tasked with adaptive scaling of the infrastructure and efficient offloading of computation within the continuum. This paper presents a self-adaptation approach for an efficient computing system of a mixed human-robot environment. The computation task is associated with a Neural Network algorithm that leverages sensory data to predict human mobility behaviors, to enhance mobile robots' proactive path planning, and ensure human safety. To streamline neural network processing, we built a distributed edge offloading system with heterogeneous processing units, orchestrated by Kubernetes. By monitoring response times and power consumption, the MAPE-K-based adaptation supervisor makes informed decisions on scaling and offloading. Results show notable improvements in service quality over traditional setups, demonstrating the effectiveness of the proposed approach for AI-driven systems.
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Robotics

Evolvable Embodied Agent for Robotic Manipulation via Long Short-Term Reflection and Optimization

Jianzong Wang, Botao Zhao, Yayun He et al. (+2 more)

Achieving general-purpose robotics requires empowering robots to adapt and evolve based on their environment and feedback. Traditional methods face limitations such as extensive training requirements, difficulties in cross-task generalization, and lack of interpretability. Prompt learning offers new opportunities for self-evolving robots without extensive training, but simply reflecting on past experiences. However, extracting meaningful insights from task successes and failures remains a challenge. To this end, we propose the evolvable embodied agent (EEAgent) framework, which leverages large vision-language models (VLMs) for better environmental interpretation and policy planning. To enhance reflection on past experiences, we propose a long short-term reflective optimization (LSTRO) mechanism that dynamically refines prompts based on both past experiences and newly learned lessons, facilitating continuous self-evolution, thereby enhancing overall task success rates. Evaluations on six VIMA-Bench tasks reveal that our approach sets a new state-of-the-art, notably outperforming baselines in complex scenarios.
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All authors Jianzong Wang, Botao Zhao, Yayun He, Junqing Peng, Xulong Zhang

Robotics

Stability Principle Underlying Passive Dynamic Walking of Rimless Wheel

Fumihiko Asano

Rimless wheels are known as the simplest model for passive dynamic walking. It is known that the passive gait generated only by gravity effect always becomes asymptotically stable and 1-period because a rimless wheel automatically achieves the two necessary conditions for guaranteeing the asymptotic stability; one is the constraint on impact posture and the other is the constraint on restored mechanical energy. The asymptotic stability is then easily shown by the recurrence formula of kinetic energy. There is room, however, for further research into the inherent stability principle. In this paper, we reconsider the stability of the stance phase based on the linearization of the equation of motion, and investigate the relation between the stability and energy conservation law. Through the mathematical analysis, we provide a greater understanding of the inherent stability principle.
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Robotics

A transformable slender microrobot inspired by nematode parasites for interventional endovascular surgery

Xin Yang, Dongliang Fan, Yunteng Ma et al. (+5 more)

Cardiovascular diseases account for around 17.9 million deaths per year globally, the treatment of which is challenging considering the confined space and complex topology of the vascular network and high risks during operations. Robots, although promising, still face the dilemma of possessing versatility or maneuverability after decades of development. Inspired by nematodes, the parasites living, feeding, and moving in the human body's vascular system, this work develops a transformable slender magnetic microrobot. Based on the experiments and analyses, we optimize the fabrication and geometry of the robot and finally create a slender prototype with an aspect ratio larger than 100 (smaller than 200 microns in diameter and longer than 20 mm in length), which possesses uniformly distributed magnetic beads on the body of an ultrathin polymer string and a big bead on the head. This prototype shows great flexibility (largest curvature 0.904 mm-1) and locomotion capability (the maximum speed: 125 mm/s). Moreover, the nematode-inspired robot can pass through sharp turns with a radius of 0.84 mm and holes distributed in three-dimensional (3D) space. We also display the potential application in interventional surgery of the microrobot by navigating it through a narrow blood vessel mold to wrap and transport a drug (95 times heavier than the robot) by deforming the robot's slender body and releasing the drug to the aim position finally. Moreover, the robot also demonstrates the possible applications in embolization by transforming and winding itself into an aneurysms phantom and exhibits its outstanding injectability by being successfully withdrawn and injected through a medical needle (diameter: 1.2 mm) of a syringe.
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All authors Xin Yang, Dongliang Fan, Yunteng Ma, Yuxuan Liao, Diancheng Li, U Kei Cheang, Bo Peng, Hongqiang Wang

