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Robotics

Discover robotics research, automation, and intelligent systems

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Sat, Nov 15

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The progress in robotic hands is moving fast

The progress in robotic hands is moving fast

Robotics intermediate General Robotics
By: Diligent_Rabbit7740
Source: Reddit Nov 15, 2025
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Scientists gave a robot a hyper-realistic human mask and the result is eerily uncanny.

Scientists gave a robot a hyper-realistic human mask and the result is eerily uncanny.

Robotics intermediate General Robotics
By: ActivityEmotional228
Source: Reddit Nov 15, 2025
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Accelerate Robotics and Real-Time AI Inference on NVIDIA Jetson Thor

Discover the future of robotics with NVIDIA Jetson Thor—the breakthrough platform for physical AI and real-time reasoning. Now with 3.5X faster generative AI performance compared to product launch, Jetson Thor delivers unmatched power for next-gen robotics. ➡️ Learn more: https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/ 📝 Tech blog: https://developer.nvidia.com/blog/unlock-faster-smarter-edge-models-with-7x-gen-ai-performance-on-nvidia-jetson-agx-thor/ 📥 Benchmarki

Robotics beginner Autonomous Systems
By: NVIDIA Developer
Source: YouTube Nov 14, 2025
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Phantom Menace: Exploring and Enhancing the Robustness of VLA Models against Physical Sensor Attacks

Vision-Language-Action (VLA) models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA models to interpret complex, real-world environments using diverse sensor data streams. Given the fact that VLA-based systems heavily rely on the sensory input, the security of VLA models against physical-world sensor attacks remains critically underexplored. To address this gap, we present the first systematic study of physical sensor attacks against VLAs, quantifying the influence of sensor attacks and investigating the defenses for VLA models. We introduce a novel ``Real-Sim-Real'' framework that automatically simulates physics-based sensor attack vectors, including six attacks targeting cameras and two targeting microphones, and validates them on real robotic systems. Through large-scale evaluations across various VLA architectures and tasks under varying attack parameters, we demonstrate significant vulnerabilities, with susceptibility patterns that reveal critical dependencies on task types and model designs. We further develop an adversarial-training-based defense that enhances VLA robustness against out-of-distribution physical perturbations caused by sensor attacks while preserving model performance. Our findings expose an urgent need for standardized robustness benchmarks and mitigation strategies to secure VLA deployments in safety-critical environments.

Robotics advanced RoboticsArtificial Intelligence
By: Xuancun Lu, Jiaxiang Chen, Shilin Xiao +7 more
Source: arXiv Nov 12, 2025
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DecARt Leg: Design and Evaluation of a Novel Humanoid Robot Leg with Decoupled Actuation for Agile Locomotion

In this paper, we propose a novel design of an electrically actuated robotic leg, called the DecARt (Decoupled Actuation Robot) Leg, aimed at performing agile locomotion. This design incorporates several new features, such as the use of a quasi-telescopic kinematic structure with rotational motors for decoupled actuation, a near-anthropomorphic leg appearance with a forward facing knee, and a novel multi-bar system for ankle torque transmission from motors placed above the knee. To analyze the agile locomotion capabilities of the design numerically, we propose a new descriptive metric, called the `Fastest Achievable Swing Time` (FAST), and perform a quantitative evaluation of the proposed design and compare it with other designs. Then we evaluate the performance of the DecARt Leg-based robot via extensive simulation and preliminary hardware experiments.

Robotics advanced Robotics
By: Egor Davydenko, Andrei Volchenkov, Vladimir Gerasimov +1 more
Source: arXiv Nov 12, 2025
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Physics-informed Machine Learning for Static Friction Modeling in Robotic Manipulators Based on Kolmogorov-Arnold Networks

Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are widely used due to their simple forms; however, they typically require predefined functional assumptions, which poses significant challenges when dealing with unknown functional structures. To address this issue, this paper proposes a physics-inspired machine learning approach based on the Kolmogorov Arnold Network (KAN) for static friction modeling of robotic joints. The method integrates spline activation functions with a symbolic regression mechanism, enabling model simplification and physical expression extraction through pruning and attribute scoring, while maintaining both high prediction accuracy and interpretability. We first validate the method's capability to accurately identify key parameters under known functional models, and further demonstrate its robustness and generalization ability under conditions with unknown functional structures and noisy data. Experiments conducted on both synthetic data and real friction data collected from a six-degree-of-freedom industrial manipulator show that the proposed method achieves a coefficient of determination greater than 0.95 across various tasks and successfully extracts concise and physically meaningful friction expressions. This study provides a new perspective for interpretable and data-driven robotic friction modeling with promising engineering applicability.

