Robotics

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Overview

Robotics brings together mechanical design, sensing, perception, planning, and control so machines can act in changing physical environments, from homes and factories to fields, hospitals, and space. Approaches range from geometric models, feedback and model-predictive control, and motion planning to reinforcement learning, imitation learning, and vision-language-action models; many systems combine these with specialized hardware such as soft, aerial, legged, or continuum robots. Simulation, shared datasets, and benchmarks help compare methods, while real-robot experiments test whether they transfer across embodiments, conditions, and long tasks. A central challenge is making learned systems reliable, safe, and efficient outside controlled demonstrations, where uncertainty, contact, hardware limits, and human needs all matter.

Latest papers

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755 papers77 papers in the last four weeks, down 33% on the four weeks before. 0.4% of all new papers.

Apr 17, 2026

Limits of Lamarckian Evolution Under Pressure of Morphological Novelty

Lamarckian inheritance has been shown to be a powerful accelerator in systems where the joint evolution of robot morphologies and controllers is enhanced with individual learning. Its defining advantage lies in the offspring inheriting controllers learned by their parents. The efficacy of this option, however, relies on morphological similarity between parent and offspring. In this study, we examine how Lamarckian inheritance performs when the search process is driven toward high morphological variance, potentially straining the requirement for parent-offspring similarity. Using a system of modular robots that can evolve and learn to solve a locomotion task, we compare Darwinian and Lamarckian evolution to determine how they respond to shifting from pure task-based selection to a multi-objective pressure that also rewards morphological novelty. Our results confirm that Lamarckian evolution outperforms Darwinian evolution when optimizing task-performance alone. However, introducing selection pressure for morphological diversity causes a substantial performance drop, which is much greater in the Lamarckian system. Further analyses show that promoting diversity reduces parent-offspring similarity, which in turn reduces the benefits of inheriting controllers learned by parents. These results reveal the limits of Lamarckian evolution by exposing a fundamental trade-off between inheritance-based exploitation and diversity-driven exploration.

Jed R Muff, Karine Miras, A. E. Eiben

Department of Computer Science Vrije Universiteit Amsterdam, The Netherlands

RoboticsEvolutionary Robotics
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Apr 17, 2026

Factor Graph-Based Continuum Robot State Estimation with Magnus Expansion Priors

Reconstructing the shape of continuum manipulators from sparse, noisy sensor data is a challenging task, owing to the infinite-dimensional nature of such systems. Existing approaches broadly trade off between parametric methods that yield compact state representations but lack probabilistic structure, and Cosserat rod inference on factor graphs, which provides principled uncertainty quantification at the cost of a state dimension that grows with the spatial discretization. This letter combines the strengths of both these approaches by estimating the coefficients of a compact Geometric Variable Strain (GVS) parameterization within a factor graph framework. A novel kinematic factor, derived from the Magnus expansion of the strain field, encodes the closed-form rod geometry as a prior constraint linking the GVS strain coefficients to the backbone pose variables. The resulting formulation yields a compact state vector, is directly amenable to model-based control, and it retains the modularity, probabilistic treatment, and computational efficiency of factor graph inference. The proposed method is evaluated on both simulated and experimental data and compared against a Gaussian Process (GP) regression baseline, demonstrating increased accuracy in partial and sparse measurement scenarios while preserving computational efficiency.

Lorenzo Ticozzi, Sabyasachi Dash, Yangkun Liu +2

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Factor Graph OptimizationRoboticsRobot State EstimationContinuum Robot Shape Estimation
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Apr 16, 2026

Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios

Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical methods are often compared on small ad hoc examples or on rectangular grids. This paper presents a reproducible benchmark of deterministic single-vehicle coverage path planning heuristics on irregular hexagonal graphs derived from synthetic but maritime-motivated areas of interest. The benchmark contains 10,000 Hamiltonian-feasible instances spanning compact, elongated, and irregular morphologies, 17 heuristics from seven families, and a common evaluation protocol covering Hamiltonian success, complete-coverage success, revisits, path length, heading changes, and CPU latency. Across the released dataset, heuristics with explicit shortest-path reconnection solve the relaxed coverage task reliably but almost never produce zero-revisit tours. Exact Depth-First Search confirms that every released instance is Hamiltonian-feasible. The strongest classical Hamiltonian baseline is a Warnsdorff variant that uses an index-based tie-break together with a terminal-inclusive residual-degree policy, reaching 79.0% Hamiltonian success. The dominant design choice is not tie-breaking alone, but how the residual degree is defined when the endpoint is reserved until the final move. This shows that underreported implementation details can materially affect performance on sparse geometric graphs with bottlenecks. The benchmark is intended as a controlled testbed for heuristic analysis rather than as a claim of operational optimality at fleet scale.

