Robotic Assembly

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Robotic assembly spans recognizing parts and their relationships, planning feasible sequences and motions, and controlling robots as components make contact. Common approaches combine CAD-based or symbolic planning with imitation learning and reinforcement learning; vision-language models help interpret instructions, while force, tactile, and compliant control guide precise insertion and recovery. Systems are tested on tasks such as peg-in-hole and connector insertion, NIST assembly boards, and longer furniture, industrial, and construction assemblies, often with human or multiple robots. Precise, short tasks can be reliable, but generalizing to unfamiliar parts and completing long sequences remains difficult because small perception or execution errors compound, and simulation performance may not transfer to hardware.

Latest papers

88 papers13 papers in the last four weeks, down 35% on the four weeks before. 0.1% of all new papers.

May 13, 2026

AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects

Assembling objects from parts requires understanding multimodal instructions, linking them to 3D components, and predicting physically plausible 6-DoF motions for each assembly step. Existing datasets focus on simplified scenarios, overlooking shape complexities and assembly trajectories in industrial assemblies. We introduce AssemblyBench, a synthetic dataset of 2,789 industrial objects with multimodal instruction manuals, corresponding 3D part models, and part assembly trajectories. We also propose a transformer-based model, AssemblyDyno, which uses the instructional manual and the 3D shape of each part to jointly predict assembly order and part assembly trajectories. AssemblyDyno outperforms prior works in both assembly pose estimation and trajectory feasibility, where the latter is evaluated by our physics-based simulations.

Danrui Li, Jiahao Zhang, Bernhard Egger +4

Rutgers, The State University of New Jersey, USA · The Australian National University, Australia · Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany +1

Object Pose Estimation Robot Manipulation Benchmarks Robotic Assembly Trajectory Prediction

May 12, 2026

SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy

Contact-rich assembly is fundamental in robotics but poses significant challenges due to uncertainties in relative poses, such as misalignments and small clearances in peg-in-hole tasks. Existing approaches typically address search and high-precision insertion separately, because these tasks involve distinct action patterns. However, supporting both tasks within a single model, without switching models or weights, is desirable for intelligent assembly systems. In this work, we propose SI-Diff, a framework that learns both search and high-precision insertion through a force-domain diffusion policy. To this end, we introduce a new mode-conditioning mechanism that enables the policy to capture distinct action behaviors under a single framework. Moreover, we develop a new search teacher policy that can generate diverse trajectories. By training on successful and efficient demonstrations provided by the teacher policy, the model learns the mapping from tactile and end-effector velocity observations to effective action behaviors. We conduct thorough experiments to show that SI-Diff extends the tolerance to x-y misalignments from 2 mm to 5 mm compared to the state-of-the-art baseline, TacDiffusion, while also demonstrating strong zero-shot transferability to unseen shapes.

Yibo Liu, Stanko Oparnica, Simon Shewchun-Jakaitis +5

Epson Canada, Markham, Ontario L3R 6G3, Canada · Queen’s University, Kingston, Ontario K7L 3N6, Canada

Contact-Rich Robotic Manipulation Diffusion Policy Robotic Insertion Robotic Assembly

May 8, 2026

BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly

Autonomous robotic assembly of interlocking bricks demands seamless integration of long-horizon task reasoning, spatial grounding, and fine-grained manipulation. This paper presents BrickCraft, a compositional framework designed for long-horizon and generalizable interlocking brick assembly. BrickCraft models the assembly process using a relative formulation, where each step is anchored to a reference brick within the partial structure, thereby decomposing complex tasks into a finite set of reusable primitive skills. BrickCraft bridges the gap between high-level assembly plans and physical execution through situated manuals, which provide explicit spatial guidance for learned visuomotor skills by projecting the assembly intent onto real-time robot observations. Finally, BrickCraft employs a modular execution pipeline that chains these spatially grounded skills to accomplish long-horizon assembly tasks. Extensive experimental validations demonstrate that BrickCraft acquires proficient assembly skills from a limited set of demonstrations and effectively composes them for diverse long-horizon assembly tasks. The project website is available at https://intelligent-control-lab.github.io/BrickCraft.