Robotics advanced Machine LearningRobotics
By: Yizheng Wang, Timon Rabczuk, Yinghua Liu
Source: arXiv Nov 13, 2025
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Opinion: Towards Unified Expressive Policy Optimization for Robust Robot Learning

Offline-to-online reinforcement learning (O2O-RL) has emerged as a promising paradigm for safe and efficient robotic policy deployment but suffers from two fundamental challenges: limited coverage of multimodal behaviors and distributional shifts during online adaptation. We propose UEPO, a unified generative framework inspired by large language model pretraining and fine-tuning strategies. Our contributions are threefold: (1) a multi-seed dynamics-aware diffusion policy that efficiently captures diverse modalities without training multiple models; (2) a dynamic divergence regularization mechanism that enforces physically meaningful policy diversity; and (3) a diffusion-based data augmentation module that enhances dynamics model generalization. On the D4RL benchmark, UEPO achieves +5.9\% absolute improvement over Uni-O4 on locomotion tasks and +12.4\% on dexterous manipulation, demonstrating strong generalization and scalability.

Robotics advanced Machine LearningRobotics
By: Haidong Huang, Haiyue Zhu. Jiayu Song, Xixin Zhao +4 more
Source: arXiv Nov 13, 2025
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Learning a Thousand Tasks in a Day

Humans are remarkably efficient at learning tasks from demonstrations, but today's imitation learning methods for robot manipulation often require hundreds or thousands of demonstrations per task. We investigate two fundamental priors for improving learning efficiency: decomposing manipulation trajectories into sequential alignment and interaction phases, and retrieval-based generalisation. Through 3,450 real-world rollouts, we systematically study this decomposition. We compare different design choices for the alignment and interaction phases, and examine generalisation and scaling trends relative to today's dominant paradigm of behavioural cloning with a single-phase monolithic policy. In the few-demonstrations-per-task regime (<10 demonstrations), decomposition achieves an order of magnitude improvement in data efficiency over single-phase learning, with retrieval consistently outperforming behavioural cloning for both alignment and interaction. Building on these insights, we develop Multi-Task Trajectory Transfer (MT3), an imitation learning method based on decomposition and retrieval. MT3 learns everyday manipulation tasks from as little as a single demonstration each, whilst also generalising to novel object instances. This efficiency enables us to teach a robot 1,000 distinct everyday tasks in under 24 hours of human demonstrator time. Through 2,200 additional real-world rollouts, we reveal MT3's capabilities and limitations across different task families. Videos of our experiments can be found on at https://www.robot-learning.uk/learning-1000-tasks.

Robotics advanced Robotics
By: Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius +1 more
Source: arXiv Nov 13, 2025
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VISTA: A Vision and Intent-Aware Social Attention Framework for Multi-Agent Trajectory Prediction

Multi-agent trajectory prediction is crucial for autonomous systems operating in dense, interactive environments. Existing methods often fail to jointly capture agents' long-term goals and their fine-grained social interactions, which leads to unrealistic multi-agent futures. We propose VISTA, a recursive goal-conditioned transformer for multi-agent trajectory forecasting. VISTA combines (i) a cross-attention fusion module that integrates long-horizon intent with past motion, (ii) a social-token attention mechanism for flexible interaction modeling across agents, and (iii) pairwise attention maps that make social influence patterns interpretable at inference time. Our model turns single-agent goal-conditioned prediction into a coherent multi-agent forecasting framework. Beyond standard displacement metrics, we evaluate trajectory collision rates as a measure of joint realism. On the high-density MADRAS benchmark and on SDD, VISTA achieves state-of-the-art accuracy and substantially fewer collisions. On MADRAS, it reduces the average collision rate of strong baselines from 2.14 to 0.03 percent, and on SDD it attains zero collisions while improving ADE, FDE, and minFDE. These results show that VISTA generates socially compliant, goal-aware, and interpretable trajectories, making it promising for safety-critical autonomous systems.