Carlos S. Sepúlveda, Gonzalo A. Ruz

Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile · Millennium Nucleus for Social Data Science (SODAS) · Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning)

RoboticsGraph-Based PlanningMotion PlanningCoverage Path PlanningRobot Motion Planning
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Apr 16, 2026

POMDP-based Object Search with Growing State Space and Hybrid Action Domain

Efficiently locating target objects in complex indoor environments with diverse furniture, such as shelves, tables, and beds, is a significant challenge for mobile robots. This difficulty arises from factors like localization errors, limited fields of view, and visual occlusion. We address this by framing the object-search task as a highdimensional Partially Observable Markov Decision Process (POMDP) with a growing state space and hybrid (continuous and discrete) action spaces in 3D environments. Based on a meticulously designed perception module, a novel online POMDP solver named the growing neural process filtered k-center clustering tree (GNPF-kCT) is proposed to tackle this problem. Optimal actions are selected using Monte Carlo Tree Search (MCTS) with belief tree reuse for growing state space, a neural process network to filter useless primitive actions, and k-center clustering hypersphere discretization for efficient refinement of high-dimensional action spaces. A modified upper-confidence bound (UCB), informed by belief differences and action value functions within cells of estimated diameters, guides MCTS expansion. Theoretical analysis validates the convergence and performance potential of our method. To address scenarios with limited information or rewards, we also introduce a guessed target object with a grid-world model as a key strategy to enhance search efficiency. Extensive Gazebo simulations with Fetch and Stretch robots demonstrate faster and more reliable target localization than POMDP-based baselines and state-of-the-art (SOTA) non-POMDP-based solvers, especially large language model (LLM) based methods, in object search under the same computational constraints and perception systems. Real-world tests in office environments confirm the practical applicability of our approach. Project page: https://sites.google.com/view/gnpfkct.

Yongbo Chen, Hesheng Wang, Shoudong Huang +1

School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China · School of Computing, Australian National University (ANU), Canberra, ACT, 2601, Australia (The main part of this work is completed at ANU) · Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, People’s Republic of China +1

Mobile RoboticsMonte Carlo Tree SearchRoboticsPartially Observable Markov Decision ProcessesRobotic Object Search
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Apr 16, 2026

An Intelligent Robotic and Bio-Digestor Framework for Smart Waste Management

Rapid urbanization and continuous population growth have made municipal solid waste management increasingly challenging. These challenges highlight the need for smarter and automated waste management solutions. This paper presents the design and evaluation of an integrated waste management framework that combines two connected systems, a robotic waste segregation module and an optimized bio-digestor. The robotic waste segregation system uses a MyCobot 280 Jetson Nano robotic arm along with YOLOv8 object detection and robot operating system (ROS)-based path planning to identify and sort waste in real time. It classifies waste into four different categories with high precision, reducing the need for manual intervention. After segregation, the biodegradable waste is transferred to a bio-digestor system equipped with multiple sensors. These sensors continuously monitor key parameters, including temperature, pH, pressure, and motor revolutions per minute. The Particle Swarm Optimization (PSO) algorithm, combined with a regression model, is used to dynamically adjust system parameters. This intelligent optimization approach ensures stable operation and maximizes digestion efficiency under varying environmental conditions. System testing under dynamic conditions demonstrates a sorting accuracy of 98% along with highly efficient biological conversion. The proposed framework offers a scalable, intelligent, and practical solution for modern waste management, making it suitable for both residential and industrial applications.

Radhika Khatri, Adit Tewari, Nikhil Sharma +1

BITS Pilani Dubai Campus

RoboticsParticle Swarm Optimization
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Apr 16, 2026

DigiForest: Digital Analytics and Robotics for Sustainable Forestry

Covering one third of Earth's land surface, forests are vital to global biodiversity, climate regulation, and human well-being. In Europe, forests and woodlands reach approximately 40% of land area, and the forestry sector is central to achieving the EU's climate neutrality and biodiversity goals; these emphasize sustainable forest management, increased use of long-lived wood products, and resilient forest ecosystems. To meet these goals and properly address their inherent challenges, current practices require further innovation. This chapter introduces DigiForest, a novel, large-scale precision forestry approach leveraging digital technologies and autonomous robotics. DigiForest is structured around four main components: (1) autonomous, heterogeneous mobile robots (aerial, legged, and marsupial) for tree-level data collection; (2) automated extraction of tree traits to build forest inventories; (3) a Decision Support System (DSS) for forecasting forest growth and supporting decision-making; and (4) low-impact selective logging using purpose-built autonomous harvesters. These technologies have been extensively validated in real-world conditions in several locations, including forests in Finland, the UK, and Switzerland.

Marco Camurri, Enrico Tomelleri, Matías Mattamala +18

Dipartimento di Ingegneria Industriale, University of Trento, via Sommarive 9, 38123 Trento (TN), Italy · Faculty of Engineering, Free University of Bozen-Bolzano, via Bruno Buozzi 1, 39100 Bozen-Bolzano (BZ), Italy · Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, piazza Universit`a 5, 39100 Bozen-Bolzano (BZ), Italy +9

Robotic HarvestingRobotics
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Apr 15, 2026

ESCAPE: Episodic Spatial Memory and Adaptive Execution Policy for Long-Horizon Mobile Manipulation