Robotics · Cross-listed from cs.LG

Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning

Shentong Mo

Designing effective reward functions is a cornerstone of reinforcement learning (RL), yet it remains a challenging and labor-intensive process due to the inefficiencies and inconsistencies inherent in traditional methods. Existing methods often rely on extensive manual design and evaluation steps, which are prone to redundancy and overlook local uncertainties at intermediate decision points. To address these challenges, we propose the Chain of Uncertain Rewards (CoUR), a novel framework that integrates large language models (LLMs) to streamline reward function design and evaluation in RL environments. Specifically, our CoUR introduces code uncertainty quantification with a similarity selection mechanism that combines textual and semantic analyses to identify and reuse the most relevant reward function components. By reducing redundant evaluations and leveraging Bayesian optimization on decoupled reward terms, CoUR enables a more efficient and robust search for optimal reward feedback. We comprehensively evaluate CoUR across nine original environments from IsaacGym and all 20 tasks from the Bidexterous Manipulation benchmark. The experimental results demonstrate that CoUR not only achieves better performance but also significantly lowers the cost of reward evaluations.
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Robotics

RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment

Pou-Chun Kung, Yuan Tian, Zhengqin Li et al. (+4 more)

Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM). However, most radar SLAM pipelines still rely heavily on frame-to-frame odometry, which leads to substantial drift. While loop closure can correct long-term errors, it requires revisiting places and relies on robust place recognition. In contrast, visual odometry methods typically leverage bundle adjustment (BA) to jointly optimize poses and map within a local window. However, an equivalent BA formulation for radar has remained largely unexplored. We present the first radar BA framework enabled by Gaussian Splatting (GS), a dense and differentiable scene representation. Our method jointly optimizes radar sensor poses and scene geometry using full range-azimuth-Doppler data, bringing the benefits of multi-frame BA to radar for the first time. When integrated with an existing radar-inertial odometry frontend, our approach significantly reduces pose drift and improves robustness. Across multiple indoor scenes, our radar BA achieves substantial gains over the prior radar-inertial odometry, reducing average absolute translational and rotational errors by 90% and 80%, respectively.
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All authors Pou-Chun Kung, Yuan Tian, Zhengqin Li, Yue Liu, Eric Whitmire, Wolf Kienzle, Hrvoje Benko

Robotics

RobotPan: A 360$^\circ$ Surround-View Robotic Vision System for Embodied Perception

Jiahao Ma, Qiang Zhang, Peiran Liu et al. (+11 more)

Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings such as teleoperation, data collection, and emergency takeover. However, current robotic visual interfaces are often limited to narrow forward-facing views, or, when multiple on-board cameras are available, require cumbersome manual switching that interrupts the operator's workflow. Both configurations suffer from motion-induced jitter that causes simulator sickness in head-mounted displays. We introduce a surround-view robotic vision system that combines six cameras with LiDAR to provide full 360$^\circ$ visual coverage, while meeting the geometric and real-time constraints of embodied deployment. We further present RobotPan, a feed-forward framework that predicts metric-scaled and compact 3D Gaussians from calibrated sparse-view inputs for real-time rendering, reconstruction, and streaming. RobotPan lifts multi-view features into a unified spherical coordinate representation and decodes Gaussians using hierarchical spherical voxel priors, allocating fine resolution near the robot and coarser resolution at larger radii to reduce computational redundancy without sacrificing fidelity. To support long sequences, our online fusion updates dynamic content while preventing unbounded growth in static regions by selectively updating appearance. Finally, we release a multi-sensor dataset tailored to 360$^\circ$ novel view synthesis and metric 3D reconstruction for robotics, covering navigation, manipulation, and locomotion on real platforms. Experiments show that RobotPan achieves competitive quality against prior feed-forward reconstruction and view-synthesis methods while producing substantially fewer Gaussians, enabling practical real-time embodied deployment.
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All authors Jiahao Ma, Qiang Zhang, Peiran Liu, Zeran Su, Pihai Sun, Gang Han, Wen Zhao, Wei Cui, Zhang Zhang, Zhiyuan Xu, Renjing Xu, Jian Tang, Miaomiao Liu, Yijie Guo

Robotics

Robust Energy-Aware Routing for Air-Ground Cooperative Multi-UAV Delivery in Wind-Uncertain Environments

Tianshun Li, Hongliang Lu, Yanggang Sheng et al. (+3 more)

