arXiv subjects

Masato Kobayashi

Publications and source records attributed to Masato Kobayashi.

At least 19 recordsLinked to original sources

arXiv2026

MRReP: Mixed Reality-based Hand-drawn Reference Path Editing Interface for Mobile Robot Navigation

Autonomous mobile robots operating in human-shared indoor environments often require paths that reflect human spatial intentions, such as avoiding interference with pedestrian flow or maintaining comfortable clearance. However, conventional path planners primarily optimize geometric costs and provide limited support for explicit route specification by human operators. This paper presents MRReP, a Mixed Reality-based interface that enables users to draw a Hand-drawn Reference Path (HRP) directly on the physical floor using hand gestures. The drawn HRP is integrated into the robot navigation stack through a custom Hand-drawn Reference Path Planner, which converts the user-specified point sequence into a global path for autonomous navigation. We evaluated MRReP in a within-subject experiment against a conventional 2D baseline interface. The results demonstrated that MRReP enhanced path specification accuracy, usability, and perceived workload, while enabling more stable path specification in the physical environment. These findings suggest that direct path specification in MR is an effective approach for incorporating human spatial intention into mobile robot navigation. Additional material is available at https://mertcookimg.github.io/mrrep

cs.RO↗

arXiv2026

MRPoS: Mixed Reality-Based Robot Navigation Interface Using Spatial Pointing and Speech with Large Language Model

Recent advancements have made robot navigation more intuitive by transitioning from traditional 2D displays to spatially aware Mixed Reality (MR) systems. However, current MR interfaces often rely on manual "air tap" gestures for goal placement, which can be repetitive and physically demanding, especially for beginners. This paper proposes the Mixed Reality-Based Robot Navigation Interface using Spatial Pointing and Speech (MRPoS). This novel framework replaces complex hand gestures with a natural, multimodal interface combining spatial pointing with Large Language Model (LLM)-based speech interaction. By leveraging both information, the system translates verbal intent into navigation goals visualized by MR technology. Comprehensive experiments comparing MRPoS against conventional gesture-based systems demonstrate that our approach significantly reduces task completion time and workload, providing a more accessible and efficient interface. For additional material, please check: https://mertcookimg.github.io/mrpos

cs.RO↗

arXiv2026

Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation

Long-horizon contact-rich robotic manipulation remains challenging due to partial observability and unstable subtask transitions under contact uncertainty. While hierarchical architectures improve temporal reasoning and bilateral imitation learning enables force-aware control, existing approaches often rely on flat policies that struggle with long-horizon coordination. We propose Bi-HIL, a bilateral control-based multimodal hierarchical imitation learning framework for long-horizon manipulation. Bi-HIL stabilizes hierarchical coordination by integrating keyframe memory with subtask-level progress rate that models phase progression within the active subtask and conditions both high- and low-level policies. We evaluate Bi-HIL on unimanual and bimanual real-robot tasks, demonstrating consistent improvements over flat and ablated variants. The results highlight the importance of explicitly modeling subtask progression together with force-aware control for robust long-horizon manipulation. For additional material, please check: https://mertcookimg.github.io/bi-hil

cs.RO↗

arXiv2026

Symmetry of the generating function of semistandard oscillating tableaux

H.Choi-D.Kim-S.J.Lee and S.J.Lee introduced a new kind of tableaux, semistandard oscillating tableaux (SSOT), around 2024 in the context of Lusztig $q$-weight multiplicities, KR crystals and King tableaux. In this paper, we study generating function of the SSOTs and its symmetry. First, we extend Gessel's and Assaf-Searles' expansion of a Schur function in terms of fundamental quasi-symmetric functions to our generating function. As a consequence, we show that it is $F$-positive. Further, we improve Sundaram's work on oscillating tableaux by proving that it is symmetric, Schur-positive, and has Saturated Newton polytope.

math.CO↗

arXiv2025

Crystal skeleton polynomials with major index, charge and depth

We introduce a new family of polynomials, crystal skeleton polynomials, to better understand enumeration of standard Young tableaux, quasi-Yamanouchi tableaux and interactions with Gessel's expansion of a Schur function, quasi-crystals and crystal skeletons as Maas-Gari\'{e}py introduced in 2023. After developing calculus of those polynomials, we organize thoughts on major index, charge, depth, inversions with RSK correspondence and a bivariate factorial. Also, we revisit the theorem on internal zeros of fake degree polynomials by Billey--Konvalinka--Swanson (2020). These results altogether improve Gessel's expansion.