Jichuan Yu, Bowei Li, Zhenran Tang +4

Tsinghua University · Carnegie Mellon University

Robotic Construction Long-Horizon Robotic Manipulation Robot Skill Learning Generalization in Robotic Manipulation Visuomotor Policy Learning Robotic Manipulation+2

Apr 22, 2026

Visual-Tactile Peg-in-Hole Assembly Learning from Peg-out-of-Hole Disassembly

Peg-in-hole (PiH) assembly is a fundamental yet challenging robotic manipulation task. While reinforcement learning (RL) has shown promise in tackling such tasks, it requires extensive exploration. In this paper, we propose a novel visual-tactile skill learning framework for the PiH task that leverages its inverse task, i.e., peg-out-of-hole (PooH) disassembly, to facilitate PiH learning. Compared to PiH, PooH is inherently easier as it only needs to overcome existing friction without precise alignment, making data collection more efficient. To this end, we formulate both PooH and PiH as Partially Observable Markov Decision Processes (POMDPs) in a unified environment with shared visual-tactile observation space. A visual-tactile PooH policy is first trained; its trajectories, containing kinematic, visual and tactile information, are temporally reversed and action-randomized to provide expert data for PiH. In the policy learning, visual sensing facilitates the peg-hole approach, while tactile measurements compensate for peg-hole misalignment. Experiments across diverse peg-hole geometries show that the visual-tactile policy attains 6.4% lower contact forces than its single-modality counterparts, and that our framework achieves average success rates of 87.5% on seen objects and 77.1% on unseen objects, outperforming direct RL methods that train PiH policies from scratch by 18.1% in success rate. Demos, code, and datasets are available at https://sites.google.com/view/pooh2pih.

Yongqiang Zhao, Xuyang Zhang, Zhuo Chen +3

Department of Engineering, King’s College London, Strand, London, WC2R 2LS, United Kingdom · Department of Informatics, King’s College London, Strand, London, WC2R 2LS, United Kingdom

RL for Robotics Contact-Rich Robotic Manipulation Robotic Manipulation Robot Imitation Learning Robotic Assembly

Apr 10, 2026

AssemLM: A Spatial Reasoning Multimodal Large Language Model for Robotic Assembly

Spatial reasoning is a fundamental capability for embodied intelligence, especially for fine-grained manipulation tasks such as robotic assembly. Recent methods based on vision-language models (VLMs) largely rely on coarse 2D perception and struggle to perform accurate reasoning over complex 3D geometry. To address this limitation, we propose AssemLM, a spatial multimodal large language model for robotic assembly that integrates assembly manuals, point clouds, and textual instructions to predict task-critical 6D assembly poses with explicit geometric understanding. To bridge raw 3D perception and high-level linguistic reasoning, AssemLM employs a specialized point cloud encoder to extract fine-grained geometric and rotational features for accurate 3D spatial reasoning in assembly tasks. In addition, we introduce AssemBench, a large-scale benchmark for assembly-oriented spatial reasoning with over 900K multimodal samples and precise 6D pose annotations, extending evaluation from 2D grounding to full 3D geometric inference. Extensive experiments and real-robot evaluations demonstrate that AssemLM achieves state-of-the-art 6D pose reasoning performance and effectively supports fine-grained, multi-step assembly tasks in real-world settings. Code, models, and the AssemBench dataset will be made publicly available.

Zhi Jing, Jinbin Qiao, Ouyang Lu +5

Fudan University† · Institute of Artificial Intelligence (TeleAI), China Telecom† · Tianjin University +3

3D Spatial Reasoning Robotic Assembly 3D VLMs VLMs for Robotics Object Pose Estimation

Apr 8, 2026

Learning-Based Strategy for Composite Robot Assembly Skill Adaptation

Contact-rich robotic skills remain challenging for industrial robots due to tight geometric tolerances, frictional variability, and uncertain contact dynamics, particularly when using position-controlled manipulators. This paper presents a reusable and encapsulated skill-based strategy for peg-in-hole assembly, in which adaptation is achieved through Residual Reinforcement Learning (RRL). The assembly process is represented using composite skills with explicit pre-, post-, and invariant conditions, enabling modularity, reusability, and well-defined execution semantics across task variations. Safety and sample efficiency are promoted through RRL by restricting adaptation to residual refinements within each skill during contact-rich interactions, while the overall skill structure and execution flow remain invariant. The proposed approach is evaluated in MuJoCo simulation on a UR5e robot equipped with a Robotiq gripper and trained using SAC and JAX. Results demonstrate that the proposed formulation enables robust execution of assembly skills, highlighting its suitability for industrial automation.

Khalil Abuibaid, Aleksandr Sidorenko, Achim Wagner +1

Chair of Machine Tools and Control System, RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany · Innovative Factory Systems, German Institute of Artificial Intelligence, 67663 Kaiserslautern, Germany · Chair of Machine Tools and Control Systems, RPTU University Kaiserslautern-Landau, 67663 Kaiserslautern, Germany

Robot Skill Learning Robotic Assembly Contact-Rich Robotic Manipulation Industrial Robotics

Mar 31, 2026

Long-Reach Robotic Manipulation for Assembly and Outfitting of Lunar Structures

Future infrastructure construction on the lunar surface will require semi- or fully-autonomous operation from robots deployed at the build site. In particular, tasks such as electrical outfitting necessitate transport, routing, and fine manipulation of cables across large structures. To address this need, we present a compact and long-reach manipulator incorporating a deployable composite boom, capable of performing manipulation tasks across large structures and workspaces. We characterize the deflection, vibration, and blossoming characteristics inherent to the deployable structure, and present a manipulation control strategy to mitigate these effects. Experiments indicate an average endpoint accuracy error of less than 15 mm for boom lengths up to 1.8 m. We demonstrate the approach with a cable routing task to illustrate the potential for lunar outfitting applications that benefit from long reach.

