🤖

Robotics

Discover robotics research, automation, and intelligent systems

2822
Total Items
1538
Papers
177
Videos
37
Books
974
Blogs
30
News
0
Podcasts
34
GitHub
0
Q&A
Browsing by date

Navigate through content by publication date

Thu, Feb 19

24 items found

1 of 152
📄 paper

Articulated 3D Scene Graphs for Open-World Mobile Manipulation

Semantics has enabled 3D scene understanding and affordance-driven object interaction. However, robots operating in real-world environments face a critical limitation: they cannot anticipate how objects move. Long-horizon mobile manipulation requires closing the gap between semantics, geometry, and kinematics. In this work, we present MoMa-SG, a novel framework for building semantic-kinematic 3D scene graphs of articulated scenes containing a myriad of interactable objects. Given RGB-D sequences containing multiple object articulations, we temporally segment object interactions and infer object motion using occlusion-robust point tracking. We then lift point trajectories into 3D and estimate articulation models using a novel unified twist estimation formulation that robustly estimates revolute and prismatic joint parameters in a single optimization pass. Next, we associate objects with estimated articulations and detect contained objects by reasoning over parent-child relations at identified opening states. We also introduce the novel Arti4D-Semantic dataset, which uniquely combines hierarchical object semantics including parent-child relation labels with object axis annotations across 62 in-the-wild RGB-D sequences containing 600 object interactions and three distinct observation paradigms. We extensively evaluate the performance of MoMa-SG on two datasets and ablate key design choices of our approach. In addition, real-world experiments on both a quadruped and a mobile manipulator demonstrate that our semantic-kinematic scene graphs enable robust manipulation of articulated objects in everyday home environments. We provide code and data at: https://momasg.cs.uni-freiburg.de.

Robotics advanced Computer VisionArtificial Intelligence
By: Martin Büchner, Adrian Röfer, Tim Engelbracht +5 more
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

System Identification under Constraints and Disturbance: A Bayesian Estimation Approach

We introduce a Bayesian system identification (SysID) framework for jointly estimating robot's state trajectories and physical parameters with high accuracy. It embeds physically consistent inverse dynamics, contact and loop-closure constraints, and fully featured joint friction models as hard, stage-wise equality constraints. It relies on energy-based regressors to enhance parameter observability, supports both equality and inequality priors on inertial and actuation parameters, enforces dynamically consistent disturbance projections, and augments proprioceptive measurements with energy observations to disambiguate nonlinear friction effects. To ensure scalability, we derive a parameterized equality-constrained Riccati recursion that preserves the banded structure of the problem, achieving linear complexity in the time horizon, and develop computationally efficient derivatives. Simulation studies on representative robotic systems, together with hardware experiments on a Unitree B1 equipped with a Z1 arm, demonstrate faster convergence, lower inertial and friction estimation errors, and improved contact consistency compared to forward-dynamics and decoupled identification baselines. When deployed within model predictive control frameworks, the resulting models yield measurable improvements in tracking performance during locomotion over challenging environments.

Robotics advanced Robotics
By: Sergi Martinez, Steve Tonneau, Carlos Mastalli
Source: arXiv Feb 18, 2026
0.0
5 min read
0
Quality
📄 paper

Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped

SLOT (Soft Legged Omnidirectional Tetrapod), a tendon-driven soft quadruped robot with 3D-printed TPU legs, is presented to study physics-informed modeling and control of compliant legged locomotion using only four actuators. Each leg is modeled as a deformable continuum using discrete Cosserat rod theory, enabling the capture of large bending deformations, distributed elasticity, tendon actuation, and ground contact interactions. A modular whole-body modeling framework is introduced, in which compliant leg dynamics are represented through physically consistent reaction forces applied to a rigid torso, providing a scalable interface between continuum soft limbs and rigid-body locomotion dynamics. This formulation allows efficient whole-body simulation and real-time control without sacrificing physical fidelity. The proposed model is embedded into a convex model predictive control framework that optimizes ground reaction forces over a 0.495 s prediction horizon and maps them to tendon actuation through a physics-informed force-angle relationship. The resulting controller achieves asymptotic stability under diverse perturbations. The framework is experimentally validated on a physical prototype during crawling and walking gaits, achieving high accuracy with less than 5 mm RMSE in center of mass trajectories. These results demonstrate a generalizable approach for integrating continuum soft legs into model-based locomotion control, advancing scalable and reusable modeling and control methods for soft quadruped robots.