Ensuring energy feasibility under wind uncertainty is critical for the safety and reliability of UAV delivery missions. In realistic truck-drone logistics systems, UAVs must deliver parcels and safely return under time-varying wind conditions that are only partially observable during flight. However, most existing routing approaches assume static or deterministic energy models, making them unreliable in dynamic wind environments. We propose Battery-Efficient Routing (BER), an online risk-sensitive planning framework for wind-sensitive truck-assisted UAV delivery. The problem is formulated as routing on a time dependent energy graph whose edge costs evolve according to wind-induced aerodynamic effects. BER continuously evaluates return feasibility while balancing instantaneous energy expenditure and uncertainty-aware risk. The approach is embedded in a hierarchical aerial-ground delivery architecture that combines task allocation, routing, and decentralized trajectory execution. Extensive simulations on synthetic ER graphs generated in Unreal Engine environments and quasi-real wind logs demonstrate that BER significantly improves mission success rates and reduces wind-induced failures compared with static and greedy baselines. These results highlight the importance of integrating real-time energy budgeting and environmental awareness for UAV delivery planning under dynamic wind conditions.
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All authors Tianshun Li, Hongliang Lu, Yanggang Sheng, Zhongzhen Wang, Haoang Li, Xinhu Zheng

Robotics

Singularity Avoidance in Inverse Kinematics: A Unified Treatment of Classical and Learning-based Methods

Vishnu Rudrasamudram and Hariharasudan Malaichamee

Singular configurations cause loss of task-space mobility, unbounded joint velocities, and solver divergence in inverse kinematics (IK) for serial manipulators. No existing survey bridges classical singularity-robust IK with rapidly growing learning-based approaches. We provide a unified treatment spanning Jacobian regularization, Riemannian manipulability tracking, constrained optimization, and modern data-driven paradigms. A systematic taxonomy classifies methods by retained geometric structure and robustness guarantees (formal vs. empirical). We address a critical evaluation gap by proposing a benchmarking protocol and presenting experimental results: 12 IK solvers are evaluated on the Franka Panda under position-only IK across four complementary panels measuring error degradation by condition number, velocity amplification, out-of-distribution robustness, and computational cost. Results show that pure learning methods fail even on well-conditioned targets (MLP: 0% success, approx. 10 mm mean error), while hybrid warm-start architectures - IKFlow (59% to 100%), CycleIK(0% to 98.6%), GGIK (0% to 100%) - rescue learned solvers via classical refinement, with DLS converging from initial errors up to 207 mm. Deeper singularity-regime evaluation is identified as immediate future work.
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Robotics · Cross-listed from cs.LG

Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics

Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin et al. (+2 more)

Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate system identification as an in-context meta-learning problem and compare deterministic and generative sequence models for forward dynamics prediction. We take a Transformer-based meta-model, as a strong deterministic baseline, and introduce to this setting two complementary diffusion-based approaches: (i) inpainting diffusion (Diffuser), which learns the joint input-observation distribution, and (ii) conditioned diffusion models (CNN and Transformer), which generate future observations conditioned on control inputs. Through large-scale randomized simulations, we analyze performance across in-distribution and out-of-distribution regimes, as well as computational trade-offs relevant for control. We show that diffusion models significantly improve robustness under distribution shift, with inpainting diffusion achieving the best performance in our experiments. Finally, we demonstrate that warm-started sampling enables diffusion models to operate within real-time constraints, making them viable for control applications. These results highlight generative meta-models as a promising direction for robust system identification in robotics.
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All authors Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin, Asad Ali Shahid, Loris Roveda

Tuesday 14 April

Robotics · Cross-listed from physics.soc-ph

On Common Misconceptions in Classical Vehicle Dynamics

Massimo Guiggiani

Classical vehicle dynamics contains several widely adopted misconceptions that, while intuitively appealing, may lead to inconsistencies when examined under a rigorous mechanical framework. This paper revisits a number of such misconceptions, clarifying their domains of validity and highlighting potential sources of misunderstanding. The aim is not to dismiss established models, but to promote a more precise and scientifically grounded interpretation of key concepts.
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Robotics

Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle

James Dallas, Thomas Lew, John Talbot et al. (+3 more)

Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation, which can be difficult to efficiently sample in complex, high-dimensional systems. In this work, we characterize trajectories that barely avoid safety violations using the Pontryagin Maximum Principle. These boundary trajectories are used to guide data collection for learned Hamilton-Jacobi Reachability, concentrating learning efforts near safety-critical states to improve efficiency. The learned Control Barrier Value Function is then used directly for safety filtering. Simulations and experimental validation on a shared-control automotive racing application demonstrate PMP sampling improves learning efficiency, yielding faster convergence, reduced failure rates, and improved safe set reconstruction, with wall times around 3ms.
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All authors James Dallas, Thomas Lew, John Talbot, Jonathan DeCastro, Somil Bansal, John Subosits

Robotics

Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control

Shrutheesh R Iyer, I-Chia Chang, Andrew Z. Liu et al. (+2 more)

Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation. Our work is available at https://commalab.org/papers/mcvamp/.
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All authors Shrutheesh R Iyer, I-Chia Chang, Andrew Z. Liu, Yan Gu, Zachary Kingston

Robotics

Utilizing Inpainting for Keypoint Detection for Vision-Based Control of Robotic Manipulators