Stanley Wang, Venny Kojouharov, Long Yin Chung +2

Stanford University · All authors are with the Dept. of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA.

Robotic Manipulation Space Robotics Robotic Construction Robotic Assembly

Mar 23, 2026

A Framework for Closed-Loop Robotic Assembly, Alignment and Self-Recovery of Precision Optical Systems

Robotic automation has transformed scientific workflows in domains such as chemistry and materials science, yet free-space optics, which is a high precision domain, remains largely manual. Optical systems impose strict spatial and angular tolerances, and their performance is governed by tightly coupled physical parameters, making generalizable automation particularly challenging. In this work, we present a robotics framework for the autonomous construction, alignment, and maintenance of precision optical systems. Our approach integrates hierarchical computer vision systems, optimization routines, and custom-built tools to achieve this functionality. As a representative demonstration, we perform the fully autonomous construction of a tabletop laser cavity from randomly distributed components. The system performs several tasks such as laser beam centering, spatial alignment of multiple beams, resonator alignment, laser mode selection, and self-recovery from induced misalignment and disturbances. By achieving closed-loop autonomy for highly sensitive optical systems, this work establishes a foundation for autonomous optical experiments for applications across technical domains.

Seou Choi, Sachin Vaidya, Caio Silva +4

Research Laboratory of Electronics, Massachusetts Institute of Technology, MA 02139, USA · NSF Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) and the Department of Physics, Massachusetts Institute of Technology, MA 02139, USA · Nokia Bell Labs +1

Robotic Assembly Laboratory Automation Robotic Control Robotic Manipulation

Mar 21, 2026

LASER: Level-Based Asynchronous Scheduling and Execution Regime for Spatiotemporally Constrained Multi-Robot Timber Manufacturing

Automating large-scale manufacturing in domains like timber construction requires multi-robot systems to manage tightly coupled spatiotemporal constraints, such as collision avoidance and process-driven deadlines. This paper introduces LASER (Level-based Asynchronous Scheduling and Execution Regime), a complete framework for scheduling and executing complex assembly tasks, demonstrated on a screw-press gluing application for timber slab manufacturing. Our central contribution is to integrate a barrier-based mechanism into a constraint programming (CP) scheduling formulation that partitions tasks into spatiotemporally disjoint sets, which we define as levels. This structure enables robots to execute tasks in parallel and asynchronously within a level, synchronizing only at level barriers, which guarantees collision-free operation by construction and provides robustness to timing uncertainties. To solve this formulation for large problems, we propose two specialized algorithms: an iterative temporal-relaxation approach for heterogeneous task sequences and a bi-level decomposition for homogeneous tasks that balances workload. We validate the LASER framework by fabricating a full-scale 2.4m x 6m timber slab with a two-robot system mounted on parallel linear tracks, successfully coordinating 108 subroutines and 352 screws under tight adhesive time windows. Computational studies show our method scales steadily with size compared to a monolithic approach.

Zhenxiang Huang, Lior Skoury, Tim Stark +4

Institute for Computational Design and Construction (ICD) and Cluster of Excellence IntCDC at University of Stuttgart, Germany.

Multi-Robot Systems Multi-Robot Coordination Robotic Assembly Robotic Construction Constraint Programming

Mar 19, 2026

ATG-MoE: Autoregressive trajectory generation with mixture-of-experts for assembly skill learning

Flexible manufacturing requires robot systems that can adapt to constantly changing tasks, objects, and environments. However, traditional robot programming is labor-intensive and inflexible, while existing learning-based assembly methods often suffer from weak positional generalization, complex multi-stage designs, and limited multi-skill integration capability. To address these issues, this paper proposes ATG-MoE, an end-to-end autoregressive trajectory generation method with mixture of experts for assembly skill learning from demonstration. The proposed method establishes a closed-loop mapping from multi-modal inputs, including RGB-D observations, natural language instructions, and robot proprioception to manipulation trajectories. It integrates multi-modal feature fusion for scene and task understanding, autoregressive sequence modeling for temporally coherent trajectory generation, and a mixture-of-experts architecture for unified multi-skill learning. In contrast to conventional methods that separate visual perception and control or train different skills independently, ATG-MoE directly incorporates visual information into trajectory generation and supports efficient multi-skill integration within a single model. We train and evaluate the proposed method on eight representative assembly skills from a pressure-reducing valve assembly task. Experimental results show that ATG-MoE achieves strong overall performance in simulation, with an average grasp success rate of 96.3% and an average overall success rate of 91.8%, while also demonstrating strong generalization and effective multi-skill integration. Real-world experiments further verify its practicality for multi-skill industrial assembly. The project page can be found at https://hwh23.github.io/ATG-MoE