math.CO↗

arXiv2025

Bi-AQUA: Bilateral Control-Based Imitation Learning for Underwater Robot Arms via Lighting-Aware Action Chunking with Transformers

Underwater robotic manipulation remains challenging because lighting variation, color attenuation, scattering, and reduced visibility can severely degrade visuomotor policies. We present Bi-AQUA, the first underwater bilateral control-based imitation learning framework for robot arms that explicitly models lighting within the policy. Bi-AQUA integrates transformer-based bilateral action chunking with a hierarchical lighting-aware design composed of a label-free Lighting Encoder, FiLM-based visual feature modulation, and a lighting token for action conditioning. This design enables adaptation to static and dynamically changing underwater illumination while preserving the force-sensitive advantages of bilateral control, which are particularly important in long-horizon and contact-rich manipulation. Real-world experiments on underwater pick-and-place, drawer closing, and peg extraction tasks show that Bi-AQUA outperforms a bilateral baseline without lighting modeling and achieves robust performance under seen, unseen, and changing lighting conditions. These results highlight the importance of combining explicit lighting modeling with force-aware bilateral imitation learning for reliable underwater manipulation. For additional material, please check: https://mertcookimg.github.io/bi-aqua

cs.RO↗

arXiv2025

MR-UBi: Mixed Reality-Based Underwater Robot Arm Teleoperation System with Reaction Torque Indicator via Bilateral Control

We present a mixed reality-based underwater robot arm teleoperation system with a reaction torque indicator via bilateral control (MR-UBi). The reaction torque indicator (RTI) overlays a color and length-coded torque bar in the MR-HMD, enabling seamless integration of visual and haptic feedback during underwater robot arm teleoperation. User studies with sixteen participants compared MR-UBi against a bilateral-control baseline. MR-UBi significantly improved grasping-torque control accuracy, increasing the time within the optimal torque range and reducing both low and high grasping torque range during lift and pick-and-place tasks with objects of different stiffness. Subjective evaluations further showed higher usability (SUS) and lower workload (NASA--TLX). Overall, the results confirm that \textit{MR-UBi} enables more stable, accurate, and user-friendly underwater robot-arm teleoperation through the integration of visual and haptic feedback. For additional material, please check: https://mertcookimg.github.io/mr-ubi

cs.RO↗

arXiv2025

Disentangling Static and Dynamic Information for Reducing Static Bias in Action Recognition

Action recognition models rely excessively on static cues rather than dynamic human motion, which is known as static bias. This bias leads to poor performance in real-world applications and zero-shot action recognition. In this paper, we propose a method to reduce static bias by separating temporal dynamic information from static scene information. Our approach uses a statistical independence loss between biased and unbiased streams, combined with a scene prediction loss. Our experiments demonstrate that this method effectively reduces static bias and confirm the importance of scene prediction loss.

cs.CV↗

arXiv2025

Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation

We propose Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation (Bi-VLA), a novel framework that extends bilateral control-based imitation learning to handle more than one task within a single model. Conventional bilateral control methods exploit joint angle, velocity, torque, and vision for precise manipulation but require task-specific models, limiting their generality. Bi-VLA overcomes this limitation by utilizing robot joint angle, velocity, and torque data from leader-follower bilateral control with visual features and natural language instructions through SigLIP and FiLM-based fusion. We validated Bi-VLA on two task types: one requiring supplementary language cues and another distinguishable solely by vision. Real-robot experiments showed that Bi-VLA successfully interprets vision-language combinations and improves task success rates compared to conventional bilateral control-based imitation learning. Our Bi-VLA addresses the single-task limitation of prior bilateral approaches and provides empirical evidence that combining vision and language significantly enhances versatility. Experimental results validate the effectiveness of Bi-VLA in real-world tasks. For additional material, please visit the website: https://mertcookimg.github.io/bi-vla/

cs.RO↗

arXiv2025

Spatially and Dynamically Extended Molecular Gas in Stephan's Quintet Revealed by ALMA CO(1-0) Total Power Mapping