Sreejani Chatterjee, Venkatesh Mullur, Abhinav Gandhi et al. (+1 more)

We present a novel visual servoing framework for controlling a robotic manipulator in configuration space using only natural visual features. To train our data-driven keypoint detector, we attach ArUco markers along the robot body, use their centers as keypoint labels, and apply image inpainting to remove the markers and reconstruct the occluded regions. This produces automatically labeled, markerless robot images without requiring accurate camera calibration or robot models. At runtime, a second inpainting model reconstructs robot regions that are partially occluded, enabling continuous keypoint detection. An Unscented Kalman Filter (UKF) further improves temporal consistency and robustness of the keypoint estimates. We demonstrate successful model-free, vision-based control using natural robot features under both full visibility and partial occlusion. To show broader applicability, we also extend the perception pipeline to two-module and three-module soft origami arms and qualitatively evaluate keypoint detection and temporal tracking on these platforms.
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All authors Sreejani Chatterjee, Venkatesh Mullur, Abhinav Gandhi, Berk Calli

Robotics

GeoVision-Enabled Digital Twin for Hybrid Autonomous-Teleoperated Medical Responses

Parham Kebria, Soheil Sabri and Laura J Brattain

Remote medical response systems are increasingly being deployed to support emergency care in disaster-affected and infrastructure-limited environments. Enabled by GeoVision capabilities, this paper presents a Digital Twin architecture for hybrid autonomous-teleoperated medical response systems. The proposed framework integrates perception and adaptive navigation with a Digital Twin, synchronized in real-time, that mirrors system states, environmental dynamics, patient conditions, and mission objectives. Unlike traditional ground control interfaces, the Digital Twin provides remote clinical and operational users with an intuitive, continuously updated virtual representation of the platform and its operational context, enabling enhanced situational awareness and informed decision-making.
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Robotics

Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation

Joonkyung Kim, Yanze Zhang, Wenhao Luo et al. (+1 more)

Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination in heterogeneous robot teams. We derive a canonical second-order control-affine representation that unifies holonomic and nonholonomic robots under acceleration-level control via canonical transformation and backstepping, preserving forward invariance of the safe set while avoiding relative-degree mismatch across heterogeneous dynamics. We further introduce a support-function-based directional capability metric that quantifies each robot's ability to follow its motion intent, deriving a pairwise responsibility allocation that distributes the safety burden proportionally to each robot's motion capability. A feasibility-aware clipping mechanism further constrains the allocation to each agent's physically achievable range, mitigating infeasible constraint assignments common in dense decentralized CBF settings. Simulations with up to 30 heterogeneous robots and a physical multi-robot demonstration show improved safety and task efficiency over baselines, validating real-world applicability across robots with distinct kinematic constraints.
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All authors Joonkyung Kim, Yanze Zhang, Wenhao Luo, Yiwei Lyu

Robotics · Cross-listed from cs.CV

4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview

Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič et al. (+52 more)

The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy and embedded real-time feasibility. This report summarizes the MaCVi 2026 challenge setup, evaluation protocols, datasets, and benchmark tracks, and presents quantitative results, qualitative comparisons, and cross-challenge analyses of emerging method trends. We also include technical reports from top-performing teams to highlight practical design choices and lessons learned across the benchmark suite. Datasets, leaderboards, and challenge resources are available at https://macvi.org/workshop/cvpr26.
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All authors Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič, Mingi Jeong, Arnold Wiliem, Janez Pers, Matej Kristan, Alberto Quattrini Li, Matija Teršek, Josip Šarić, Arpita Vats, Dominik Hildebrand, Rafia Rahim, Mahmut Karaaslan, Arpit Vaishya, Steve Xie, Ersin Kaya, Akib Mashrur, Tze-Hsiang Tang, Chun-Ming Tsai, Jun-Wei Hsieh, Ming-Ching Chang, Wonwoo Jo, Doyeon Lee, Yusi Cao, Lingling Li, Vinayak Nageli, Arshad Jamal, Gorthi Rama Krishna Sai Subrahmanyam, Jemo Maeng, Seongju Lee, Kyoobin Lee, Xu Liu, LiCheng Jiao, Jannik Sheikh, Martin Weinmann, Ivan Martinović, Jose Mateus Raitz Persch, Rahul Harsha Cheppally, Mehmet E. Belviranli, Dimitris Gahtidis, Hyewon Chun, Sangmun Lee, Philipp Gorczak, Hansol Kim, Jeeyeon Jeon, Borja Carrillo Perez, Jiahui Wang, Sangmin Park, Andreas Michel, Jannick Kuester, Bettina Felten, Wolfgang Gross, Yuan Feng, Justin Davis

