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Hang Luo

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2 papers

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IROS Conference 2025 Conference Paper

ZBOT: A Novel Modular Robot Capable of Active Transformation from Snake to Bipedal Configuration through RL

Nanlin Zhou
Sikai Zhao
Hang Luo
Kai Han
Zhiyuan Yang
Jian Qi
Ning Zhao
Jie Zhao 0003

In recent years, significant progress has been made in the prototype design and control methodologies of modular snake robots. However, there is still relatively little research on the potential enabled by the active morphological transformation of robots. This paper presents a novel modular snake robot capable of morphing into a bipedal configuration. The robot, ZBOT, is composed of some independent and homogeneous unit modules (named ZBot) connected in series. Each ZBot module has a dual-motor-driven 1-DoF rotational joint, which can rotate continuously, provide a large output torque and achieve backlash elimination. There are four connection orientations between adjacent modules. This paper proposes an articulation configuration, which enables the snake robot to achieve the active transformation from a snake form to a bipedal form. Meanwhile, through reinforcement learning (RL), movements including the stand-up gait are trained and verified in the IsaacSim/Lab simulation environment. This research will advance snake robots beyond surface-dependent locomotion, endowing them with more possibilities, unlocking greater potential for versatile applications.

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AAAI Conference 2023 Conference Paper

Efficient Image Captioning for Edge Devices

Ning Wang
Jiangrong Xie
Hang Luo
Qinglin Cheng
Jihao Wu
Mingbo Jia
Linlin Li

Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object detectors) and complicated cross-modal fusion networks. To this end, we propose LightCap, a lightweight image captioner for resource-limited devices. The core design is built on the recent CLIP model for efficient image captioning. To be specific, on the one hand, we leverage the CLIP model to extract the compact grid features without relying on the time-consuming object detectors. On the other hand, we transfer the image-text retrieval design of CLIP to image captioning scenarios by devising a novel visual concept extractor and a cross-modal modulator. We further optimize the cross-modal fusion model and parallel prediction heads via sequential and ensemble distillations. With the carefully designed architecture, our model merely contains 40M parameters, saving the model size by more than 75% and the FLOPs by more than 98% in comparison with the current state-of-the-art methods. In spite of the low capacity, our model still exhibits state-of-the-art performance on prevalent datasets, e.g., 136.6 CIDEr on COCO Karpathy test split. Testing on the smartphone with only a single CPU, the proposed LightCap exhibits a fast inference speed of 188ms per image, which is ready for practical applications.

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