We present ALMA Total Power CO(1-0) mapping of Stephan's Quintet (SQ), a prototypical compact galaxy group, with a uniform noise level at a spatial scale of ~25 kpc. These observations provide the first complete view of molecular gas across the whole system. Molecular gas is found to spread over a wide area (~120 x 80 kpc), mainly over the two main member galaxies (NGC7318B and 7319), but also in the shocked ridges between these galaxies, the tidal tail, and also in intergalactic regions north of the tail. The total CO(1-0) luminosity is $(2.47\pm0.12)\times10^9~\mathrm{K~km~s^{-1}~pc^2}$, corresponding to a molecular gas mass of $(1.07\pm0.05)\times10^{10}~M_\odot$ assuming the Galactic CO-to-H2 conversion factor. The global star formation efficiency of SQ is estimated at 0.29-0.70 $\mathrm{Gyr^{-1}}$, comparable to or lower than that of nearby star-forming galaxies. Molecular gas spans a velocity range of ~1300 km/s, which can be divided into three components (low, mid, high). The low- and mid-velocity components, linked to NGC7318B and the ridge, show relatively active star formation, whereas the high-velocity component, associated with NGC7319, shows suppressed star formation. Our mapping reveals molecular gas extending ~100 kpc in projection along the inner tail and north of it, containing $(1.64\pm0.08)\times10^9~M_\odot$ (15% of total) with low velocity dispersion (~20 km/s) and ongoing star formation. While previous studies suggested in situ molecular gas formation in the tail, our data suggest an additional contribution from gas stripped from NGC7319.

astro-ph.GA↗

arXiv2025

The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results

This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop aimed to bridge the gap between the theoretical promise of Disentangled Representation Learning (DRL) and its application in realistic scenarios, moving beyond synthetic benchmarks. DRL4Real focused on evaluating DRL methods in practical applications such as controllable generation, exploring advancements in model robustness, interpretability, and generalization. The workshop accepted 9 papers covering a broad range of topics, including the integration of novel inductive biases (e.g., language), the application of diffusion models to DRL, 3D-aware disentanglement, and the expansion of DRL into specialized domains like autonomous driving and EEG analysis. This summary details the workshop's objectives, the themes of the accepted papers, and provides an overview of the methodologies proposed by the authors.

cs.LG↗

arXiv2025

RSK correspondence for King tableaux with Berele insertion

We establish a bijective RSK correspondence of type C for King tableaux with Berele insertion as a reformulation of Sundaram's correspondence (1986). For its $Q$-symbol, we make use of semistandard oscillating tableaux (SSOT), a new object which Lee (2025) introduced. Further, we show hidden duality of Cauchy identity through RSK correspondences of type A and C. Finally, we prove that the generating function of SSOT is symmetric by constructing a new sort of Bender-Knuth involution.

math.CO↗

arXiv2025

Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers

We present Bi-LAT, a novel imitation learning framework that unifies bilateral control with natural language processing to achieve precise force modulation in robotic manipulation. Bi-LAT leverages joint position, velocity, and torque data from leader-follower teleoperation while also integrating visual and linguistic cues to dynamically adjust applied force. By encoding human instructions such as "softly grasp the cup" or "strongly twist the sponge" through a multimodal Transformer-based model, Bi-LAT learns to distinguish nuanced force requirements in real-world tasks. We demonstrate Bi-LAT's performance in (1) unimanual cup-stacking scenario where the robot accurately modulates grasp force based on language commands, and (2) bimanual sponge-twisting task that requires coordinated force control. Experimental results show that Bi-LAT effectively reproduces the instructed force levels, particularly when incorporating SigLIP among tested language encoders. Our findings demonstrate the potential of integrating natural language cues into imitation learning, paving the way for more intuitive and adaptive human-robot interaction. For additional material, please visit: https://mertcookimg.github.io/bi-lat/

cs.RO↗

arXiv2025

MRHaD: Mixed Reality-based Hand-Drawn Map Editing Interface for Mobile Robot Navigation

Mobile robot navigation systems are increasingly relied upon in dynamic and complex environments, yet they often struggle with map inaccuracies and the resulting inefficient path planning. This paper presents MRHaD, a Mixed Reality-based Hand-drawn Map Editing Interface that enables intuitive, real-time map modifications through natural hand gestures. By integrating the MR head-mounted display with the robotic navigation system, operators can directly create hand-drawn restricted zones (HRZ), thereby bridging the gap between 2D map representations and the real-world environment. Comparative experiments against conventional 2D editing methods demonstrate that MRHaD significantly improves editing efficiency, map accuracy, and overall usability, contributing to safer and more efficient mobile robot operations. The proposed approach provides a robust technical foundation for advancing human-robot collaboration and establishing innovative interaction models that enhance the hybrid future of robotics and human society. For additional material, please check: https://mertcookimg.github.io/mrhad/