Robotics

Weakly-supervised Learning for Physics-informed Neural Motion Planning via Sparse Roadmap

Ruiqi Ni, Yuchen Liu and Ahmed H. Qureshi

The motion planning problem requires finding a collision-free path between start and goal configurations in high-dimensional, cluttered spaces. Recent learning-based methods offer promising solutions, with self-supervised physics-informed approaches such as Neural Time Fields (NTFields) solving the Eikonal equation to learn value functions without expert demonstrations. However, existing physics-informed methods struggle to scale in complex, multi-room environments, where simply increasing the number of samples cannot resolve local minima or guarantee global consistency. We propose Hierarchical Neural Time Fields (H-NTFields), a weakly-supervised framework that combines weak supervision from sparse roadmaps with physics-informed PDE regularization. The roadmap provides global topological anchors through upper and lower bounds on travel times, while PDE losses enforce local geometric fidelity and obstacle-aware propagation. Experiments on 18 Gibson environments and real robotic platforms show that H-NTFields substantially improves robustness over prior physics-informed methods, while enabling fast amortized inference through a continuous value representation.
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Robotics · Cross-listed from eess.SY

Synthesis and Deployment of Maximal Robust Control Barrier Functions through Adversarial Reinforcement Learning

Donggeon David Oh, Duy P. Nguyen, Haimin Hu et al. (+1 more)

Robust control barrier functions (CBFs) provide a principled mechanism for smooth safety enforcement under worst-case disturbances. However, existing approaches typically rely on explicit, closed-form structure in the dynamics (e.g., control-affine) and uncertainty models. This has led to limited scalability and generality, with most robust CBFs certifying only conservative subsets of the maximal robust safe set. In this paper, we introduce a new robust CBF framework for general nonlinear systems under bounded uncertainty. We first show that the safety value function solving the dynamic programming Isaacs equation is a valid robust discrete-time CBF that enforces safety on the maximal robust safe set. We then adopt the key reinforcement learning (RL) notion of quality function (or Q-function), which removes the need for explicit dynamics by lifting the barrier certificate into state-action space and yields a novel robust Q-CBF constraint for safety filtering. Combined with adversarial RL, this enables the synthesis and deployment of robust Q-CBFs on general nonlinear systems with black-box dynamics and unknown uncertainty structure. We validate the framework on a canonical inverted pendulum benchmark and a 36-D quadruped simulator, achieving substantially less conservative safe sets than barrier-based baselines on the pendulum and reliable safety enforcement even under adversarial uncertainty realizations on the quadruped.
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All authors Donggeon David Oh, Duy P. Nguyen, Haimin Hu, Jaime Fernández Fisac

Robotics

Multi-modal panoramic 3D outdoor datasets for place categorization

Hojung Jung, Yuki Oto, Oscar M. Mozos et al. (+2 more)

We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse).
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All authors Hojung Jung, Yuki Oto, Oscar M. Mozos, Yumi Iwashita, Ryo Kurazume

Robotics

Learning Versatile Humanoid Manipulation with Touch Dreaming

Yaru Niu, Zhenlong Fang, Binghong Chen et al. (+8 more)

Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stability, end-effector dexterity, and contact-aware interaction under frequent contact changes. In this work, we study dexterous, contact-rich humanoid loco-manipulation. We first develop an RL-based lower-body controller that serves as the stability backbone for whole-body execution during complex manipulation. Building on this controller, we develop a VR-based whole-body humanoid data collection system that integrates dexterous hands and tactile sensing for contact-rich manipulation. We then propose Humanoid Transformer with Touch Dreaming (HTD), a multimodal encoder-decoder Transformer that models touch as a core modality alongside multi-view vision and proprioception. HTD is trained in a single stage with behavioral cloning augmented by touch dreaming: in addition to predicting action chunks, the policy predicts future hand-joint forces and future tactile latents, with tactile-latent targets provided by an exponential moving average target encoder without requiring a separate tactile pretraining stage. This encourages the policy to learn contact-aware representations for dexterous manipulation. Across five real-world contact-rich tasks, HTD achieves a 90.9% relative improvement in average success rate over the stronger baseline for each task. Ablation results further show that latent-space tactile prediction is more effective than raw tactile prediction, yielding a 30% relative gain in success rate. These results demonstrate that our touch-dreaming-enhanced learning system enables versatile, high-dexterity humanoid manipulation in the real world. More information and open-source materials are available at humanoid-touch-dream.github.io.
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All authors Yaru Niu, Zhenlong Fang, Binghong Chen, Shuai Zhou, Revanth Krishna Senthilkumaran, Hao Zhang, Bingqing Chen, Chen Qiu, H. Eric Tseng, Jonathan Francis, Ding Zhao