Robotics advanced RoboticsArtificial Intelligence
By: Stephane Da Silva Martins, Emanuel Aldea, Sylvie Le Hégarat-Mascle
Source: arXiv Nov 13, 2025
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RoboBenchMart: Benchmarking Robots in Retail Environment

Most existing robotic manipulation benchmarks focus on simplified tabletop scenarios, typically involving a stationary robotic arm interacting with various objects on a flat surface. To address this limitation, we introduce RoboBenchMart, a more challenging and realistic benchmark designed for dark store environments, where robots must perform complex manipulation tasks with diverse grocery items. This setting presents significant challenges, including dense object clutter and varied spatial configurations -- with items positioned at different heights, depths, and in close proximity. By targeting the retail domain, our benchmark addresses a setting with strong potential for near-term automation impact. We demonstrate that current state-of-the-art generalist models struggle to solve even common retail tasks. To support further research, we release the RoboBenchMart suite, which includes a procedural store layout generator, a trajectory generation pipeline, evaluation tools and fine-tuned baseline models.

Robotics advanced RoboticsArtificial Intelligence
By: Konstantin Soshin, Alexander Krapukhin, Andrei Spiridonov +4 more
Source: arXiv Nov 13, 2025
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MSGNav: Unleashing the Power of Multi-modal 3D Scene Graph for Zero-Shot Embodied Navigation

Embodied navigation is a fundamental capability for robotic agents operating. Real-world deployment requires open vocabulary generalization and low training overhead, motivating zero-shot methods rather than task-specific RL training. However, existing zero-shot methods that build explicit 3D scene graphs often compress rich visual observations into text-only relations, leading to high construction cost, irreversible loss of visual evidence, and constrained vocabularies. To address these limitations, we introduce the Multi-modal 3D Scene Graph (M3DSG), which preserves visual cues by replacing textual relational edges with dynamically assigned images. Built on M3DSG, we propose MSGNav, a zero-shot navigation system that includes a Key Subgraph Selection module for efficient reasoning, an Adaptive Vocabulary Update module for open vocabulary support, and a Closed-Loop Reasoning module for accurate exploration reasoning. Additionally, we further identify the last-mile problem in zero-shot navigation - determining the feasible target location with a suitable final viewpoint, and propose a Visibility-based Viewpoint Decision module to explicitly resolve it. Comprehensive experimental results demonstrate that MSGNav achieves state-of-the-art performance on GOAT-Bench and HM3D-OVON datasets. The open-source code will be publicly available.

Robotics advanced RoboticsComputer Vision
By: Xun Huang, Shijia Zhao, Yunxiang Wang +6 more
Source: arXiv Nov 13, 2025
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nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation

Recent advances in closed-loop planning benchmarks have significantly improved the evaluation of autonomous vehicles. However, existing benchmarks still rely on rule-based reactive agents such as the Intelligent Driver Model (IDM), which lack behavioral diversity and fail to capture realistic human interactions, leading to oversimplified traffic dynamics. To address these limitations, we present nuPlan-R, a new reactive closed-loop planning benchmark that integrates learning-based reactive multi-agent simulation into the nuPlan framework. Our benchmark replaces the rule-based IDM agents with noise-decoupled diffusion-based reactive agents and introduces an interaction-aware agent selection mechanism to ensure both realism and computational efficiency. Furthermore, we extend the benchmark with two additional metrics to enable a more comprehensive assessment of planning performance. Extensive experiments demonstrate that our reactive agent model produces more realistic, diverse, and human-like traffic behaviors, leading to a benchmark environment that better reflects real-world interactive driving. We further reimplement a collection of rule-based, learning-based, and hybrid planning approaches within our nuPlan-R benchmark, providing a clearer reflection of planner performance in complex interactive scenarios and better highlighting the advantages of learning-based planners in handling complex and dynamic scenarios. These results establish nuPlan-R as a new standard for fair, reactive, and realistic closed-loop planning evaluation. We will open-source the code for the new benchmark.

Robotics advanced RoboticsArtificial Intelligence
By: Mingxing Peng, Ruoyu Yao, Xusen Guo +1 more
Source: arXiv Nov 13, 2025
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Improving dependability in robotized bolting operations

Bolting operations are critical in industrial assembly and in the maintenance of scientific facilities, requiring high precision and robustness to faults. Although robotic solutions have the potential to improve operational safety and effectiveness, current systems still lack reliable autonomy and fault management capabilities. To address this gap, we propose a control framework for dependable robotized bolting tasks and instantiate it on a specific robotic system. The system features a control architecture ensuring accurate driving torque control and active compliance throughout the entire operation, enabling safe interaction even under fault conditions. By designing a multimodal human-robot interface (HRI) providing real-time visualization of relevant system information and supporting seamless transitions between automatic and manual control, we improve operator situation awareness and fault detection capabilities. A high-level supervisor (SV) coordinates the execution and manages transitions between control modes, ensuring consistency with the supervisory control (SVC) paradigm, while preserving the human operator's authority. The system is validated in a representative bolting operation involving pipe flange joining, under several fault conditions. The results demonstrate improved fault detection capabilities, enhanced operator situational awareness, and accurate and compliant execution of the bolting operation. However, they also reveal the limitations of relying on a single camera to achieve full situational awareness.