Weihang Huang, Chaoran Zhang, Xiaoxin Deng +4

Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Faculty of Engineering, Imperial College London, London SW7 2AZ, United Kingdom

Robotic Assembly Multi-Task Robotic Manipulation Robot Imitation Learning Autoregressive Generation

Mar 18, 2026

Huddle: Parallel Shape Assembly using Decentralized, Minimalistic Robots

We propose a novel algorithm for forming arbitrarily shaped assemblies using decentralized robots. By relying on local interactions, the algorithm ensures there are no unreachable states or gaps in the assembly, which are global properties. The in-assembly robots attract passing-by robots into expanding the assembly via a simple implementation of signaling and alignment. Our approach is minimalistic, requiring only communication between attached, immediate neighbors. It is motion-agnostic and requires no pose localization, enabling asynchronous and order-independent assembly. We prove the algorithm's correctness and demonstrate its effectiveness in forming a 107-robot assembly.

Khai Yi Chin, Tingwei Meng, Zhe Chen +2

Amazon Robotics

Robotic Assembly Multi-Robot Formation Control Swarm Robotics Multi-Robot Coordination

Mar 16, 2026

RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation

Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action. This transition is highly significant for industrial robotic manipulation, promising to free human workers from repetitive, dangerous daily labor. To benchmark and advance this capability, we introduce the Robotic Collaborative Assembly Assistance (RoCo) Challenge with a dataset towards simulation and real-world assembly manipulation. Set against the backdrop of human-centered manufacturing, this challenge focuses on a high-precision planetary gearbox assembly task, a demanding yet highly representative operation in modern industry. Built upon a self-developed data collection, training, and evaluation system in Isaac Sim, and utilizing a dual-arm robot for real-world deployment, the challenge operates in two phases. The Simulation Round defines fine-grained task phases for step-wise scoring to handle the long-horizon nature of the assembly. The Real-World Round mirrors this evaluation with physical gearbox components and high-quality teleoperated datasets. The core tasks require assembling an epicyclic gearbox from scratch, including mounting three planet gears, a sun gear, and a ring gear. Attracting over 60 teams and 170+ participants from more than 10 countries, the challenge yielded highly effective solutions, most notably ARC-VLA and RoboCola. Results demonstrate that a dual-model framework for long-horizon multi-task learning is highly effective, and the strategic utilization of recovery-from-failure curriculum data is a critical insight for successful deployment. This report outlines the competition setup, evaluation approach, key findings, and future directions for industrial EAI. Our dataset, CAD files, code, and evaluation results can be found at: https://rocochallenge.github.io/RoCo2026/.

Haichao Liu, Yuheng Zhou, Zhenyu Wu +14

Nanyang Technological University, Singapore · Beijing University of Posts and Telecommunications, Beijing, China · Carnegie Mellon University, Pittsburgh, USA +1

Robot Manipulation Benchmarks Robotic Assembly Industrial Robotics Long-Horizon Robotic Manipulation Multi-Task Robotic Manipulation

Mar 13, 2026

Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations

Robotic assembly systems traditionally require substantial manual engineering effort to integrate new tasks, adapt to new environments, and improve performance over time. This paper presents a framework for autonomous integration and continuous improvement of robotic assembly systems based on Skill Graph representations. A Skill Graph organizes robot capabilities as verb-based skills, explicitly linking semantic descriptions (verbs and nouns) with executable policies, pre-conditions, post-conditions, and evaluators. We show how Skill Graphs enable rapid system integration by supporting semantic-level planning over skills, while simultaneously grounding execution through well-defined interfaces to robot controllers and perception modules. After initial deployment, the same Skill Graph structure supports systematic data collection and closed-loop performance improvement, enabling iterative refinement of skills and their composition. We demonstrate how this approach unifies system configuration, execution, evaluation, and learning within a single representation, providing a scalable pathway toward adaptive and reusable robotic assembly systems. The code is at https://github.com/intelligent-control-lab/AIDF.