Robotics

XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios

James Wang, Primo Pu, Zephyr Fung et al. (+19 more)

The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot manipulation. Although robot-free human demonstrations (e.g., the UMI paradigm) offer a scalable alternative to traditional teleoperation, current systems are constrained by sub-optimal hardware ergonomics, open-loop workflows, and a lack of systematic data-mixing strategies. To address these limitations, we present XRZero-G0, a hardware-software co-designed system for embodied data collection and policy learning. The system features an ergonomic, virtual reality interface equipped with a top-view camera and dual specialized grippers to directly improve collection efficiency. To ensure dataset reliability, we propose a closed-loop collection, inspection, training, and evaluation pipeline for non-proprioceptive data. This workflow achieves an 85% data validity rate and establishes a transparent mechanism for quality control. Furthermore, we investigate the empirical scaling behaviors and optimal mixing ratios of robot-free data. Extensive experiments indicate that combining a minimal volume of real-robot data with large-scale robot-free data (e.g., a 10:1 ratio) achieves performance comparable to exclusively real-robot datasets, while reducing acquisition costs by a factor of twenty. Utilizing XRZero-G0, we construct a 2,000-hour robot-free dataset that enables zero-shot cross-embodiment transfer to a target physical robot, demonstrating a highly scalable methodology for generalized real-world manipulation.Our project repository: https://github.com/X-Square-Robot/XRZero-G0
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All authors James Wang, Primo Pu, Zephyr Fung, Alex Wang, Sam Wang, Bender Deng, Kevin Wang, Zivid Liu, Chris Pan, Panda Yang, Andy Zhai, Lucy Liang, Shalfun Li, Johnny Sun, Jacky Xu, Will Tian, Kai Yan, Kohler Ye, Scott Li, Qian Wang, Roy Gan, Hao Wang

Robotics

RMGS-SLAM: Real-time Multi-sensor Gaussian Splatting SLAM

Dongen Li, Yi Liu, Junqi Liu et al. (+8 more)

Achieving real-time Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian splatting (3DGS) in large-scale real-world environments remains challenging, as existing methods still struggle to jointly achieve low-latency pose estimation, continuous 3D Gaussian reconstruction, and long-term global consistency. In this paper, we present a tightly coupled LiDAR-Inertial-Visual 3DGS-based SLAM framework for real-time pose estimation and photorealistic mapping in large-scale real-world scenes. The system executes state estimation and 3D Gaussian primitive initialization in parallel with global Gaussian optimization, enabling continuous dense mapping. To improve Gaussian initialization quality and accelerate optimization convergence, we introduce a cascaded strategy that combines feed-forward predictions with geometric priors derived from voxel-based principal component analysis. To enhance global consistency, we perform loop closure directly on the optimized global Gaussian map by estimating loop constraints through Gaussian-based Generalized Iterative Closest Point registration, followed by pose-graph optimization. We also collect challenging large-scale looped outdoor sequences with hardware-synchronized LiDAR-camera-IMU and ground-truth trajectories for realistic evaluation. Extensive experiments on both public datasets and our dataset demonstrate that the proposed method achieves a state of the art among real-time efficiency, localization accuracy, and rendering quality across diverse real-world scenes.
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All authors Dongen Li, Yi Liu, Junqi Liu, Zewen Sun, Zefan Huang, Shuo Sun, Jiahui Liu, Chengran Yuan, Hongliang Guo, Francis E.H. Tay, Marcelo H. Ang Jr

Robotics

DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Coding

Yuhan Jin, Nayari Marie Lessa, Mariela De Lucas Alvarez et al. (+4 more)

Marine ecosystem degradation necessitates continuous, scientifically selective underwater monitoring. However, most autonomous underwater vehicles (AUVs) operate as passive data loggers, capturing exhaustive video for offline review and frequently missing transient events of high scientific value. Transitioning to active perception requires a causal, online signal that highlights significant phenomena while suppressing maneuver-induced visual changes. We propose DINO-Explorer, a novelty-aware perception framework driven by a continuous semantic surprise signal. Operating within the latent space of a frozen DINOv3 foundation model, it leverages a lightweight, action-conditioned recurrent predictor to anticipate short-horizon semantic evolution. An efference-copy-inspired module utilizes globally pooled optical flow to discount self-induced visual changes without suppressing genuine environmental novelty. We evaluate this signal on the downstream task of asynchronous event triage under variant telemetry constraints. Results demonstrate that DINO-Explorer provides a robust, bandwidth-efficient attention mechanism. At a fixed operating point, the system retains 78.8% of post-discovery human-reviewer consensus events with a 56.8% trigger confirmation rate, effectively surfacing mission-relevant phenomena. Crucially, ego-motion conditioning suppresses 45.5% of false positives relative to an uncompensated surprise signal baseline. In a replay-side Pareto ablation study, DINO-Explorer robustly dominates the validated peak F1 versus telemetry bandwidth frontier, reducing telemetry bandwidth by 48.2% at the selected operating point while maintaining a 62.2% peak F1 score, successfully concentrating data transmission around human-verified novelty events.
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All authors Yuhan Jin, Nayari Marie Lessa, Mariela De Lucas Alvarez, Melvin Laux, Lucas Amparo Barbosa, Frank Kirchner, Rebecca Adam