Coordinating navigation and manipulation with robust performance is essential for embodied AI in complex indoor environments. However, as tasks extend over long horizons, existing methods often struggle due to catastrophic forgetting, spatial inconsistency, and rigid execution. To address these issues, we propose ESCAPE (Episodic Spatial Memory Coupled with an Adaptive Policy for Execution), operating through a tightly coupled perception-grounding-execution workflow. For robust perception, ESCAPE features a Spatio-Temporal Fusion Mapping module to autoregressively construct a depth-free, persistent 3D spatial memory, alongside a Memory-Driven Target Grounding module for precise interaction mask generation. To achieve flexible action, our Adaptive Execution Policy dynamically orchestrates proactive global navigation and reactive local manipulation to seize opportunistic targets. ESCAPE achieves state-of-the-art performance on the ALFRED benchmark, reaching 65.09% and 60.79% success rates in test seen and unseen environments with step-by-step instructions. By reducing redundant exploration, our ESCAPE attains substantial improvements in path-length-weighted metrics and maintains robust performance (61.24% / 56.04%) even without detailed guidance for long-horizon tasks.

Jingjing Qian, Zeyuan He, Chen Shi +2

The Chinese University of Hong Kong, Shenzhen · LiAuto Inc.

Robotic Spatial MemoryLong-Horizon Robotic ManipulationMobile ManipulationRoboticsRobot Policy Learning
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Apr 15, 2026

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

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.

Xin Yang, Dongliang Fan, Yunteng Ma +5

Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China. · SUSTech Institute of Robotics Research, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China. · Fuwai Hospital CAMS&PUMC, Beijing, 100037, China.

RoboticsRobotic Endovascular InterventionSoft Robotics
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Apr 14, 2026

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

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.

Parham Kebria, Soheil Sabri, Laura J Brattain

Electrical and Computer Engineering / North Carolina A&T State University / Greensboro, USA · Urban Digital Twin Lab, School of Modeling Simulation and Training / University of Central Florida / Orlando, USA · Department of Internal Medicine / College of Medicine, University of Central Florida / Orlando, USA

Digital TwinsRoboticsRobot Teleoperation
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Apr 14, 2026

Evolving the Complete Muscle: Efficient Morphology-Control Co-design for Musculoskeletal Locomotion

Musculoskeletal robots offer intrinsic compliance and flexibility, providing a promising paradigm for versatile locomotion. However, existing research typically relies on models with fixed muscle physiological parameters. This static physical setting fails to accommodate the diverse dynamic demands of complex tasks, inherently limiting the robot's performance upper bound. In this work, we focus on the morphology and control co-design of musculoskeletal systems. Unlike previous studies that optimize single physiological attributes such as stiffness, we introduce a Complete Musculoskeletal Morphological Evolution Space that simultaneously evolves muscle strength, velocity, and stiffness. To overcome the exponential expansion of the exploration space caused by this comprehensive evolution, we propose Spectral Design Evolution (SDE), a high-efficiency co-optimization framework. By integrating a bilateral symmetry prior with Principal Component Analysis (PCA), SDE projects complex muscle parameters onto a low-dimensional spectral manifold, enabling efficient morphological exploration. Evaluated on the MyoSuite framework across four tasks (Walk, Stair, Hilly, and Rough terrains), our method demonstrates superior learning efficiency and locomotion stability compared to fixed-morphology and standard evolutionary baselines.

Lidong Sun, Wentao Zhao, Ye Wang +2

Naval University of Engineering, Wuhan 430033, China · Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China · Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China

Robot Morphology-Control Co-DesignRoboticsRobot LocomotionEvolutionary Robotics
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Apr 14, 2026

Designing for Error Recovery in Human-Robot Interaction

This position paper looks briefly at the way we attempt to program robotic AI systems. Many AI systems are based on the idea of trying to improve the performance of one individual system to beyond so-called human baselines. However, these systems often look at one shot and one-way decisions, whereas the real world is more continuous and interactive. Humans, however, are often able to recover from and learn from errors - enabling a much higher rate of success. We look at the challenges of building a system that can detect/recover from its own errors, using the example of robotic nuclear gloveboxes as a use case to help illustrate examples. We then go on to talk about simple starting designs.

Christopher D. Wallbridge, Erwin Jose Lopez Pulgarin

Cardiff University, United Kingdom · The University of Manchester, Manchester, United Kingdom

Robot Failure RecoveryRoboticsHuman-Robot InteractionRobot Failure Detection
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Apr 14, 2026

HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models

Vision-Language-Action (VLA) models inherit rich world knowledge from vision-language backbones and acquire executable skills from action demonstrations. Yet current evaluations primarily measure task completion, leaving the semantic safety of learned action policies underexplored. This gap creates a critical vulnerability: a policy may execute the intended action correctly while producing unsafe outcomes when the surrounding visual-linguistic context changes. We present HazardArena, a benchmark for stress-testing semantic safety in VLA systems. Its core design is a set of safe/unsafe twin scenarios: paired environments with matched objects, layouts, and action requirements, but different semantic risk contexts. This controlled contrast isolates safety judgment from motor capability and directly tests whether a VLA can recognize when an otherwise valid action becomes hazardous. HazardArena includes over 2,000 assets and 51 risk-sensitive tasks across seven safety categories grounded in robotic safety standards. Across four representative VLA backbones, we observe a consistent and alarming pattern: safe-only fine-tuning improves benign task success while also increasing hazardous execution on matched unsafe scenarios. Physical-world experiments confirm that this failure transfers beyond simulation. These results show that stronger action execution does not imply safer behavior, and motivate semantic-risk-aware evaluation and enforcement as first-class requirements for real-world VLA deployment. Code released at https://github.com/HazardArena-Team/HazardArena ; updated code availability information.

