arXiv subjects

Kensuke Harada

Publications and source records attributed to Kensuke Harada.

At least 19 recordsLinked to original sources

arXiv2026

ARTiS: An Adaptive Robotic Gripper for Enhanced Tool Manipulation in Disassembly Applications

Grasping and holding tools while using them presents a considerable challenge not only for robots but also for humans. Such a challenge is particularly noticeable in processes involving assembly and disassembly, where efficiency and consistency depend on performing rapidly adaptive tasks. Nonetheless, contemporary robotic grasping technologies that can securely manipulate tools during operation frequently have significant constraints. In this paper, introduce ARTiS (Adaptive Robotic Tool Gripper in Disassembly Systems), a novel gripper that combines the adaptability of soft grippers, the dexterity of anthropomorphic hands, and the robustness of rigid mechanisms with a soft palm and fingertips. This unique combination makes it possible to hold tools securely in a variety of situations through using active jamming in the palm and fin-ray adaptation in fingertips. Furthermore, high finger dexterity is achieved through the seven degrees of freedom design, which enables the fingertips to orient to any surface, both for automated solutions and collaborative tasks. A comprehensive evaluation was conducted using a range of conventional disassembly tools to assess the gripper's compliance, durability, and functional versatility. More information, hardware instructions, and videos at https://romanmykhailyshyn.github.io/artis/

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arXiv2026

Planning a Shared Modular Fixture Layout Across Robotic Disassembly Stages

Stable support remains challenging in robotic disassembly of irregularly shaped products. As components are progressively removed, the available support surfaces, mass distribution, and task loads change throughout the process. A fixture layout designed for one workpiece state may therefore become infeasible at later stages, motivating unified support planning over the complete disassembly sequence. This paper presents a modular vacuum-based fixturing system that plans one shared support configuration for the complete disassembly sequence of a screwdriver or shaver, allowing each sequence to proceed without fixture reconfiguration. To search the mixed continuous--discrete layout space under repeated cross-stage evaluation, a denoising diffusion probabilistic model generates physics-informed initial configurations that are refined through Bayesian optimization. Robotic screw and component-removal experiments verified the disassembly feasibility of the planned layouts, while 11 directional-load tests quantified their stability. Comparisons between the measured operational loads and directional responses yielded mean empirical stability margins of 66.9% for the screwdriver and 81.6% for the shaver. These results demonstrate that a product-specific shared layout can provide stable support throughout the tested robotic disassembly sequence.

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arXiv2026

Extreme Motion Generation via Hybrid Null-Space Control for Straight-Line Path Following

This work studies ``extreme motion generation'', which aims to maximize the Cartesian path length along a pre-defined trajectory within the manipulator's workspace. This objective is important in industry as long as path-following is fundamental to a large variety of tasks such as surface coating and welding. More critically, extreme motion enables a fixed-base manipulator to exploit the kinematic capability under limited reachability. However, such exploitation is challenging in practice, as the manipulator must actively avoid the safety boundary through execution, which is inherently a long-horizon problem. Accordingly, we claim that long-horizon decision-making should be delegated to a learning-based policy to maximize exploitation, while a classical model-based controller covers the near-boundary region, where the learning policy degrades sharply due to sparse data coverage. In detail, our proposed method is a step-level hybrid controller that switches between an RL-based and a model-based controller according to the normalized joint-limit distance. The initial joint configuration is sampled through conditional diffusion-based sampling, which improves the achievable path length based on the learned motion prior. We evaluate the proposed framework on 10,000 straight-line path-following tasks with a 7-DoF Franka FR3, extending the average rollout length by 27\% over the model-based baseline. Notably, certain tasks yield a pronounced extension toward the motion extreme, as reflected in the maximum improvement reported in the statistical results. The project website and related videos of this paper can be found at https://yuan-xinyi.github.io/extreme-motion-generation/.