Robotics

E2E-Fly: An Integrated Training-to-Deployment System for End-to-End Quadrotor Autonomy

Fangyu Sun, Fanxing Li, Linzuo Zhang et al. (+5 more)

Training and transferring learning-based policies for quadrotors from simulation to reality remains challenging due to inefficient visual rendering, physical modeling inaccuracies, unmodeled sensor discrepancies, and the absence of a unified platform integrating differentiable physics learning into end-to-end training. While recent work has demonstrated various end-to-end quadrotor control tasks, few systems provide a systematic, zero-shot transfer pipeline, hindering reproducibility and real-world deployment. To bridge this gap, we introduce E2E-Fly, an integrated framework featuring an agile quadrotor platform coupled with a full-stack training, validation, and deployment workflow. The training framework incorporates a high-performance simulator with support for differentiable physics learning and reinforcement learning, alongside structured reward design tailored to common quadrotor tasks. We further introduce a two-stage validation strategy using sim-to-sim transfer and hardware-in-the-loop testing, and deploy policies onto two physical quadrotor platforms via a dedicated low-level control interface and a comprehensive sim-to-real alignment methodology, encompassing system identification, domain randomization, latency compensation, and noise modeling. To the best of our knowledge, this is the first work to systematically unify differentiable physical learning with training, validation, and real-world deployment for quadrotors. Finally, we demonstrate the effectiveness of our framework for training six end-to-end control tasks and deploy them in the real world.
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All authors Fangyu Sun, Fanxing Li, Linzuo Zhang, Yu Hu, Renbiao Jin, Shuyu Wu, Wenxian Yu, Danping Zou

Robotics

Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots

Yifei Yan and Linqi Ye

As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become a key challenge in embodied intelligence. Existing approaches either rely on complex topology adjustments in Mixture-of-Experts (MoE) models or require training extremely large-scale models, making lightweight deployment difficult. To address this, we propose Tree Learning, a multi-skill continual learning framework for humanoid robots. The framework adopts a root-branch hierarchical parameter inheritance mechanism, providing motion priors for branch skills through parameter reuse to fundamentally prevent catastrophic forgetting. A multi-modal feedforward adaptation mechanism combining phase modulation and interpolation is designed to support both periodic and aperiodic motions. A task-level reward shaping strategy is also proposed to accelerate skill convergence. Unity-based simulation experiments show that, in contrast to simultaneous multi-task training, Tree Learning achieves higher rewards across various representative locomotion skills while maintaining a 100% skill retention rate, enabling seamless multi-skill switching and real-time interactive control. We further validate the performance and generalization capability of Tree Learning on two distinct Unity-simulated tasks: a Super Mario-inspired interactive scenario and autonomous navigation in a classical Chinese garden environment.
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Robotics

Robotic Manipulation is Vision-to-Geometry Mapping: Vision-Geometry Backbones over Language and Video Models

Zijian Song, Qichang Li, Jiawei Zhou et al. (+4 more)

At its core, robotic manipulation is a problem of vision-to-geometry mapping ($f(v) \rightarrow G$). Physical actions are fundamentally defined by geometric properties like 3D positions and spatial relationships. Consequently, we argue that the foundation for generalizable robotic control should be a vision-geometry backbone, rather than the widely adopted vision-language or video models. Conventional VLA and video-predictive models rely on backbones pretrained on large-scale 2D image-text or temporal pixel data. While effective, their representations are largely shaped by semantic concepts or 2D priors, which do not intrinsically align with the precise 3D geometric nature required for physical manipulation. Driven by this insight, we propose the Vision-Geometry-Action (VGA) model, which directly conditions action generation on pretrained 3D representations. Specifically, VGA replaces conventional language or video backbones with a pretrained 3D world model, establishing a seamless vision-to-geometry mapping that translates visual inputs directly into physical actions. To further enhance geometric consistency, we introduce Progressive Volumetric Modulation and jointly train action and 3D property prediction to preserve geometric representations. Extensive experiments validate the effectiveness of our approach. Across simulation benchmarks, VGA outperforms leading VLA, 3D-VLA, and WAM baselines, including $\pi_{0.5}$, OpenVLA-OFT, GeoVLA, and Motus. In real-world deployments, VGA surpasses $\pi_{0.5}$ under unseen viewpoints and accurately follows language instructions for target grasping. These results highlight that operating on native 3D representations, rather than relying primarily on language or video priors, offers a promising direction toward generalizable physical intelligence. Project page: https://hcplab-sysu.github.io/VisionGeometryActionModel.
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All authors Zijian Song, Qichang Li, Jiawei Zhou, Zhenlong Yuan, Tianshui Chen, Liang Lin, Guangrun Wang