Zixing Chen, Yifeng Gao, Li Wang +8

Fudan University · City University of Hong Kong · Shanghai Innovation Institute

RoboticsAI Safety EvaluationLLM SafetyVision-Language-Action ModelsSafety Evaluation of VLA Models
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Apr 14, 2026

Robotic Nanoparticle Synthesis via Solution-based Processes

We present a screw geometry-based manipulation planning framework for the robotic automation of solution-based synthesis, exemplified through the preparation of gold and magnetite nanoparticles. The synthesis protocols are inherently long-horizon, multi-step tasks, requiring skills such as pick-and-place, pouring, turning a knob, and periodic visual inspection to detect reaction completion. A central challenge is that some skills, notably pouring, transferring containers with solutions, and turning a knob, impose geometric and kinematic constraints on the end-effector motion. To address this, we use a programming by demonstration paradigm where the constraints can be extracted from a single demonstration. This combination of screw-based motion representation and demonstration-driven specification enables domain experts, such as chemists, to readily adapt and reprogram the system for new experimental protocols and laboratory setups without requiring expertise in robotics or motion planning. We extract sequences of constant screws from demonstrations, which compactly encode the motion constraints while remaining coordinate-invariant. This representation enables robust generalization across variations in grasp placement and allows parameterized reuse of a skill learned from a single example. By composing these screw-parameterized primitives according to the synthesis protocol, the robot autonomously generates motion plans that execute the complete experiment over repeated runs. Our results highlight that screw-theoretic planning, combined with programming by demonstration, provides a rigorous and generalizable foundation for long-horizon laboratory automation, thereby enabling fundamental kinematics to have a translational impact on the use of robots in developing scalable solution-based synthesis protocols.

Dasharadhan Mahalingam, Michael Gallagher, Nilanjan Chakraborty +1

Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY 11794, USA · Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA

RoboticsLong-Horizon Robotic ManipulationRobot Motion Planning
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Apr 13, 2026

Complementarity by Construction: A Lie-Group Approach to Solving Quadratic Programs with Linear Complementarity Constraints

Many problems in robotics require reasoning over a mix of continuous dynamics and discrete events, such as making and breaking contact in manipulation and locomotion. These problems are locally well modeled by linear complementarity quadratic programs (LCQPs), an extension to QPs that introduce complementarity constraints. While very expressive, LCQPs are non-convex, and few solvers exist for computing good local solutions for use in planning pipelines. In this work, we observe that complementarity constraints form a Lie group under infinitesimal relaxation, and leverage this structure to perform on-manifold optimization. We introduce a retraction map that is numerically well behaved, and use it to parameterize the constraints so that they are satisfied by construction. The resulting solver avoids many of the classical issues with complementarity constraints. We provide an open-source solver, Marble, that is implemented in C++ with Julia and Python bindings. We demonstrate that Marble is competitive on a suite of benchmark problems, and solves a number of robotics problems where existing approaches fail to converge.

Arun L. Bishop, Micah I. Reich, Zachary Manchester

Authors are with the Robotics Institute, Carnegie Mellon University · Author is with the Department of Aeronautics and Astronautics, Massachusetts Institute of Technology

Constrained OptimizationQuadratic ProgrammingRobotics
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Apr 13, 2026

Performance Characterization of Frequency-Selective Wireless Power Transfer Toward Scalable Untethered Magnetic Actuation

Frequency-selective wireless power transfer provides a feasible route to enable independent actuation and control of multiple untethered robots in a common workspace; however, the scalability remains unquantified, particularly the maximum number of resonators that can be reliably addressed within a given frequency bandwidth. To address this, we formulate the relationship between resonator quality factor (Q-factor) and the number of individually addressable inductor-capacitor (LC) resonant energy harvesters within a fixed radio-frequency (RF) spectrum, and we convert selectively activated harvested energy into mechanical motion. We theoretically proved and experimentally demonstrated that scalability depends primarily on the Q-factor. For this proof-of-concept study, we define effective series resistance as a function of frequency allocating bandwidths to discrete actuators. We provide design equations for scaling untethered magnetic actuation with Q-factor optimization. Resonator networks spanning bandwidths from 100kHz to 1MHz were analyzed to quantify how increasing the number of resonators affects independent addressability. We validated the approach experimentally by fabricating three centimeter-scale untethered actuators that selectively trigger the motion of mechanical beams at 734kHz, 785kHz, and 855kHz. We also characterized the generated mechanical force and the activation bandwidth of each actuator, confirming that no unintended cross-triggering occurred.