Robotics advanced Robotics
By: Lorenzo Pagliara, Violeta Redondo, Enrico Ferrentino +2 more
Source: arXiv Nov 13, 2025
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SemanticVLA: Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation

Vision-Language-Action (VLA) models have advanced in robotic manipulation, yet practical deployment remains hindered by two key limitations: 1) perceptual redundancy, where irrelevant visual inputs are processed inefficiently, and 2) superficial instruction-vision alignment, which hampers semantic grounding of actions. In this paper, we propose SemanticVLA, a novel VLA framework that performs Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation. Specifically: 1) To sparsify redundant perception while preserving semantic alignment, Semantic-guided Dual Visual Pruner (SD-Pruner) performs: Instruction-driven Pruner (ID-Pruner) extracts global action cues and local semantic anchors in SigLIP; Spatial-aggregation Pruner (SA-Pruner) compacts geometry-rich features into task-adaptive tokens in DINOv2. 2) To exploit sparsified features and integrate semantics with spatial geometry, Semantic-complementary Hierarchical Fuser (SH-Fuser) fuses dense patches and sparse tokens across SigLIP and DINOv2 for coherent representation. 3) To enhance the transformation from perception to action, Semantic-conditioned Action Coupler (SA-Coupler) replaces the conventional observation-to-DoF approach, yielding more efficient and interpretable behavior modeling for manipulation tasks. Extensive experiments on simulation and real-world tasks show that SemanticVLA sets a new SOTA in both performance and efficiency. SemanticVLA surpasses OpenVLA on LIBERO benchmark by 21.1% in success rate, while reducing training cost and inference latency by 3.0-fold and 2.7-fold.SemanticVLA is open-sourced and publicly available at https://github.com/JiuTian-VL/SemanticVLA

Robotics advanced RoboticsComputer Vision
By: Wei Li, Renshan Zhang, Rui Shao +4 more
Source: arXiv Nov 13, 2025
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From Fold to Function: Dynamic Modeling and Simulation-Driven Design of Origami Mechanisms

Origami-inspired mechanisms can transform flat sheets into functional three-dimensional dynamic structures that are lightweight, compact, and capable of complex motion. These properties make origami increasingly valuable in robotic and deployable systems. However, accurately simulating their folding behavior and interactions with the environment remains challenging. To address this, we present a design framework for origami mechanism simulation that utilizes MuJoCo's deformable-body capabilities. In our approach, origami sheets are represented as graphs of interconnected deformable elements with user-specified constraints such as creases and actuation, defined through an intuitive graphical user interface (GUI). This framework allows users to generate physically consistent simulations that capture both the geometric structure of origami mechanisms and their interactions with external objects and surfaces. We demonstrate our method's utility through a case study on an origami catapult, where design parameters are optimized in simulation using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and validated experimentally on physical prototypes. The optimized structure achieves improved throwing performance, illustrating how our system enables rapid, simulation-driven origami design, optimization, and analysis.

Robotics advanced Robotics
By: Tianhui Han, Shashwat Singh, Sarvesh Patil +1 more
Source: arXiv Nov 13, 2025
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Safe Planning in Interactive Environments via Iterative Policy Updates and Adversarially Robust Conformal Prediction

Safe planning of an autonomous agent in interactive environments -- such as the control of a self-driving vehicle among pedestrians and human-controlled vehicles -- poses a major challenge as the behavior of the environment is unknown and reactive to the behavior of the autonomous agent. This coupling gives rise to interaction-driven distribution shifts where the autonomous agent's control policy may change the environment's behavior, thereby invalidating safety guarantees in existing work. Indeed, recent works have used conformal prediction (CP) to generate distribution-free safety guarantees using observed data of the environment. However, CP's assumption on data exchangeability is violated in interactive settings due to a circular dependency where a control policy update changes the environment's behavior, and vice versa. To address this gap, we propose an iterative framework that robustly maintains safety guarantees across policy updates by quantifying the potential impact of a planned policy update on the environment's behavior. We realize this via adversarially robust CP where we perform a regular CP step in each episode using observed data under the current policy, but then transfer safety guarantees across policy updates by analytically adjusting the CP result to account for distribution shifts. This adjustment is performed based on a policy-to-trajectory sensitivity analysis, resulting in a safe, episodic open-loop planner. We further conduct a contraction analysis of the system providing conditions under which both the CP results and the policy updates are guaranteed to converge. We empirically demonstrate these safety and convergence guarantees on a two-dimensional car-pedestrian case study. To the best of our knowledge, these are the first results that provide valid safety guarantees in such interactive settings.