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arXiv2026

Component Influence-Driven Fastener Reduction for Robotic Disassemblability-Aware Design Simplification

To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quantitative feedback to identify which structural elements hinder robotic operations. To address this, this study proposes an analytical framework that provides actionable redesign guidance focused on fastener reduction, as fasteners are numerous and ubiquitous components found in almost all manufactured products. Using a Computer-Aided Design (CAD) model and its automatically generated Contact-Connection-Constraint (CCC) graph, the framework translates robotic disassembly sequence planning outcomes into component influence scores. These scores reflect how often a component causes structural constraint violations or evaluation objective deteriorations in the robotic disassembly sequence. To visually highlight structural hindrances, the framework projects these scores onto the CAD geometry as 3D heatmaps. The system then analytically simulates the removal of highly influential fasteners. It reports the expected reductions in structural constraints, tool changes, and robot travel distances, while preventing structurally unsafe modifications by evaluating geometric stability metrics. Experiments on seven household appliances demonstrate that the framework successfully targets redundant fasteners. Removing the recommended fasteners simplified the structural dependencies by eliminating between 8 and 132 structural constraints on the graph depending on each product's structural configuration. Furthermore, it improved robotic operational efficiency by eliminating unnecessary tool change operations and shortening travel distances by 165 to 1675 millimeters wherever structurally permissible.

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arXiv2026

Differentiable Object Pose Connectivity Metrics for Regrasp Sequence Optimization

Regrasp planning is often required when one pick-and-place cannot transfer an object from an initial pose to a goal pose while maintaining grasp feasibility. The main challenge is to reason about shared-grasp connectivity across intermediate poses, where discrete search becomes brittle. We propose an implicit multi-step regrasp planning framework based on differentiable pose sequence connectivity metrics. We model grasp feasibility under an object pose using an Energy-Based Model (EBM) and leverage energy additivity to construct a continuous energy landscape that measures pose-pair connectivity, enabling gradient-based optimization of intermediate object poses. An adaptive iterative deepening strategy is introduced to determine the minimum number of intermediate steps automatically. Experiments show that the proposed cost formulation provides smooth and informative gradients, improving planning robustness over other alternatives. They also demonstrate generalization to unseen grasp poses and cross-end-effector transfer, where a model trained with suction constraints can guide parallel gripper grasp manipulation. The multi-step planning results further highlight the effectiveness of adaptive deepening and minimum-step search.

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arXiv2026

Industrial-Grade Robust Robot Vision for Screw Detection and Removal under Uneven Conditions

As the amount of used home appliances is expected to increase despite the decreasing labor force in Japan, there is a need to automate disassembling processes at recycling plants. The automation of disassembling air conditioner outdoor units, however, remains a challenge due to unit size variations and exposure to dirt and rust. To address these challenges, this study proposes an automated system that integrates a task-specific two-stage detection method and a lattice-based local calibration strategy. This approach achieved a screw detection recall of 99.8% despite severe degradation and ensured a manipulation accuracy of +/-0.75 mm without pre-programmed coordinates. In real-world validation with 120 units, the system attained a disassembly success rate of 78.3% and an average cycle time of 193 seconds, confirming its feasibility for industrial application.

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arXiv2026

Generalizable task-oriented object grasping through LLM-guided ontology and similarity-based planning

Task-oriented grasping (TOG) is more challenging than simple object grasping because it requires precise identification of object parts and careful selection of grasping areas to ensure effective and robust manipulation. While recent approaches have trained large-scale vision-language models to integrate part-level object segmentation with task-aware grasp planning, their instability in part recognition and grasp inference limits their ability to generalize across diverse objects and tasks. To address this issue, we introduce a novel, geometry-centric strategy for more generalizable TOG that does not rely on semantic features from visual recognition, effectively overcoming the viewpoint sensitivity of model-based approaches. Our main proposals include: 1) an object-part-task ontology for functional part selection based on intuitive human commands, constructed using a Large Language Model (LLM); 2) a sampling-based geometric analysis method for identifying the selected object part from observed point clouds, incorporating multiple point distribution and distance metrics; and 3) a similarity matching framework for imitative grasp planning, utilizing similar known objects with pre-existing segmentation and grasping knowledge as references to guide the planning for unknown targets. We validate the high accuracy of our approach in functional part selection, identification, and grasp generation through real-world experiments. Additionally, we demonstrate the method's generalization capabilities to novel-category objects by extending existing ontological knowledge, showcasing its adaptability to a broad range of objects and tasks.

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arXiv2026

Affordance-Guided Enveloping Grasp Demonstration Toward Non-destructive Disassembly of Pinch-Infeasible Mating Parts

Robotic disassembly of complex mating components often renders pinch grasping infeasible, necessitating multi-fingered enveloping grasps. However, visual occlusions and geometric constraints complicate teaching appropriate grasp motions when relying solely on 2D camera feeds. To address this, we propose an affordance-guided teleoperation method that pre-generates enveloping grasp candidates via physics simulation. These Affordance Templates (ATs) are visualized with a color gradient reflecting grasp quality to augment operator perception. Simulations demonstrate the method's generality across various components. Real-robot experiments validate that AT-based visual augmentation enables operators to effectively select and teach enveloping grasp strategies for real-world disassembly, even under severe visual and geometric constraints.