Gabriel Cooper, Xiaolong Liu

Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, USA · Department of Mechanical Engineering, Texas Tech University, Lubbock, USA

Robotics
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Apr 13, 2026

Micro-Dexterity in Biological Micromanipulation: Embodiment, Perception, and Control

Microscale manipulation has advanced substantially in controlled locomotion and targeted transport, yet many biomedical applications require precise and adaptive interaction with biological micro-objects. At these scales, manipulation is realized through three main classes of platforms: embodied microrobots that physically interact as mobile agents, field-mediated systems that generate contactless trapping or manipulation forces, and externally actuated end-effectors that interact through remotely driven physical tools. Unlike macroscale manipulators, these systems function in fluidic, confined, and surface-dominated environments characterized by negligible inertia, dominant interfacial forces, and soft, heterogeneous, and fragile targets. Consequently, classical assumptions of dexterous manipulation, including rigid-body contact, stable grasping, and rich proprioceptive feedback, become difficult to maintain. This review introduces micro-dexterity as a framework for analyzing biological micromanipulation through the coupled roles of embodiment, perception, and control. We examine how classical manipulation primitives, including pushing, reorientation, grasping, and cooperative manipulation, are reformulated at the microscale; compare the architectures that enable them, from contact-based micromanipulators to contactless field-mediated systems and cooperative multi-agent platforms; and review the perception and control strategies required for task execution. We identify the current dexterity gap between laboratory demonstrations and clinically relevant biological manipulation, and outline key challenges for future translation.

Kangyi Lu, Lan Wei, Zongcai Tan +1

Department of Bioengineering, Imperial-X AI Initiative, Imperial College London, London, United Kingdom

Dexterous ManipulationRobotic ManipulationRobotics
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Apr 13, 2026

Learning Racket-Ball Bounce Dynamics Across Diverse Rubbers for Robotic Table Tennis

Accurate dynamic models for racket-ball bounces are essential for reliable control in robotic table tennis. Existing models typically assume simple linear models and are restricted to inverted rubbers, limiting their ability to generalize across the wide variety of rackets encountered in practice. In this work, we present a unified framework for modeling ball-racket interactions across 10 racket configurations featuring different rubber types, including inverted, anti-spin, and pimpled surfaces. Using a high-speed multi-camera setup with spin estimation, we collect a dataset of racket-ball bounces spanning a broad range of incident velocities and spins. We show that key physical parameters governing rebound, such as the Coefficient of Restitution and tangential impulse response, vary systematically with the impact state and differ significantly across rubbers. To capture these effects while preserving physical interpretability, we estimate the parameters of an impulse-based contact model using Gaussian Processes conditioned on the ball's incoming velocity and spin. The resulting model provides both accurate predictions and uncertainty estimations. Compared to the constant parameter baselines, our approach reduces post-impact velocity and spin prediction errors across all racket types, with the largest improvements observed for nonstandard rubbers. Furthermore, the GP-based model enables online identification of racket dynamics with few observations during gameplay.

Thomas Gossard

Robotic Table TennisRobotics
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Apr 13, 2026

3D-Anchored Lookahead Planning for Persistent Robotic Scene Memory via World-Model-Based MCTS

We present 3D-Anchored Lookahead Planning (3D-ALP), a System 2 reasoning engine for robotic manipulation that combines Monte Carlo Tree Search (MCTS) with a 3D-consistent world model as the rollout oracle. Unlike reactive policies that evaluate actions from the current camera frame only, 3D-ALP maintains a persistent camera-to-world (c2w) anchor that survives occlusion, enabling accurate replanning to object positions that are no longer directly observable. On a 5-step sequential reach task requiring spatial memory (Experiment E3), 3D-ALP achieves 0.650 0.109 success rate on memory-required steps versus 0.006 0.008 for a greedy reactive baseline (Δ=+0.645), while step 5 success reaches 0.822 against 0.000 for greedy. An ablation study (30 episodes, 3 seeds) isolates tree search spatial memory as the primary driver (+0.533, 82% of gain) with additional benefit from deeper lookahead (+0.111, 17%). We also identify and resolve four structural failure modes in applying UCT-MCTS (Upper Confidence Bounds applied to Trees [10]) to continuous robotic manipulation.

Bronislav Sidik, Dror Mizrahi

Huawei Technologies, Israel R&D Center

Robotic Spatial MemoryRoboticsRobotic ManipulationRobot Motion PlanningWorld Model-Based Planning
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Apr 12, 2026

Positioning radiata pine branches requiring pruning by drone stereo vision

This paper presents a stereo-vision-based system mounted on a drone for detecting and localising radiata pine branches to support autonomous pruning. The proposed pipeline comprises two stages: branch segmentation and depth estimation. For segmentation, YOLOv8, YOLOv9, and Mask R-CNN variants are compared on a custom dataset of 71 stereo image pairs captured with a ZED Mini camera. For depth estimation, both a traditional method (SGBM with WLS filtering) and deep-learning-based methods (PSMNet, ACVNet, GWCNet, MobileStereoNet, RAFT-Stereo, and NeRF-Supervised Deep Stereo) are evaluated. A centroid-based triangulation algorithm with MAD outlier rejection is proposed to compute branch distance from the segmentation mask and disparity map. Qualitative evaluation at distances of 1-2 m indicates that the deep learning-based disparity maps produce more coherent depth estimates than SGBM, demonstrating the feasibility of low-cost stereo vision for automated branch positioning in forestry.