Peiqi Yu, Philip Huang, Chaitanya Chawla +3

Department of Electrical and Computer Engineering, Carnegie Mellon University · Robotics Institute, Carnegie Mellon University

Robotic Assembly Robot Task Planning Robot Skill Learning Continual Robot Learning

Mar 12, 2026

CoViLLM: An Adaptive Human-Robot Collaborative Assembly Framework Using Large Language Models

With increasing demand for mass customization, traditional manufacturing robots that rely on rule-based operations lack the flexibility to accommodate customized or new product variants. Human-Robot Collaboration has demonstrated potential to improve system adaptability by leveraging human versatility and decision-making capabilities. However, existing Human-Robot Collaborative frameworks typically depend on predefined perception-manipulation pipelines, limiting their ability to autonomously generate task plans for new product assembly. In this work, we propose CoViLLM, an adaptive human-robot collaborative assembly framework that supports the assembly of customized and previously unseen products. CoViLLM combines depth-camera-based localization for object position estimation, human operator classification for identifying new components, and a Large Language Model for assembly task planning based on natural language instructions. The framework is validated on the NIST Assembly Task Board for known, customized, and new product cases. Experimental results show that the proposed framework enables flexible collaborative assembly by extending Human-Robot Collaboration beyond predefined product and task settings.

Jiabao Zhao, Jonghan Lim, Hongliang Li +1

The Pennsylvania State University, University Park, PA, USA

Human-Robot Collaboration Robotic Assembly Generalization in Robotic Manipulation Large Language Model-Based Robot Planning

Mar 11, 2026

SUBTA: A Framework for Supported User-Guided Bimanual Teleoperation in Structured Assembly

In human-robot collaboration, shared autonomy enhances human performance through precise, intuitive support. Effective robotic assistance requires accurately inferring human intentions and understanding task structures to determine optimal support timing and methods. In this paper, we present SUBTA, a supported teleoperation system for bimanual assembly that couples learned intention estimation, scene-graph task planning, and context-dependent motion assists. We validate our approach through a user study (N=12) comparing standard teleoperation, motion-support only, and SUBTA. Linear mixed-effects analysis revealed that SUBTA significantly outperformed standard teleoperation in position accuracy (p<0.001, d=1.18) and orientation accuracy (p<0.001, d=1.75), while reducing mental demand (p=0.002, d=1.34). Post-experiment ratings indicate clearer, more trustworthy visual feedback and predictable interventions in SUBTA. The results demonstrate that SUBTA greatly improves both effectiveness and user experience in teleoperation.

Xiao Liu, Prakash Baskaran, Songpo Li +4

Authors are with Honda Research Institute USA, San Jose, CA 95134 USA. · Authors are with Honda Research Institute Europe GmbH, 63073 Offenbach am Main, Germany.

Robot Teleoperation Human-Robot Collaboration Shared Autonomy Bimanual Robotic Manipulation Robotic Assembly

Mar 10, 2026

ReTac-ACT: A State-Gated Vision-Tactile Fusion Transformer for Precision Assembly

Precision assembly requires sub-millimeter corrections in contact-rich "last-millimeter" regions where visual feedback fails due to occlusion from the end-effector and workpiece. We present ReTac-ACT (Reconstruction-enhanced Tactile ACT), a vision-tactile imitation learning policy that addresses this challenge through three synergistic mechanisms: (i) bidirectional cross-attention enabling reciprocal visuo-tactile feature enhancement before fusion, (ii) a proprioception-conditioned gating network that dynamically elevates tactile reliance when visual occlusion occurs, and (iii) a tactile reconstruction objective enforcing learning of manipulation-relevant contact information rather than generic visual textures. Evaluated on the standardized NIST Assembly Task Board M1 benchmark, ReTac-ACT achieves 90% peg-in-hole success, substantially outperforming vision-only and generalist baseline methods, and maintains 80% success at industrial-grade 0.1mm clearance. Ablation studies validate that each architectural component is indispensable. The ReTac-ACT codebase and a vision-tactile demonstration dataset covering various clearance levels with both visual and tactile features will be released to support reproducible research.

Minchi Ruan, LiangQing Zhou, Hongtong Li +4

Beijing University of Posts and Telecommunications, Beijing, China · SunHDex Intelligent Technology (Beijing) Co. Ltd., Beijing, China · University of Hamburg, Hamburg, Germany

Contact-Rich Robotic Manipulation Robotic Assembly Visuo-Tactile Representation Learning Imitation Learning

Mar 2, 2026

Bimanual XR Specification of Relative and Absolute Assembly Hierarchies for Teleoperation

We present a bimanual XR interaction approach for specifying remote assembly tasks as hierarchies of relative and absolute object constraints that specify high-level teleoperation goals for robots. Grabbing one object in each hand creates a constraint group (visualized as a hull) and groups can be nested into hierarchies. Each group can be relative (with a robot-specifiable 6DoF pose) or absolute (with an author-specified fixed 6DoF pose) in relation to its parent. A relative group specifies a subassembly that can be constructed at a location chosen by the robot software for efficiency rather than mandated by the user.

