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Ruize Wang

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3 papers

2 author rows

IROS Conference 2025 Conference Paper

A Hybrid Mapping Method: Balancing Efficiency and Intuitiveness in Lateral Teleoperation

Yuwei Xie
Ruize Wang
Jiming Chen
Gaofeng Li

Mobile manipulators integrate the locomotion flexibility of quadruped robots with the operational capabilities of robotic manipulators. This integrated system is particularly effective for teleoperating explosive ordnance disposal (EOD) tasks in hazardous environments, enabling the safe handling of explosive devices. However, when the quadruped operates in narrow corridors or cluttered spaces, its ability to reposition is limited. This limitation, combined with targets located laterally relative to the robot, poses critical challenges for achieving rapid and intuitive teleoperation of the manipulator. Existing manipulator mapping methods either fail to support lateral teleoperation or lack proper coordinate transformations, leading to mismatches between the intended and actual movement directions of the leader and follower devices. This reduces operational intuitiveness and increases the cognitive load on human operators. To overcome these issues, we propose a hybrid mapping method that combines joint-space velocity control with Cartesian-space control. This method leverages joint-space velocity commands for rapid manipulator reorientation, while employing Cartesian-space commands to achieve precise end-effector teleoperation. Furthermore, we introduce a virtual base coordinate frame that adaptively adjusts in response to the manipulator’s reorientation. This adaptive compensation ensures that the visual feedback from the camera mounted on the end-effector remains consistent and intuitive. The proposed method was validated through experiments on a quadruped robot equipped with a manipulator in an EOD scenario. Results demonstrated significant improvements, including 100% success rate, 43. 9% task duration reduction, and 31. 7% NASA-TLX score decrease, indicating decreased cognitive load and enhanced task efficiency compared to baseline methods.

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IJCAI Conference 2021 Conference Paper

TCIC: Theme Concepts Learning Cross Language and Vision for Image Captioning

Zhihao Fan
Zhongyu Wei
Siyuan Wang
Ruize Wang
Zejun Li
Haijun Shan
Xuanjing Huang

Existing research for image captioning usually represents an image using a scene graph with low-level facts (objects and relations) and fails to capture the high-level semantics. In this paper, we propose a Theme Concepts extended Image Captioning (TCIC) framework that incorporates theme concepts to represent high-level cross-modality semantics. In practice, we model theme concepts as memory vectors and propose Transformer with Theme Nodes (TTN) to incorporate those vectors for image captioning. Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN. On the vision side, TTN is configured to take both scene graph based features and theme concepts as input for visual representation learning. On the language side, TTN is configured to take both captions and theme concepts as input for text representation re-construction. Both settings aim to generate target captions with the same transformer-based decoder. During the training, we further align representations of theme concepts learned from images and corresponding captions to enforce the cross-modality learning. Experimental results on MS COCO show the effectiveness of our approach compared to some state-of-the-art models.

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AAAI Conference 2020 Conference Paper

Storytelling from an Image Stream Using Scene Graphs

Ruize Wang
Zhongyu Wei
Piji Li
Qi Zhang
Xuanjing Huang

Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difﬁcult to interpret. We argue that translating each image into a graph-based semantic representation, i. e. , scene graph, which explicitly encodes the objects and relationships detected within image, would beneﬁt representing and describing images. To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local ﬁne-grained region representations of objects on scene graphs. To further model the interaction among images, on the cross-images level, a Temporal Convolution Network (TCN) is utilized to reﬁne the region representations along the temporal dimension. Then the relation-aware representations are fed into the Gated Recurrent Unit (GRU) with attention mechanism for story generation. Experiments are conducted on the public visual storytelling dataset. Automatic and human evaluation results indicate that our method achieves state-of-the-art.

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