Robotics advanced Robotics
By: Saumya Karan, Neerav Maram, Suraj Borate +1 more
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation

The pursuit of general-purpose robotic manipulation is hindered by the scarcity of diverse, real-world interaction data. Unlike data collection from web in vision or language, robotic data collection is an active process incurring prohibitive physical costs. Consequently, automated task curation to maximize data value remains a critical yet under-explored challenge. Existing manual methods are unscalable and biased toward common tasks, while off-the-shelf foundation models often hallucinate physically infeasible instructions. To address this, we introduce RoboGene, an agentic framework designed to automate the generation of diverse, physically plausible manipulation tasks across single-arm, dual-arm, and mobile robots. RoboGene integrates three core components: diversity-driven sampling for broad task coverage, self-reflection mechanisms to enforce physical constraints, and human-in-the-loop refinement for continuous improvement. We conduct extensive quantitative analysis and large-scale real-world experiments, collecting datasets of 18k trajectories and introducing novel metrics to assess task quality, feasibility, and diversity. Results demonstrate that RoboGene significantly outperforms state-of-the-art foundation models (e.g., GPT-4o, Gemini 2.5 Pro). Furthermore, real-world experiments show that VLA models pre-trained with RoboGene achieve higher success rates and superior generalization, underscoring the importance of high-quality task generation. Our project is available at https://robogene-boost-vla.github.io.

Robotics advanced Artificial IntelligenceMachine Learning
By: Yixue Zhang, Kun Wu, Zhi Gao +12 more
Source: arXiv Feb 18, 2026
0.0
5 min read
0
Quality
📄 paper

Reactive Motion Generation With Particle-Based Perception in Dynamic Environments

Reactive motion generation in dynamic and unstructured scenarios is typically subject to essentially static perception and system dynamics. Reliably modeling dynamic obstacles and optimizing collision-free trajectories under perceptive and control uncertainty are challenging. This article focuses on revealing tight connection between reactive planning and dynamic mapping for manipulators from a model-based perspective. To enable efficient particle-based perception with expressively dynamic property, we present a tensorized particle weight update scheme that explicitly maintains obstacle velocities and covariance meanwhile. Building upon this dynamic representation, we propose an obstacle-aware MPPI-based planning formulation that jointly propagates robot-obstacle dynamics, allowing future system motion to be predicted and evaluated under uncertainty. The model predictive method is shown to significantly improve safety and reactivity with dynamic surroundings. By applying our complete framework in simulated and noisy real-world environments, we demonstrate that explicit modeling of robot-obstacle dynamics consistently enhances performance over state-of-the-art MPPI-based perception-planning baselines avoiding multiple static and dynamic obstacles.

Robotics advanced Robotics
By: Xiyuan Zhao, Huijun Li, Lifeng Zhu +3 more
Source: arXiv Feb 18, 2026
0.0
5 min read
0
Quality
📄 paper