Robotics advanced Robotics
By: Omid Mirzaeedodangeh, Eliot Shekhtman, Nikolai Matni +1 more
Source: arXiv Nov 13, 2025
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Optimizing the flight path for a scouting Uncrewed Aerial Vehicle

Post-disaster situations pose unique navigation challenges. One of those challenges is the unstructured nature of the environment, which makes it hard to layout paths for rescue vehicles. We propose the use of Uncrewed Aerial Vehicle (UAV) in such scenario to perform reconnaissance across the environment. To accomplish this, we propose an optimization-based approach to plan a path for the UAV at optimal height where the sensors of the UAV can cover the most area and collect data with minimum uncertainty.

Robotics advanced Robotics
By: Raghav Adhikari, Sachet Khatiwada, Suman Poudel
Source: arXiv Nov 13, 2025
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Saddle Creek brings in Robust.AI’s Carter robots to automate tote delivery and speed up warehouse operations

Robust.AI, a provider of AI-driven warehouse automation, has announced a partnership with Saddle Creek Logistics Services, an omnichannel supply chain solutions company, to deploy Robust.AI’s flagship collaborative robotics platform, Carter, in Saddle Creek’s Charlotte, North Carolina warehouse. Saddle Creek deployed Carter in its order fulfillment operation for a beauty client. It helps to automate and […]

Robotics intermediate Industry NewsAutomation
By: David Edwards
Source: Robotics and Automation News Nov 14, 2025
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Video Friday: DARPA Challenge Focuses on Heavy Lift Drones

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.ICRA 2026: 1–5 June 2026, VIENNAEnjoy today’s videos! Current multirotor drones provide simplicity, affordability, and ease of operation; however, their primary limitation is their low payload-to-weight ratio, which typically falls at 1:1 or less. The D...

Robotics intermediate Robotics ResearchIndustry News
By: Evan Ackerman
Source: IEEE Spectrum Robotics Nov 14, 2025
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This Soft Robot Is 100% Edible, Including the Battery

While there are many useful questions to ask when encountering a new robot, “can I eat it” is generally not one of them. I say ‘generally,’ because edible robots are actually a thing—and not just edible in the sense that you can technically swallow them and suffer both the benefits and consequences, but ingestible, where you can take a big bite out of the robot, chew it up, and swallow it.Yum.But so far these ingestible robots have included a very please-don’t-ingest-this asterisk: the motor ...

Robotics intermediate Robotics ResearchIndustry News
By: Evan Ackerman
Source: IEEE Spectrum Robotics Nov 14, 2025
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Students Compete—and Cooperate—in FIRST Global Robotics Challenge

Aspiring engineers from 191 countries gathered in Panama City in October to compete in the FIRST Global Robotics Challenge. The annual contest aims to foster problem-solving, cooperation, and inspire the next generation of engineers through three challenges that are inspired by a different theme every year. Teams of students from 14 to 18 years old from around the world compete in the three day event, remotely operating their robots to complete the challenges. This year’s topic was “Eco-equil...

Robotics intermediate Robotics ResearchIndustry News
By: Kohava Mendelsohn
Source: IEEE Spectrum Robotics Nov 15, 2025
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LEMO launches REDEL SP 1P68 Series watertight connectors

LEMO says its new REDEL SP 1P68 Series connectors are suitable for medical devices, drones, and harsh environments. The post LEMO launches REDEL SP 1P68 Series watertight connectors appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: The Robot Report Staff
Source: Robotics Business Review Nov 14, 2025
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California agencies eye BurnBot for wildfire prevention

BurnBot offers an ecologically sensitive, cost-effective new way to create wildfire fuel breaks. The post California agencies eye BurnBot for wildfire prevention appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: Mike Oitzman
Source: Robotics Business Review Nov 14, 2025
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A2RL autonomous racecars take to the track in Abu Dhabi

The second A2RL autonomous racing event in the United Arab Emirates is challenging university teams' AI drivers. The post A2RL autonomous racecars take to the track in Abu Dhabi appeared first on The Robot Report.

Robotics intermediate BusinessMarket Trends
By: Mike Oitzman
Source: Robotics Business Review Nov 15, 2025
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