Yida Lin, Bing Xue, Mengjie Zhang +2

Centre of Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand · Computer Vision Research Lab & Department of Computer Science and Software Engineering, Canterbury University, PO Box 8041, Christchurch, New Zealand

Stereo VisionDepth EstimationRobotics
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Apr 10, 2026

CableTract: A Co-Designed Cable-Driven Field Robot for Low-Compaction, Off-Grid Capable Agriculture

Conventional field operations spend most of their energy moving the tractor body, not the implement. Yet feasibility studies for novel agricultural vehicles rarely tie mechanics, energy harvest, draft, field geometry, economics, life-cycle CO2, and uncertainty quantification together on a single reproducible code path. This paper builds such a framework and applies it to CableTract, a two-module cable-driven field robot. A stationary Main Unit (winch + motor + battery + harvester module) (MU) and a lighter Anchor module (held by helical screw piles) tension a cable across a strip while a lightweight implement carriage rolls along it. The heavy bodies stay on the headland; only the carriage enters the field. The carriage runs a 10-implement library co-designed for the cable architecture. This co-design is the paper's central analytical lever. The framework is prototype-free. It chains a catenary cable model, a drivetrain efficiency chain, a stochastic draft model fitted to the co-designed library, an hourly solar + wind + battery simulator on six sites, a polygon coverage planner on a 50-field corpus, a contact-pressure compaction model, a discounted cash-flow economics engine with battery replacement and life-cycle CO2, and a global sensitivity analysis on 20 inputs. An operating-envelope sweep and an architectural-variant comparison close the loop. The full implementation is open source. Applied to the codesigned reference, the framework yields energy, compaction advantages and potential off-grid operation.

Ozgur Yilmaz

Adana Science and Technology University, Adana, Türkiye

RoboticsHardware-Software Co-DesignAgricultural Robotics
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Apr 10, 2026

Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks

Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice due to inherent nonlinearities, friction, and hysteresis, which complicate modeling and control. So far, these challenges have hindered policy transfer from simulation to real systems. To bridge this gap, we propose a sim-to-real pipeline that learns a neural network model of this complex actuation and leverages established rigid body simulation for the arm dynamics and interactions with the environment. Our method, called Generalized Actuator Network (GenAN), enables actuation model identification across a wide range of robots by learning directly from joint position trajectories rather than requiring torque sensors. Using GenAN on PAMY2, a tendon-driven robot powered by pneumatic artificial muscles, we successfully deploy dynamic but precise goal-reaching, ball-in-a-cup, and table tennis policies, trained entirely in simulation. To the best of our knowledge, this result constitutes the first successful sim-to-real transfer for a four-degrees-of-freedom muscle-actuated robot arm.

Jan Schneider, Mridul Mahajan, Le Chen +4

Max Planck Institute for Intelligent Systems, Tübingen · Boston University · ELLIS Institute Tübingen +4

Sim-to-Real TransferRoboticsRobotic ControlRobot Dynamics IdentificationTendon-Driven Actuation
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Apr 10, 2026

On the Terminology and Geometric Aspects of Redundant Parallel Manipulators

Parallel kinematics machines (PKM) can exhibit kinematic as well as actuation redundancy. While the meaning of kinematic redundancy has been clarified already for serial manipulators, actuation redundancy, that is only possible in PKM, is differently classified in the literature. In this paper a consistent terminology for general redundant PKM is proposed. A kinematic model is introduced with the configuration space (c-space) as central part. The notion of kinematic redundancy is recalled for PKM. C-space, output, and input singularities are distinguished. The significance of the c-space geometry is emphasized, and it is pointed out geometrically that input singularities can be avoided by redundant actuation schemes. In order to distinguish different actuation schemes of PKM a non-linear control system is introduced whose dynamics evolves on the c-space. The degree of actuation (DOA) is introduced as the number of independent control vector fields, and PKM are classified as full-actuated and underactuated. Relating this DOA to the degree of freedom (DOF) allows to classify the actuation redundancy.

Andreas Mueller

Chair of Mechanics and Robotics, University Duisburg-Essen, Lotharstrasse 1, 47057 Duisburg, Germany

Robotics
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Apr 10, 2026

ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents

Embodied agents increasingly rely on modular capabilities that are installed, upgraded, composed, and governed at runtime, yet the interfaces between these modules are specified only at the level of message types, so integration failures surface only during execution. We present ECM Contracts, a contract-based interface model for embodied capability modules. Unlike conventional interfaces that specify only input and output types, ECM Contracts encode six dimensions of embodied execution: functional signature, behavioral assumptions, resource requirements, permission boundaries, recovery semantics, and version compatibility. On this model we build a compatibility framework that checks installation, composition, and upgrade before deployment, and a release discipline of version-aware compatibility classes and upgrade gates. We evaluate the prototype by predicting real, independently documented integration failures in the ROS ecosystem: contracts are reconstructed blind from each module's published interface, scored by a checker frozen before reconstruction against bugs from third-party datasets, and confirmed in live runtime execution. Contract checking predicts 56% and 72% of these documented failures across two substrates, against at most 17% for the strongest type and quality-of-service baselines, with the advantage statistically significant and zero false positives on matched-good controls. The resource and version dimensions carry most of this margin; the behavioral dimension adds little beyond the middleware's quality-of-service check, and we report the permission and recovery dimensions as forward-looking. Stable embodied software ecosystems require not just modular packaging but explicit contracts connecting composition, governance, and evolution.