VIGOR: Visual Goal-In-Context Inference for Unified Humanoid Fall Safety

Reliable fall recovery is critical for humanoids operating in cluttered environments. Unlike quadrupeds or wheeled robots, humanoids experience high-energy impacts, complex whole-body contact, and large viewpoint changes during a fall, making recovery essential for continued operation. Existing methods fragment fall safety into separate problems such as fall avoidance, impact mitigation, and stand-up recovery, or rely on end-to-end policies trained without vision through reinforcement learning or imitation learning, often on flat terrain. At a deeper level, fall safety is treated as monolithic data complexity, coupling pose, dynamics, and terrain and requiring exhaustive coverage, limiting scalability and generalization. We present a unified fall safety approach that spans all phases of fall recovery. It builds on two insights: 1) Natural human fall and recovery poses are highly constrained and transferable from flat to complex terrain through alignment, and 2) Fast whole-body reactions require integrated perceptual-motor representations. We train a privileged teacher using sparse human demonstrations on flat terrain and simulated complex terrains, and distill it into a deployable student that relies only on egocentric depth and proprioception. The student learns how to react by matching the teacher's goal-in-context latent representation, which combines the next target pose with the local terrain, rather than separately encoding what it must perceive and how it must act. Results in simulation and on a real Unitree G1 humanoid demonstrate robust, zero-shot fall safety across diverse non-flat environments without real-world fine-tuning. The project page is available at https://vigor2026.github.io/

Robotics advanced Robotics
By: Osher Azulay, Zhengjie Xu, Andrew Scheffer +1 more
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

Decentralized and Fully Onboard: Range-Aided Cooperative Localization and Navigation on Micro Aerial Vehicles

Controlling a team of robots in a coordinated manner is challenging because centralized approaches (where all computation is performed on a central machine) scale poorly, and globally referenced external localization systems may not always be available. In this work, we consider the problem of range-aided decentralized localization and formation control. In such a setting, each robot estimates its relative pose by combining data only from onboard odometry sensors and distance measurements to other robots in the team. Additionally, each robot calculates the control inputs necessary to collaboratively navigate an environment to accomplish a specific task, for example, moving in a desired formation while monitoring an area. We present a block coordinate descent approach to localization that does not require strict coordination between the robots. We present a novel formulation for formation control as inference on factor graphs that takes into account the state estimation uncertainty and can be solved efficiently. Our approach to range-aided localization and formation-based navigation is completely decentralized, does not require specialized trajectories to maintain formation, and achieves decimeter-level positioning and formation control accuracy. We demonstrate our approach through multiple real experiments involving formation flights in diverse indoor and outdoor environments.

Robotics advanced Robotics
By: Abhishek Goudar, Angela P. Schoellig
Source: arXiv Feb 18, 2026
0.0
5 min read
0
Quality
📄 paper

Sensor Query Schedule and Sensor Noise Covariances for Accuracy-constrained Trajectory Estimation

Trajectory estimation involves determining the trajectory of a mobile robot by combining prior knowledge about its dynamic model with noisy observations of its state obtained using sensors. The accuracy of such a procedure is dictated by the system model fidelity and the sensor parameters, such as the accuracy of the sensor (as represented by its noise covariance) and the rate at which it can generate observations, referred to as the sensor query schedule. Intuitively, high-rate measurements from accurate sensors lead to accurate trajectory estimation. However, cost and resource constraints limit the sensor accuracy and its measurement rate. Our work's novel contribution is the estimation of sensor schedules and sensor covariances necessary to achieve a specific estimation accuracy. Concretely, we focus on estimating: (i) the rate or schedule with which a sensor of known covariance must generate measurements to achieve specific estimation accuracy, and alternatively, (ii) the sensor covariance necessary to achieve specific estimation accuracy for a given sensor update rate. We formulate the problem of estimating these sensor parameters as semidefinite programs, which can be solved by off-the-shelf solvers. We validate our approach in simulation and real experiments by showing that the sensor schedules and the sensor covariances calculated using our proposed method achieve the desired trajectory estimation accuracy. Our method also identifies scenarios where certain estimation accuracy is unachievable with the given system and sensor characteristics.