Robotics

Frequency-aware decomposition learning for sensorless wrench estimation in vibration-rich robotic contact

Hyeonbeen Lee, Min-Jae Jung, Tae-Kyeong Yeu et al. (+4 more)

Force and torque (F/T) sensors enable contact-aware control by providing reactive feedback, but they are often fragile and expensive. To overcome these limitations, sensorless methods estimate F/T or wrench solely from robot proprioception, and have shown success in slow interaction tasks such as grasping. However, their low-pass characteristics limit the estimation of high-frequency signals, which are critical in rapid-contact tasks such as grinding. Communication delays can also make their estimates outdated during deployment, but few methods address this directly. To bridge these gaps, we propose a Frequency-aware Decomposition Network (FDN) to estimate vibration-rich wrench in a sensorless, multi-step-ahead manner. Considering higher-frequency stochasticity, FDN spectrally decomposes the wrench horizon into a low-frequency trend and a high-frequency residual, and estimates each by pointwise regression and a learned conditional distribution, respectively. The frequency-aware layers impose band decomposition priors on the outputs and adaptively enhance frequency amplitudes of the inputs. FDN requires neither an identified robot model nor an F/T sensor during estimation. On real-world grinding data from our 6-DoF hydraulic manipulator, FDN reduces high-frequency amplitude error by up to 47% over the baselines under assumed time delays and maintains competitive low-frequency pointwise accuracy, while the baselines fail to balance these two. We also find multi-step-ahead estimation feasible, with FDN estimating a 1,000 ms horizon within 11 ms on a single CPU thread. Ablation studies further support our design choices. In an exploratory study, transferring wrench dynamics learned from an open-source everyday manipulation dataset reduces low-frequency error by 8%, while high-frequency dynamics appear domain-specific.
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All authors Hyeonbeen Lee, Min-Jae Jung, Tae-Kyeong Yeu, Jong-Boo Han, Daegil Park, Simon Stepputtis, Jin-Gyun Kim

Robotics

FastGrasp: Learning-based Whole-Body Control Method for Fast Dexterous Grasping with Mobile Manipulators

Heng Tao, Yiming Zhong, Zemin Yang et al. (+1 more)

Fast dexterous grasping while a mobile base remains in motion requires coordinated whole-body control and rapid adaptation to physical contact. We propose FastGrasp, a two-stage learning framework that integrates grasp guidance, whole-body control, and tactile feedback. First, a pretrained conditional variational autoencoder generates diverse grasp candidates from object point clouds. Candidates are filtered by approach direction and supporting-surface constraints, then ranked using an envelopment-based criterion combining grasp width and depth coverage measures. Second, a reinforcement learning policy jointly controls the mobile base, arm, and dexterous hand, using the selected grasp as guidance and tactile observations for online grasp adjustment. The policy is trained with domain randomization and deployed with command filtering and tactile-triggered grasp tightening. Simulation experiments show higher grasp success rates than the evaluated baselines under full and partial point-cloud observations, while real-world experiments demonstrate sim-to-real transfer across diverse object geometries.
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All authors Heng Tao, Yiming Zhong, Zemin Yang, Yuexin Ma

Robotics

OVAL: Open-Vocabulary Augmented Memory Model for Lifelong Object Goal Navigation

Jiahua Pei, Yi Liu, Guoping Pan et al. (+3 more)

Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. While existing methods demonstrate proficiency in isolated single object navigation, their limitations emerge in the restricted applicability of lifelong memory representations, which ultimately hinders effective navigation toward continual targets over extended periods. To address this problem, we propose OVAL, a novel lifelong open-vocabulary memory framework, which enables efficient and precise execution of long-term navigation in semantically open tasks. Within this framework, we introduce memory descriptors to facilitate structured management of the memory model. Additionally, we propose a novel probability-based exploration strategy, utilizing a multi-value frontier scoring to enhance lifelong exploration efficiency. Extensive experiments demonstrate the efficiency and robustness of the proposed system.
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All authors Jiahua Pei, Yi Liu, Guoping Pan, Yuanhao Jiang, Houde Liu, Xueqian Wang