cs.RO↗

arXiv2024

ALPHA-$\alpha$ and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System

Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challenges in tasks that require force information/control, like carefully grasping fragile or varying-hardness objects. As the need for diverse controls increases, there are demand for low-cost bimanual robots that consider various motor inputs. To address these challenges, we introduce Bilateral Control-Based Imitation Learning via Action Chunking with Transformers(Bi-ACT) and"A" "L"ow-cost "P"hysical "Ha"rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation (ALPHA-$\alpha$). Bi-ACT leverages bilateral control to utilize both position and force information, enhancing the robot's adaptability to object characteristics such as hardness, shape, and weight. The concept of ALPHA-$\alpha$ is affordability, ease of use, repairability, ease of assembly, and diverse control modes (position, velocity, torque), allowing researchers/developers to freely build control systems using ALPHA-$\alpha$. In our experiments, we conducted a detailed analysis of Bi-ACT in unimanual manipulation tasks, confirming its superior performance and adaptability compared to Bi-ACT without force control. Based on these results, we applied Bi-ACT to bimanual manipulation tasks. Experimental results demonstrated high success rates in coordinated bimanual operations across multiple tasks. The effectiveness of the Bi-ACT and ALPHA-$\alpha$ can be seen through comprehensive real-world experiments. Video available at: https://mertcookimg.github.io/alpha-biact/

cs.RO↗

arXiv2024

DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images

Autonomous robot manipulation is a complex and continuously evolving robotics field. This paper focuses on data augmentation methods in imitation learning. Imitation learning consists of three stages: data collection from experts, learning model, and execution. However, collecting expert data requires manual effort and is time-consuming. Additionally, as sensors have different data acquisition intervals, preprocessing such as downsampling to match the lowest frequency is necessary. Downsampling enables data augmentation and also contributes to the stabilization of robot operations. In light of this background, this paper proposes the Data Augmentation Method for Bilateral Control-Based Imitation Learning with Images, called "DABI". DABI collects robot joint angles, velocities, and torques at 1000 Hz, and uses images from gripper and environmental cameras captured at 100 Hz as the basis for data augmentation. This enables a tenfold increase in data. In this paper, we collected just 5 expert demonstration datasets. We trained the bilateral control Bi-ACT model with the unaltered dataset and two augmentation methods for comparative experiments and conducted real-world experiments. The results confirmed a significant improvement in success rates, thereby proving the effectiveness of DABI. For additional material, please check https://mertcookimg.github.io/dabi

cs.RO↗

arXiv2024

BSL: Navigation Method Considering Blind Spots Based on ROS Navigation Stack and Blind Spots Layer for Mobile Robot

This paper proposes a navigation method considering blind spots based on the robot operating system (ROS) navigation stack and blind spots layer (BSL) for a wheeled mobile robot. In this paper, environmental information is recognized using a laser range finder (LRF) and RGB-D cameras. Blind spots occur when corners or obstacles are present in the environment, and may lead to collisions if a human or object moves toward the robot from these blind spots. To prevent such collisions, this paper proposes a navigation method considering blind spots based on the local cost map layer of the BSL for the wheeled mobile robot. Blind spots are estimated by utilizing environmental data collected through RGB-D cameras. The navigation method that takes these blind spots into account is achieved through the implementation of the BSL and a local path planning method that employs an enhanced cost function of dynamic window approach. The effectiveness of the proposed method was further demonstrated through simulations and experiments.

cs.RO↗

arXiv2024

MRNaB: Mixed Reality-based Robot Navigation Interface using Optical-see-through MR-beacons

Recent advancements in robotics have led to the development of numerous interfaces to enhance the intuitiveness of robot navigation. However, the reliance on traditional 2D displays imposes limitations on the simultaneous visualization of information. Mixed Reality (MR) technology addresses this issue by enhancing the dimensionality of information visualization, allowing users to perceive multiple pieces of information concurrently. This paper proposes the Mixed Reality-based Robot Navigation Interface using an Optical-see-through MR-beacons (MRNaB), a novel approach that uses MR-beacons created with an ``air tap'', situated in the real world. This beacon is persistent, enabling multi-destination visualization and functioning as a signal transmitter for robot navigation, eliminating the need for repeated navigation inputs. Our system is mainly constructed into four primary functions: ``Add'', ``Move'', ``Delete'', and ``Select''. These allow for the addition of MR-beacons, location movement, its deletion, and the selection of MR-beacons for navigation purposes, respectively. To validate the effectiveness, we conducted comprehensive experiments comparing MRNaB with traditional 2D navigation systems. The results show significant improvements in user performance, both objectively and subjectively, confirming that the MRNaB enhances navigation efficiency and user experience. For additional material, please check: https://mertcookimg.github.io/mrnab

cs.RO↗