Robotics advanced Robotics
By: Abhishek Goudar, Angela P. Schoellig
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

Towards Autonomous Robotic Kidney Ultrasound: Spatial-Efficient Volumetric Imaging via Template Guided Optimal Pivoting

Medical ultrasound (US) imaging is a frontline tool for the diagnosis of kidney diseases. However, traditional freehand imaging procedure suffers from inconsistent, operator-dependent outcomes, lack of 3D localization information, and risks of work-related musculoskeletal disorders. While robotic ultrasound (RUS) systems offer the potential for standardized, operator-independent 3D kidney data acquisition, the existing scanning methods lack the ability to determine the optimal imaging window for efficient imaging. As a result, the scan is often blindly performed with excessive probe footprint, which frequently leads to acoustic shadowing and incomplete organ coverage. Consequently, there is a critical need for a spatially efficient imaging technique that can maximize the kidney coverage through minimum probe footprint. Here, we propose an autonomous workflow to achieve efficient kidney imaging via template-guided optimal pivoting. The system first performs an explorative imaging to generate partial observations of the kidney. This data is then registered to a kidney template to estimate the organ pose. With the kidney localized, the robot executes a fixed-point pivoting sweep where the imaging plane is aligned with the kidney long axis to minimize the probe translation. The proposed method was validated in simulation and in-vivo. Simulation results indicate that a 60% exploration ratio provides optimal balance between kidney localization accuracy and scanning efficiency. In-vivo evaluation on two male subjects demonstrates a kidney localization accuracy up to 7.36 mm and 13.84 degrees. Moreover, the optimal pivoting approach shortened the probe footprint by around 75 mm when compared with the baselines. These results valid our approach of leveraging anatomical templates to align the probe optimally for volumetric sweep.

Robotics advanced Robotics
By: Xihan Ma, Haichong Zhang
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

Learning to unfold cloth: Scaling up world models to deformable object manipulation

Learning to manipulate cloth is both a paradigmatic problem for robotic research and a problem of immediate relevance to a variety of applications ranging from assistive care to the service industry. The complex physics of the deformable object makes this problem of cloth manipulation nontrivial. In order to create a general manipulation strategy that addresses a variety of shapes, sizes, fold and wrinkle patterns, in addition to the usual problems of appearance variations, it becomes important to carefully consider model structure and their implications for generalisation performance. In this paper, we present an approach to in-air cloth manipulation that uses a variation of a recently proposed reinforcement learning architecture, DreamerV2. Our implementation modifies this architecture to utilise surface normals input, in addition to modiying the replay buffer and data augmentation procedures. Taken together these modifications represent an enhancement to the world model used by the robot, addressing the physical complexity of the object being manipulated by the robot. We present evaluations both in simulation and in a zero-shot deployment of the trained policies in a physical robot setup, performing in-air unfolding of a variety of different cloth types, demonstrating the generalisation benefits of our proposed architecture.

Robotics advanced Robotics
By: Jack Rome, Stephen James, Subramanian Ramamoorthy
Source: arXiv Feb 18, 2026
0.0
5 min read
0
Quality
📄 paper

Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation

Visual loco-manipulation of arbitrary objects in the wild with humanoid robots requires accurate end-effector (EE) control and a generalizable understanding of the scene via visual inputs (e.g., RGB-D images). Existing approaches are based on real-world imitation learning and exhibit limited generalization due to the difficulty in collecting large-scale training datasets. This paper presents a new paradigm, HERO, for object loco-manipulation with humanoid robots that combines the strong generalization and open-vocabulary understanding of large vision models with strong control performance from simulated training. We achieve this by designing an accurate residual-aware EE tracking policy. This EE tracking policy combines classical robotics with machine learning. It uses a) inverse kinematics to convert residual end-effector targets into reference trajectories, b) a learned neural forward model for accurate forward kinematics, c) goal adjustment, and d) replanning. Together, these innovations help us cut down the end-effector tracking error by 3.2x. We use this accurate end-effector tracker to build a modular system for loco-manipulation, where we use open-vocabulary large vision models for strong visual generalization. Our system is able to operate in diverse real-world environments, from offices to coffee shops, where the robot is able to reliably manipulate various everyday objects (e.g., mugs, apples, toys) on surfaces ranging from 43cm to 92cm in height. Systematic modular and end-to-end tests in simulation and the real world demonstrate the effectiveness of our proposed design. We believe the advances in this paper can open up new ways of training humanoid robots to interact with daily objects.