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arXiv2026

Gait Generation Balancing Joint Load and Mobility for Legged Modular Robots with Easily Detachable Joints

While modular robots offer versatility, excessive joint torque during locomotion poses a significant risk of mechanical failure, especially for detachable joints. To address this, we propose an optimization framework using the NSGA-III algorithm. Unlike conventional approaches that prioritize mobility alone, our method derives Pareto optimal solutions to minimize joint load while maintaining necessary locomotion speed and stability. Simulations and physical experiments demonstrate that our approach successfully generates gait motions for diverse environments, such as slopes and steps, ensuring structural integrity without compromising overall mobility.

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arXiv2026

Designing and Validating a Self-Aligning Tool Changer for Modular Reconfigurable Manipulation Robots

Modular reconfigurable robots require reliable mechanisms for automated module exchange, but conventional rigid active couplings often fail due to inevitable positioning and orientational errors. To address this, we propose a misalignment-tolerant tool-changing system. The hardware features a motor-driven coupling utilizing passive self-alignment geometries, specifically chamfered receptacles and triangular lead-in guides, to robustly compensate for angular and lateral misalignments without complex force sensors. To make this autonomous exchange practically feasible, the mechanism is complemented by a compact rotating tool exchange station for efficient module storage. Real-world autonomous tool-picking experiments validate that the self-aligning features successfully absorb execution errors, enabling highly reliable robotic tool reconfiguration.

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arXiv2026

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.

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arXiv2026

Replaceable Bit-based Gripper for Picking Cluttered Food Items

The food packaging industry goes through changes in food items and their weights quite rapidly. These items range from easy-to-pick, single-piece food items to flexible, long and cluttered ones. We propose a replaceable bit-based gripper system to tackle the challenge of weight-based handling of cluttered food items. The gripper features specialized food attachments(bits) that enhance its grasping capabilities, and a belt replacement system allows switching between different food items during packaging operations. It offers a wide range of control options, enabling it to grasp and drop specific weights of granular, cluttered, and entangled foods. We specifically designed bits for two flexible food items that differ in shape: ikura(salmon roe) and spaghetti. They represent the challenging categories of sticky, granular food and long, sticky, cluttered food, respectively. The gripper successfully picked up both spaghetti and ikura and demonstrated weight-specific dropping of these items with an accuracy over 80% and 95% respectively. The gripper system also exhibited quick switching between different bits, leading to the handling of a large range of food items.

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arXiv2025

Prompt2Craft: Generating Functional Craft Assemblies with LLMs

Inspired by traditional handmade crafts, where a person improvises assemblies based on the available objects, we formally introduce the Craft Assembly Task. It is a robotic assembly task that involves building an accurate representation of a given target object using the available objects, which do not directly correspond to its parts. In this work, we focus on selecting the subset of available objects for the final craft, when the given input is an RGB image of the target in the wild. We use a mask segmentation neural network to identify visible parts, followed by retrieving labeled template meshes. These meshes undergo pose optimization to determine the most suitable template. Then, we propose to simplify the parts of the transformed template mesh to primitive shapes like cuboids or cylinders. Finally, we design a search algorithm to find correspondences in the scene based on local and global proportions. We develop baselines for comparison that consider all possible combinations, and choose the highest scoring combination for common metrics used in foreground maps and mask accuracy. Our approach achieves comparable results to the baselines for two different scenes, and we show qualitative results for an implementation in a real-world scenario.

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arXiv2025

Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control

Human-robot cooperation is essential in environments such as warehouses and retail stores, where workers frequently handle deformable objects like paper, bags, and fabrics. Coordinating robotic actions with human assistance remains difficult due to the unpredictable dynamics of deformable materials and the need for adaptive force control. To explore this challenge, we focus on the task of gift wrapping, which exemplifies a long-horizon manipulation problem involving precise folding, controlled creasing, and secure fixation of paper. Success is achieved when the robot completes the sequence to produce a neatly wrapped package with clean folds and no tears. We propose a learning-based framework that integrates a high-level task planner powered by a large language model (LLM) with a low-level hybrid imitation learning (IL) and reinforcement learning (RL) policy. At its core is a Sub-task Aware Robotic Transformer (START) that learns a unified policy from human demonstrations. The key novelty lies in capturing long-range temporal dependencies across the full wrapping sequence within a single model. Unlike vanilla Action Chunking with Transformer (ACT), typically applied to short tasks, our method introduces sub-task IDs that provide explicit temporal grounding. This enables robust performance across the entire wrapping process and supports flexible execution, as the policy learns sub-goals rather than merely replicating motion sequences. Our framework achieves a 97% success rate on real-world wrapping tasks. We show that the unified transformer-based policy reduces the need for specialized models, allows controlled human supervision, and effectively bridges high-level intent with the fine-grained force control required for deformable object manipulation.

