arXiv subjects

Kazuhito Tanaka

Publications and source records attributed to Kazuhito Tanaka.

6 recordsLinked to original sources

arXiv2026

D-CLING: Prior-Preserving Depth-Conditioned Fine-Tuning for Navigation Foundation Models

Navigation Foundation Models (NFMs) trained on large cross-embodied datasets have demonstrated powerful generalizability in various scenarios. Adopting in-domain fine-tuning for an NFM efficiently calibrates the visuomotor policy, promising further improvement even in a novel scenario. However, the fine-tuned models still suffer from poor obstacle avoidance or fail to properly reach the provided goals. Furthermore, model updates using a small subset of data typically erode the pre-trained prior, compromising the pre-training generalization. Consequently, fine-tuning deteriorates the capability of the model for robust and accurate navigation. In this work, we present a novel fine-tuning method that leverages large-scale pre-training while efficiently learning in novel setups, such as environments or camera configurations. In particular, inspired by ControlNet, we fine-tune an NFM by attaching a trainable copy of the pre-trained backbone using zero-initialized residual pathways, thereby learning geometric cues. This design enables the model to efficiently acquire in-domain geometry while preserving pre-trained knowledge across various behaviors. Despite its simplicity, our comprehensive evaluation of real-world navigation suggests that our proposal effectively enables robust long-horizon navigation with minimal collisions and human intervention. Additionally, our offline analysis shows that the proposed method maintains or further improves action prediction capabilities beyond the fine-tuned dataset, providing a key insight into continual learning for general navigation. The project page: https://toyotafrc.github.io/DCLING-Proj/

cs.RO↗

arXiv2024

Multimodal Active Measurement for Human Mesh Recovery in Close Proximity

For physical human-robot interactions (pHRI), a robot needs to estimate the accurate body pose of a target person. However, in these pHRI scenarios, the robot cannot fully observe the target person's body with equipped cameras because the target person must be close to the robot for physical interaction. This close distance leads to severe truncation and occlusions and thus results in poor accuracy of human pose estimation. For better accuracy in this challenging environment, we propose an active measurement and sensor fusion framework of the equipped cameras with touch and ranging sensors such as 2D LiDAR. Touch and ranging sensor measurements are sparse but reliable and informative cues for localizing human body parts. In our active measurement process, camera viewpoints and sensor placements are dynamically optimized to measure body parts with higher estimation uncertainty, which is closely related to truncation or occlusion. In our sensor fusion process, assuming that the measurements of touch and ranging sensors are more reliable than the camera-based estimations, we fuse the sensor measurements to the camera-based estimated pose by aligning the estimated pose towards the measured points. Our proposed method outperformed previous methods on the standard occlusion benchmark with simulated active measurement. Furthermore, our method reliably estimated human poses using a real robot, even with practical constraints such as occlusion by blankets.

cs.RO↗

arXiv2024

CLIP-Clique: Graph-based Correspondence Matching Augmented by Vision Language Models for Object-based Global Localization

This letter proposes a method of global localization on a map with semantic object landmarks. One of the most promising approaches for localization on object maps is to use semantic graph matching using landmark descriptors calculated from the distribution of surrounding objects. These descriptors are vulnerable to misclassification and partial observations. Moreover, many existing methods rely on inlier extraction using RANSAC, which is stochastic and sensitive to a high outlier rate. To address the former issue, we augment the correspondence matching using Vision Language Models (VLMs). Landmark discriminability is improved by VLM embeddings, which are independent of surrounding objects. In addition, inliers are estimated deterministically using a graph-theoretic approach. We also incorporate pose calculation using the weighted least squares considering correspondence similarity and observation completeness to improve the robustness. We confirmed improvements in matching and pose estimation accuracy through experiments on ScanNet and TUM datasets.

cs.CV↗

arXiv2024

CLIP-Loc: Multi-modal Landmark Association for Global Localization in Object-based Maps

This paper describes a multi-modal data association method for global localization using object-based maps and camera images. In global localization, or relocalization, using object-based maps, existing methods typically resort to matching all possible combinations of detected objects and landmarks with the same object category, followed by inlier extraction using RANSAC or brute-force search. This approach becomes infeasible as the number of landmarks increases due to the exponential growth of correspondence candidates. In this paper, we propose labeling landmarks with natural language descriptions and extracting correspondences based on conceptual similarity with image observations using a Vision Language Model (VLM). By leveraging detailed text information, our approach efficiently extracts correspondences compared to methods using only object categories. Through experiments, we demonstrate that the proposed method enables more accurate global localization with fewer iterations compared to baseline methods, exhibiting its efficiency.

cs.CV↗

arXiv2016

Observation of large spin accumulation voltages in non-degenerate Si spin devices due to spin drift effect: Experiments and theory

A large spin-accumulation voltage of more than 1.5 mV at 1 mA, i.e., a magnetoresistance of 1.5 Ω, was measured by means of the local three-terminal magnetoresistance in nondegenerate Si-based lateral spin valves (LSVs) at room temperature. This is the largest spin-accumulation voltage measured in semiconductor-based LSVs. The modified spin drift-diffusion model, which successfully accounts for the spin drift effect, explains the large spin-accumulation voltage and significant bias-current-polarity dependence. The model also shows that the spin drift effect enhances the spin-dependent magnetoresistance in the electric two terminal scheme. This finding provides a useful guiding principle for spin metal-oxide semiconductor field-effect transistor (MOSFET) operations.

cond-mat.mtrl-sci↗

arXiv2015

Room-temperature operation of Si spin MOSFET with high on/off spin signal ratio

We experimentally demonstrate a Si spin metal-oxide-semiconductor field-effect transistor (MOSFET) that exhibits a high on/off ratio of source-drain current and spin signals at room temperature. The spin channel is non-degenerate n-type Si, and an effective application of gate voltage in the back-gated structure allows the spin MOSFET operation. This achievement can pave the way to practical use of the Si spin MOSFET.

cond-mat.mes-hall↗