Xue Qin, Simin Luan, Cong Yang +1

School of Software, Harbin Institute of Technology, Harbin, China. · School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. · School of Future Science and Engineering, Soochow University, Suzhou,2026 China.

Software EngineeringRoboticsAI Agent GovernanceEmbodied AI
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Apr 9, 2026

One Interface, Many Robots: Unified Real-Time Low-Level Motion Planning for Collaborative Arms

This paper proposes a common interface for real-time low-level motion planning of collaborative robotic arms, aimed at enabling broader applicability and improved portability across heterogeneous hardware platforms. In previous work, we introduced WinGs Operating Studio (WOS), a middleware solution that abstracts diverse robotic components into uniform software resources and provides a broad suite of language-agnostic APIs. This paper specifically focuses on its minimal yet flexible interface for real-time end-effector trajectory control. By employing an n-degree polynomial interpolator in conjunction with a quadratic programming solver, the proposed method generates smooth, continuously differentiable trajectories with precise position, velocity, and acceleration profiles. We validate our approach in three distinct scenarios. First, in an offline demonstration, a collaborative arm accurately draws various geometric shapes on paper. Second, in an interruptible, low-frequency re-planning setting, a robotic manipulator grasps a dynamic object placed on a moving mobile robot. Finally, we conducted a teleoperation experiment in which one robotic arm controlled another to perform a series of dexterous manipulations, confirming the proposed method's reliability, versatility, and ease of use.

Yue Feng, Weicheng Huang, I-Ming Chen

Robotics Research Center (RRC), Nanyang Technological University, Singapore, 639798. · WinGs Robotics LLC, NY, 10314 USA.

RoboticsRobot Motion PlanningReal-Time Robot ControlRobot Motion GenerationTrajectory Generation
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Apr 9, 2026

Exploring Temporal Representation in Neural Processes for Multimodal Action Prediction

Inspired by the human ability to understand and predict others, we study the applicability of Conditional Neural Processes (CNP) to the task of self-supervised multimodal action prediction in robotics. Following recent results regarding the ontogeny of the Mirror Neuron System (MNS), we focus on the preliminary objective of self-actions prediction. We find a good MNS-inspired model in the existing Deep Modality Blending Network (DMBN), able to reconstruct the visuo-motor sensory signal during a partially observed action sequence by leveraging the probabilistic generation of CNP. After a qualitative and quantitative evaluation, we highlight its difficulties in generalizing to unseen action sequences, and identify the cause in its inner representation of time. Therefore, we propose a revised version, termed DMBN-Positional Time Encoding (DMBN-PTE), that facilitates learning a more robust representation of temporal information, and provide preliminary results of its effectiveness in expanding the applicability of the architecture. DMBN-PTE figures as a first step in the development of robotic systems that autonomously learn to forecast actions on longer time scales refining their predictions with incoming observations.

Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea +1

DIBRIS Department, University of Genoa, Via All’Opera Pia 13 16145 Genoa, Italy · CONTACT Unit, Italian Institute of Technology, Via Enrico Melen 83, 16152 Genoa, Italy · International Research Center for Neurointelligence, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Temporal Representation LearningRoboticsRepresentation LearningMultimodal LearningConditional Generative Modeling
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Apr 9, 2026

State and Trajectory Estimation of Tensegrity Robots via Factor Graphs and Chebyshev Polynomials

Tensegrity robots offer compliance and adaptability, but their nonlinear, and underconstrained dynamics make state estimation challenging. Reliable continuous-time estimation of all rigid links is crucial for closed-loop control, system identification, and machine learning; however, conventional methods often fall short. This paper proposes a two-stage approach for robust state or trajectory estimation (i.e., filtering or smoothing) of a cable-driven tensegrity robot. For online state estimation, this work introduces a factor-graph-based method, which fuses measurements from an RGB-D camera with on-board cable length sensors. To the best of the authors' knowledge, this is the first application of factor graphs in this domain. Factor graphs are a natural choice, as they exploit the robot's structural properties and provide effective sensor fusion solutions capable of handling nonlinearities in practice. Both the Mahalanobis distance-based clustering algorithm, used to handle noise, and the Chebyshev polynomial method, used to estimate the most probable velocities and intermediate states, are shown to perform well on simulated and real-world data, compared to an ICP-based algorithm. Results show that the approach provides high fidelity, continuous-time state and trajectory estimates for complex tensegrity robot motions.

Edgar Granados, Patrick Meng, Charles Tang +4

Dept. of Computer Science, Rutgers University, NJ, USA. · Mech. Eng. & Material Sc. Yale University, New Haven, CT, USA.