Robotics advanced Computer VisionRobotics
By: Runpei Dong, Ziyan Li, Xialin He +1 more
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data

Human behavior is among the most scalable sources of data for learning physical intelligence, yet how to effectively leverage it for dexterous manipulation remains unclear. While prior work demonstrates human to robot transfer in constrained settings, it is unclear whether large scale human data can support fine grained, high degree of freedom dexterous manipulation. We present EgoScale, a human to dexterous manipulation transfer framework built on large scale egocentric human data. We train a Vision Language Action (VLA) model on over 20,854 hours of action labeled egocentric human video, more than 20 times larger than prior efforts, and uncover a log linear scaling law between human data scale and validation loss. This validation loss strongly correlates with downstream real robot performance, establishing large scale human data as a predictable supervision source. Beyond scale, we introduce a simple two stage transfer recipe: large scale human pretraining followed by lightweight aligned human robot mid training. This enables strong long horizon dexterous manipulation and one shot task adaptation with minimal robot supervision. Our final policy improves average success rate by 54% over a no pretraining baseline using a 22 DoF dexterous robotic hand, and transfers effectively to robots with lower DoF hands, indicating that large scale human motion provides a reusable, embodiment agnostic motor prior.

Robotics advanced Robotics
By: Ruijie Zheng, Dantong Niu, Yuqi Xie +12 more
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
📄 paper

One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation

Dexterous manipulation policies today largely assume fixed hand designs, severely restricting their generalization to new embodiments with varied kinematic and structural layouts. To overcome this limitation, we introduce a parameterized canonical representation that unifies a broad spectrum of dexterous hand architectures. It comprises a unified parameter space and a canonical URDF format, offering three key advantages. 1) The parameter space captures essential morphological and kinematic variations for effective conditioning in learning algorithms. 2) A structured latent manifold can be learned over our space, where interpolations between embodiments yield smooth and physically meaningful morphology transitions. 3) The canonical URDF standardizes the action space while preserving dynamic and functional properties of the original URDFs, enabling efficient and reliable cross-embodiment policy learning. We validate these advantages through extensive analysis and experiments, including grasp policy replay, VAE latent encoding, and cross-embodiment zero-shot transfer. Specifically, we train a VAE on the unified representation to obtain a compact, semantically rich latent embedding, and develop a grasping policy conditioned on the canonical representation that generalizes across dexterous hands. We demonstrate, through simulation and real-world tasks on unseen morphologies (e.g., 81.9% zero-shot success rate on 3-finger LEAP Hand), that our framework unifies both the representational and action spaces of structurally diverse hands, providing a scalable foundation for cross-hand learning toward universal dexterous manipulation.

Robotics advanced Robotics
By: Zhenyu Wei, Yunchao Yao, Mingyu Ding
Source: arXiv Feb 18, 2026
0.0
10 min read
0
Quality
🎥 video

Greetings From the Arduino UNO Q at Bett UK 2026! 👋

Robotics expert @kevinmcaleer28 joined us at #BettUK2026 to show how the UNO Q brings AI and physical computing together. In this demo, the board recognizes your face locally and waves hello using a servo motor — combining vision, AI, and real-time hardware control, all with privacy built in.

Robotics advanced General Robotics
By: Arduino
Source: YouTube Robotics Channels Feb 18, 2026
1.0
1,564
Quality
🎥 video

Kevin McAleer Talks Arduino UNO Q at Bett UK 2026

Want to see what the Arduino UNO Q can really do? Watch@kevinmcaleer28, robotics maker, educator and the creator of KevsRobots, share his excitement for the new UNO Q thanks to its ability to simplify data acquisition projects, including a fully local weather station that uses the board to collect, store, and visualize real environmental readings, all without the cloud.