Benjamin Yang, Xichen He, Charlie Zou +3

Columbia University

Robot Teleoperation Robotic Assembly

Mar 1, 2026

SMR-Net:Robot Snap Detection Based on Multi-Scale Features and Self-Attention Network

In robot automated assembly, snap assembly precision and efficiency directly determine overall production quality. As a core prerequisite, snap detection and localization critically affect subsequent assembly success. Traditional visual methods suffer from poor robustness and large localization errors when handling complex scenarios (e.g., transparent or low-contrast snaps), failing to meet high-precision assembly demands. To address this, this paper designs a dedicated sensor and proposes SMR-Net, an self-attention-based multi-scale object detection algorithm, to synergistically enhance detection and localization performance. SMR-Net adopts an attention-enhanced multi-scale feature fusion architecture: raw sensor data is encoded via an attention-embedded feature extractor to strengthen key snap features and suppress noise; three multi-scale feature maps are processed in parallel with standard and dilated convolution for dimension unification while preserving resolution; an adaptive reweighting network dynamically assigns weights to fused features, generating fine representations integrating details and global semantics. Experimental results on Type A and Type B snap datasets show SMR-Net outperforms traditional Faster R-CNN significantly: Intersection over Union (IoU) improves by 6.52% and 5.8%, and mean Average Precision (mAP) increases by 2.8% and 1.5% respectively. This fully demonstrates the method's superiority in complex snap detection and localization tasks.

Kuanxu Hou

Object Detection Robotic Assembly Multi-Scale Feature Fusion

Feb 27, 2026

Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans

This paper presents a novel autonomous robotic assembly framework for constructing stable structures without relying on predefined architectural blueprints. Instead of following fixed plans, construction tasks are defined through targets and obstacles, allowing the system to adapt more flexibly to environmental uncertainty and variations during the building process. A reinforcement learning (RL) policy, trained using deep Q-learning with successor features, serves as the decision-making component. As a proof of concept, we evaluate the approach on a benchmark of 15 2D robotic assembly tasks of discrete block construction. Experiments using a real-world closed-loop robotic setup demonstrate the feasibility of the method and its ability to handle construction noise. The results suggest that our framework offers a promising direction for more adaptable and robust robotic construction in real-world environments.

Jingwen Wang, Johannes Kirschner, Paul Rolland +2

Lab of Creative Computation, ´Ecole Polytechnique F´ed´erale de Lausanne · Swiss Data Science Center

Robotic Construction Sequential Decision Making Robotic RL Robotics Robotic Manipulation Robotic Assembly

Feb 27, 2026

MicroPush: A Lightweight Simulator for Sim-to-Real Microrobotic Pushing and Assembly

Developing learning-based autonomy for microrobotic manipulation is difficult because physical trials are slow, variable, and poorly suited to large-scale training. We present MicroPush, a lightweight simulation and benchmarking environment for pushing and assembly with magnetic rolling microrobots. The simulator combines overdamped planar dynamics with calibrated rolling actuation, reduced-order contact handling, short-range interaction corrections, and optional background flow, while providing common interfaces for planning, model-based control, reinforcement learning (RL), interactive visualization, and headless evaluation. In no-render tests, MicroPush remains substantially faster than real time, reaching 37.0x real time with 100 passive particles. We further evaluate the simulator through zero-shot sim-to-real policy transfer. A Residual RL-MPC policy is trained entirely in MicroPush, frozen after simulation training, and deployed on the physical system without real-world policy fine-tuning. A 20 μm magnetic microrobot pushes a similarly sized silica particle along a figure-eight trajectory with 1.91 μm median and 4.65 μm 95th-percentile path error, and the same autonomy stack performs sequential line, triangle, and six-particle hexagonal assembly. These results show that a deliberately reduced-order simulator can provide an efficient training and development substrate for sim-to-real microrobotic autonomy.

Yanda Yang, Zion Lee, Sambeeta Das

Department of Mechanical Engineering, University of Delaware, Newark, DE 19716, USA

Robot Policy Learning Robotics Simulation Sim-to-Real Transfer Robotic Manipulation Robot Manipulation Benchmarks Robotic Assembly

Feb 26, 2026

SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly

Robotic assembly presents a long-standing challenge due to its requirement for precise, contact-rich manipulation. While simulation-based learning has enabled the development of robust assembly policies, their performance often degrades when deployed in real-world settings due to the sim-to-real gap. Conversely, real-world reinforcement learning (RL) methods avoid the sim-to-real gap, but rely heavily on human supervision and lack generalization ability to environmental changes. In this work, we propose a hybrid approach that combines a simulation-trained base policy with a real-world residual policy to efficiently adapt to real-world variations. The base policy, trained in simulation using low-level state observations and dense rewards, provides strong priors for initial behavior. The residual policy, learned in the real world using visual observations and sparse rewards, compensates for discrepancies in dynamics and sensor noise. Extensive real-world experiments demonstrate that our method, SPARR, achieves near-perfect success rates across diverse two-part assembly tasks. Compared to the state-of-the-art zero-shot sim-to-real methods, SPARR improves success rates by 38.4% while reducing cycle time by 29.7%. Moreover, SPARR requires no human expertise, in contrast to the state-of-the-art real-world RL approaches that depend heavily on human supervision.