Tensegrity RoboticsRobot State EstimationRoboticsRobust Estimation
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Apr 9, 2026

The Sustainability Gap in Robotics: A Large-Scale Survey of Sustainability Awareness in 50,000 Research Articles

We present a large-scale survey of sustainability communication and motivation in robotics research. Our analysis covers nearly 50,000 open-access papers from arXiv's cs.RO category published between 2015 and early 2026. In this study, we quantify how often papers mention social, ecological, and sustainability impacts, and we analyse their alignment with the UN Sustainable Development Goals (SDGs). The results reveal a persistent gap between the field's potential and its stated intent. While a large fraction of robotics papers can be mapped to SDG-relevant domains, explicit sustainability motivation remains remarkably low. Specifically, mentions of sustainability-related impacts are typically below 2%, explicit SDG references stay below 0.1%, and the proportion of sustainability-motivated papers remains below 5%. These trends suggest that while the field of robotics is advancing rapidly, sustainability is not yet a standard part of research framing. We conclude by proposing concrete actions for researchers, conferences, and institutions to close these awareness and motivation gaps, supporting a shift toward more intentional and responsible innovation.

Antun Skuric, Leandro Von Werra, Thomas Wolf

RoboticsScientific Communication
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Apr 8, 2026

Exploiting Aggregate Programming in a Multi-Robot Service Prototype

Multi-robot systems are becoming increasingly relevant within diverse application domains, such as healthcare, exploration, and rescue missions. However, building such systems is still a significant challenge, since it adds the complexities of the physical nature of robots and their environments to those inherent in coordinating any distributed (multi-agent) system. Aggregate Programming (AP) has recently emerged as a promising approach to engineering resilient, distributed systems with proximity-based communication, and is notably supported by practical frameworks. In this paper we present a prototype of a multi-robot service system, which adopts AP for the design and implementation of its coordination software. The prototype has been validated both with simulations, and with tests in a University library.

Giorgio Audrito, Andrea Basso, Daniele Bortoluzzi +3

Dipartimento di Informatica, Universita' di Torino · Università di Torino, Turin, Italy · MITO Technology +1

Service RoboticsMulti-Robot SystemsRoboticsMulti-Robot Coordination
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Apr 7, 2026

Designing Privacy-Preserving Visual Perception for Robot Navigation Based on User Privacy Preferences

Visual navigation is a fundamental capability of mobile service robots, yet the onboard cameras required for such navigation can capture privacy-sensitive information and raise user privacy concerns. Existing approaches to privacy-preserving navigation-oriented visual perception have largely been driven by technical considerations, with limited grounding in user privacy preferences. In this work, we propose a user-centered approach to designing privacy-preserving visual perception for robot navigation. To investigate how user privacy preferences can inform such design, we conducted two user studies. The results show that users prefer privacy-preserving visual abstractions and capture-time low-resolution preservation mechanisms: their preferred RGB resolution depends both on the desired privacy level and robot proximity during navigation. Based on these findings, we further derive a user-configurable distance-to-resolution privacy policy for privacy-preserving robot visual navigation.

Xuying Huang, Sicong Pan, Delphine Reinhardt +1

Humanoid Robots Lab, University of Bonn, the Lamarr Institute for Machine Learning and Artificial Intelligence and the Center for Robotics, Bonn, Germany · Computer Security and Privacy, University of Goettingen, Goettingen, Germany

Robot NavigationRoboticsRobotic Perception
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Apr 7, 2026

Delta6: A Low-Cost, 6-DOF Force-Sensing Flexible End-Effector

This paper presents Delta6, a low-cost, six-degree-of-freedom (6-DOF) force/torque end-effector that combines antagonistic springs with magnetic encoders to deliver accurate wrench sensing while remaining as simple to assemble as flat-pack furniture. A fully 3D-printed prototype, assembled entirely from off-the-shelf parts, withstands peak forces above +/-14.4 N and torques of +/-0.33 N.m per axis; these limits can be further extended by leveraging the proposed parametric analytical model. Without calibration, Delta6 attains a 99th-percentile error of 7% full scale (FS). With lightweight sequence models, the error is reduced to 3.8% FS by the best-performing network. Benchmarks on multiple computing platforms confirm that the device's bandwidth is adjustable, enabling balanced trade-offs among update rate, accuracy, and cost, while durability, thermal drift, and zero-calibration tests confirm its robustness. With Delta6 mounted on a robot arm governed by a force-impedance controller, the system successfully performs two contact-rich tasks: buffing curved surfaces and tight assemblies. Experiments validate the design, showing that Delta6 is a robust, low-cost alternative to existing 6-DOF force sensing solutions. Open-source site: https://wings-robotics.github.io/delta6 .

Yue Feng, Weicheng Huang, Chen Qiu +2

Robotics Research Center (RRC), Nanyang Technological University, Singapore, 639798. · WinGs Robotics LLC, NY, 10314 USA. · Maider Medical Industry Equipment Company, Ltd., Taizhou 317607, China. +1

RoboticsForce-Controlled Robotic Manipulation
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