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arXiv2025

Hierarchical Planning and Scheduling for Reconfigurable Multi-Robot Disassembly Systems under Structural Constraints

This study presents a system integration approach for planning schedules, sequences, tasks, and motions for reconfigurable robots to automatically disassemble constrained structures in a non-destructive manner. Such systems must adapt their configuration and coordination to the target structure, but the large and complex search space makes them prone to local optima. To address this, we integrate multiple robot arms equipped with different types of tools, together with a rotary stage, into a reconfigurable setup. This flexible system is based on a hierarchical optimization method that generates plans meeting multiple preferred conditions under mandatory requirements within a realistic timeframe. The approach employs two many-objective genetic algorithms for sequence and task planning with motion evaluations, followed by constraint programming for scheduling. Because sequence planning has a much larger search space, we introduce a chromosome initialization method tailored to constrained structures to mitigate the risk of local optima. Simulation results demonstrate that the proposed method effectively solves complex problems in reconfigurable robotic disassembly.

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arXiv2025

Shell-Type Soft Jig for Holding Objects during Disassembly

This study addresses a flexible holding tool for robotic disassembly. We propose a shell-type soft jig that securely and universally holds objects, mitigating the risk of component damage and adapting to diverse shapes while enabling soft fixation that is robust to recognition, planning, and control errors. The balloon-based holding mechanism ensures proper alignment and stable holding performance, thereby reducing the need for dedicated jig design, highly accurate perception, precise grasping, and finely tuned trajectory planning that are typically required with conventional fixtures. Our experimental results demonstrate the practical feasibility of the proposed jig through performance comparisons with a vise and a jamming-gripper-inspired soft jig. Tests on ten different objects further showed representative successes and failures, clarifying the jig's limitations and outlook.

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arXiv2025

Soft Regrasping Tool Inspired by Jamming Gripper

Regrasping on fixtures is a promising approach to reduce pose uncertainty in robotic assembly, but conventional rigid fixtures lack adaptability and require dedicated designs for each part. To overcome this limitation, we propose a soft jig inspired by the jamming transition phenomenon, which can be continuously deformed to accommodate diverse object geometries. By pressing a triangular-pyramid-shaped tool into the membrane and evacuating the enclosed air, a stable cavity is formed as a placement space. We further optimize the stamping depth to balance placement stability and gripper accessibility. In soft-jig-based regrasping, the key challenge lies in optimizing the cavity size to achieve precise dropping; once the part is reliably placed, subsequent grasping can be performed with reduced uncertainty. Accordingly, we conducted drop experiments on ten mechanical parts of varying shapes, which achieved placement success rates exceeding 80% for most objects and above 90% for cylindrical ones, while failures were mainly caused by geometric constraints and membrane properties. These results demonstrate that the proposed jig enables general-purpose, accurate, and repeatable regrasping, while also clarifying its current limitations and future potential as a practical alternative to rigid fixtures in assembly automation.

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arXiv2025

Modular Vacuum-Based Fixturing System for Adaptive Disassembly Workspace Integration

The disassembly of small household appliances poses significant challenges due to their complex and curved geometries, which render traditional rigid fixtures inadequate. In this paper, we propose a modular vacuum-based fixturing system that leverages commercially available balloon-type soft grippers to conform to arbitrarily shaped surfaces and provide stable support during screw-removal tasks. To enable a reliable deployment of the system, we develop a stability-aware planning framework that samples the bottom surface of the target object, filters candidate contact points based on geometric continuity, and evaluates support configurations using convex hull-based static stability criteria. We compare the quality of object placement under different numbers and configurations of balloon hands. In addition, real-world experiments were conducted to compare the success rates of traditional rigid fixtures with our proposed system. The results demonstrate that our method consistently achieves higher success rates and superior placement stability during screw removal tasks.

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