Yijie Guo, Iretiayo Akinola, Lars Johannsmeier +3

Nvidia · University of Washington

Robotic Assembly Sim-to-Real Transfer Robotic RL Residual Policy Learning

Feb 25, 2026

Behavioral Cloning for Robotic Connector Assembly: An Empirical Study

Automating the assembly of wire harnesses is challenging in automotive, electrical cabinet, and aircraft production, particularly due to deformable cables and a high variance in connector geometries. In addition, connectors must be inserted with limited force to avoid damage, while their poses can vary significantly. While humans can do this task intuitively by combining visual and haptic feedback, programming an industrial robot for such a task in an adaptable manner remains difficult. This work presents an empirical study investigating the suitability of behavioral cloning for learning an action prediction model for connector insertion that fuses force-torque sensing with a fixed position camera. We compare several network architectures and other design choices using a dataset of up to 300 successful human demonstrations collected via teleoperation of a UR5e robot with a SpaceMouse under varying connector poses. The resulting system is then evaluated against five different connector geometries under varying connector poses, achieving an overall insertion success rate of over 90 %.

Andreas Kernbach, Daniel Bargmann, Werner Kraus +1

The authors are with the Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany · Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Germany

Robotic Insertion Robotic Manipulation Robotic Assembly Robot Imitation Learning

Feb 17, 2026

Autonomous Block Assembly for Boom Cranes with Passive Joint Dynamics: Integrated Vision MPC Control

This paper presents an autonomous control framework for articulated boom cranes performing prefabricated block assembly in construction environments. The key challenge addressed is precise placement control under passive joint dynamics that cause pendulum-like sway, complicating the accurate positioning of building components. Our integrated approach combines real-time vision-based pose estimation of building blocks, collision-aware B-spline path planning, and nonlinear model predictive control (NMPC) to achieve autonomous pickup, placement, and obstacle-avoidance assembly operations. The framework is validated on a laboratory-scale testbed that emulates crane kinematics and passive dynamics while enabling rapid experimentation. The collision-aware planner generates feasible B-spline references in real-time on CPU hardware with anytime performance, while the NMPC controller actively suppresses passive joint sway and tracks the planned trajectory under continuous vision feedback. Experimental results demonstrate autonomous block stacking and obstacle-avoidance assembly, with sway damping reducing settling times by more than an order of magnitude compared to uncontrolled passive dynamics, confirming the real-time feasibility of the integrated approach for construction automation.

Gerald Ebmer, Minh Nhat Vu, Tobias Glück +1

Automation & Control Institute (ACIN), TU Wien, Gusshausstrasse 27-29, 1040 Wien, Austria · Center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH, Giefinggasse 4, 1210 Vienna, Austria

Robotic Control Robotic Construction Robotic Assembly Feedback Control

Feb 16, 2026

Simulation-based Learning of Electrical Cabinet Assembly Using Robot Skills

This paper presents a simulation-driven approach for automating the force-controlled assembly of electrical terminals on DIN-rails, a task traditionally hindered by high programming effort and product variability. The proposed method integrates deep reinforcement learning (DRL) with parameterizable robot skills in a physics-based simulation environment. To realistically model the snap-fit assembly process, we develop and evaluate two types of joining models: analytical models based on beam theory and rigid-body models implemented in the MuJoCo physics engine. These models enable accurate simulation of interaction forces, essential for training DRL agents. The robot skills are structured using the pitasc framework, allowing modular, reusable control strategies. Training is conducted in simulation using Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms. Domain randomization is applied to improve robustness. The trained policies are transferred to a physical UR10e robot system without additional tuning. Experimental results demonstrate high success rates (up to 100%) in both simulation and real-world settings, even under significant positional and rotational deviations. The system generalizes well to new terminal types and positions, significantly reducing manual programming effort. This work highlights the potential of combining simulation-based learning with modular robot skills for flexible, scalable automation in small-batch manufacturing. Future work will explore hybrid learning methods, automated environment parameterization, and further refinement of joining models for design integration.