Robotics intermediate General Robotics
By: Arduino
Source: YouTube Robotics Channels Feb 18, 2026
0.8
707
Quality
🎥 video

Arduino Alvik Fight Club at Bett UK 2026

At #BettUK2026, MoonMakers proved that robotics can really pack a punch! 🤖 👊 Watch to hear their thoughts on how the Arduino Alvik robot supports classroom learning and helps students build real-world skills through challenges like the Alvik Fight Club.

Robotics intermediate General Robotics
By: Arduino
Source: YouTube Robotics Channels Feb 18, 2026
0.8
853
Quality
📝 blog

igus launches new RBTX online shopping experience for robotics developers

This shift to a self-serve model removes the requirement for a sales consultation, providing a streamlined experience, igus said. The post igus launches new RBTX online shopping experience for robotics developers appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: The Robot Report Staff
Source: Robotics Business Review Feb 18, 2026
0.0
1 min read
0
Quality
📝 blog

Ottonomy builds Ottumn.AI platform to orchestrate robots, drones, and smart infrastructure

Ottonomy built Ottumn.AI on NVIDIA's infrastructure to integrate delivery systems for healthcare, logistics, and smart cities. The post Ottonomy builds Ottumn.AI platform to orchestrate robots, drones, and smart infrastructure appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: The Robot Report Staff
Source: Robotics Business Review Feb 18, 2026
0.0
1 min read
0
Quality
📝 blog

NVIDIA adds Cosmos Policy to its world foundation models

Cosmos Policy is a new robot control policy that post-trains the Cosmos Predict-2 world foundation model for manipulation tasks. The post NVIDIA adds Cosmos Policy to its world foundation models appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: The Robot Report Staff
Source: Robotics Business Review Feb 19, 2026
0.0
1 min read
0
Quality
📝 blog

Toyota Motor Manufacturing Canada to deploy Agility Robotics’ Digit humanoids

Toyota Motor Manufacturing Canada is expanding its commercial trials for Digit from three humanoids to 10. The post Toyota Motor Manufacturing Canada to deploy Agility Robotics’ Digit humanoids appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: Eugene Demaitre
Source: Robotics Business Review Feb 19, 2026
0.0
1 min read
0
Quality
📝 blog

Ensuring Safe and Compliant Deployment of Medical Robotics

Medical robotics is not just a futuristic idea today. That’s why you now witness many robotic systems assisting surgeons, helping with rehab therapy, and automating hospital tasks, all over the world. As tech adoption continues to rise, regulatory pressure and liability risks also increase. So, if you wish to deploy medical robotics, you need a […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News Feb 18, 2026
0.0
1 min read
0
Quality
📝 blog

How Businesses Are Using Freelance Tech Talent to Drive Robotics Innovation

The robotics and automation industry is advancing faster than ever. Companies are racing to innovate, reduce costs, and bring cutting-edge solutions to market. In this highly competitive landscape, traditional hiring models are often too slow and inflexible. Businesses are increasingly turning to freelance tech talent to accelerate projects and gain access to specialized skills. Freelancers […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News Feb 18, 2026
0.0
1 min read
0
Quality
📝 blog

Designing for Automation in Injection Molding: How Product Design Drives Robotic Manufacturing Efficiency

The use of automation in injection molding has developed beyond the basic part extraction into all-inclusive robotic cells that manage the molding process, inspection, assembly, and packaging. With seemingly great emphasis on the selection of robots, end-of-arm tooling (EOAT), and control systems, the critical elements that determine the success of automation are often made at […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News Feb 19, 2026
0.0
1 min read
0
Quality
📝 blog

When the Algorithm Fails: Who is Legally Responsible After a Waymo Accident?

The Technology is Impressive, But it’s Not Infallible Waymo’s autonomous vehicles are, by most technical measures, a remarkable achievement. Each car is equipped with a suite of LiDAR sensors, radar, cameras, and a continuously updated AI model trained on billions of miles of real-world and simulated driving data. Alphabet’s robotaxi division has positioned itself as […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News Feb 19, 2026
0.0
1 min read
0
Quality