Arik Laemmle, Balázs András Bálint, Philipp Tenbrock +4

formerly Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany · formerly Institut für Strahlwerkzeuge IFSW, University of Stuttgart, Stuttgart, Germany · Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control SZTAKI, Budapest, Hungary +1

Robotic Assembly Robot Skill Learning Force-Controlled Robotic Manipulation Robotic RL Sim-to-Real Transfer

Feb 16, 2026

Replanning Human-Robot Collaborative Tasks with Vision-Language Models via Semantic and Physical Dual-Correction

Human-robot collaborative assembly requires robots to interpret ambiguous corrective instructions while producing physically executable motions. Vision-language models (VLMs) provide semantic reasoning but may select logically inconsistent targets or misjudge execution outcomes. We propose a replanning framework that maps human instructions to Action Target candidates, including grasp poses and tool selections, and combines an Internal Correction Model for pre-execution logical verification with an External Correction Model for post-execution visual verification. The framework integrates VLM reasoning with 6-DoF grasp generation and collision-free trajectory planning. Simulation ablations show configuration-dependent effects: internal correction improves candidate validity, whereas external correction enables recovery for a low-latency VLM but can reduce success when visual verification produces false negatives. Experiments with an upper-body humanoid robot achieved 66.7% success in real-world object fixation, 100% in initial tool selection, and 75.0% in corrective tool selection. These results demonstrate interactive replanning across spatial and semantic collaborative tasks while identifying visual-state verification as a key limitation.

Taichi Kato, Takuya Kiyokawa, Namiko Saito +1

aDepartment of Systems Science, School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka, Japan · bDepartment of Systems Innovation, Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, Osaka, Japan · cMicrosoft Research Asia, Shinagawa, Tokyo, Japan +1

Language-Conditioned Robot Manipulation Large Language Model-Based Robot Planning Human-Robot Collaboration Robotic Assembly VLMs for Robotics

Jan 2, 2026

From Perception to Symbolic Task Planning: Vision-Language Guided Human-Robot Collaborative Structured Assembly

Human-robot collaboration (HRC) in structured assembly requires reliable state estimation and adaptive task planning under noisy perception and human interventions. To address these challenges, we introduce a design-grounded human-aware planning framework for human-robot collaborative structured assembly. The framework comprises two coupled modules. Module I, Perception-to-Symbolic State (PSS), employs vision-language models (VLMs) based agents to align RGB-D observations with design specifications and domain knowledge, synthesizing verifiable symbolic assembly states. It outputs validated installed and uninstalled component sets for online state tracking. Module II, Human-Aware Planning and Replanning (HPR), performs task-level multi-robot assignment and updates the plan only when the observed state deviates from the expected execution outcome. It applies a minimal-change replanning rule to selectively revise task assignments and preserve plan stability even under human interventions. We validate the framework on a 27-component timber-frame assembly. The PSS module achieves 97% state synthesis accuracy, and the HPR module maintains feasible task progression across diverse HRC scenarios. Results indicate that integrating VLM-based perception with knowledge-driven planning improves robustness of state estimation and task planning under dynamic conditions.

Yanyi Chen, Min Deng

Department of Civil, Environmental, and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA

Human-Robot Collaboration Robotic Assembly Robot Task Planning Symbolic Planning Multi-Robot Coordination

Nov 13, 2025

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.

Lorenzo Pagliara, Vincenzo Petrone, Violeta Redondo +3

Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Italy · GTD Science, Infrastructures & Robotics, Av. Leonardo Da Vinci, 2, Madrid, 28906, Spain · Universidad Politécnica de Madrid, Centre for Automation and Robotics (CAR) UPM-CSIC, José Gutiérrez Abascal, 2, Madrid, 28006, Spain

Industrial Robotics Robotic Assembly Fault-Tolerant Control Robot Failure Detection Human-Robot Collaboration

Sep 21, 2025

IDfRA: Self-Verification for Iterative Design in Robotic Assembly

Design for Robotic Assembly (DfRA) remains largely dependent on manual planning and heuristic simulation, limiting scalability and robustness in complex industrial settings. Although large language models (LLMs) show promise for semantic reasoning and task planning, most approaches remain tightly coupled to pre-built simulators that assume an accurate world model. We introduce Iterative Design for Robotic Assembly (IDfRA), a closed-loop framework that combines an LLM for plan generation with a vision-language model (VLM) for execution assessment. Given a target structure and a partial environmental signature, the LLM proposes an assembly plan, the robot executes it once at test time, and the VLM evaluates the resulting state to provide feedback for replanning. Through this iterative planning-execution-verification loop, the system progressively improves semantic fidelity and physical feasibility. Crucially, IDfRA does not require an accurate a priori world model before deployment. Instead, physical constraints are discovered online through interaction, enabling adaptation to under-specified environments. Empirical evaluation demonstrates that IDfRA attains 73.3% top-1 accuracy in semantic recognisability, surpassing the baseline on this metric. Moreover, the resulting assembly plans exhibit robust physical feasibility, achieving an overall 86.9% construction success rate, with design quality improving across iterations, albeit not always monotonically. Pairwise human evaluation further corroborates the advantages of IDfRA relative to alternative approaches. By integrating self-verification with context-aware adaptation, the framework evidences strong potential for deployment in unstructured manufacturing scenarios.

Nishka Khendry, Christos Margadji, Sebastian W. Pattinson

Institute for Manufacturing, Department of Engineering, University of Cambridge

Robotic Assembly Robot Task Planning Large Language Model-Based Robot Planning