Author name cluster

Ran Yu

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5 papers

2 author rows

IROS Conference 2025 Conference Paper

AirTouch: A Low-Cost Versatile Visuotactile Feedback System for Enhanced Robotic Teleoperation

Shoujie Li
Xingting Li
Yan Huang
Ken Jiankun Zheng
Ran Yu
Xueqian Wang 0001
Wenbo Ding 0001

Vision-based teleoperation systems are widely used due to their cost-effectiveness and intuitive operation. However, these systems often suffer from challenges such as hand occlusions, environmental variability, and the lack of tactile feedback, limiting their precision and applicability in complex tasks. To address these limitations, we present Air-Touch, a novel, low-cost visuotactile teleoperation system that integrates air pressure-based tactile feedback with lightweight hand pose estimation. AirTouch features an inflatable tactile bubble that provides adjustable feedback through closed-loop pneumatic control, enhancing the operator’s sense of interaction with remote environments. The system’s robust hand-tracking algorithm ensures accurate control even under dynamic and occlusion-prone conditions, while its hardware design eliminates the need for wearable devices, enabling intuitive operation. AirTouch supports a wide range of robotic end-effectors, including dexterous hands, parallel grippers, and suction cups, demonstrating versatility across multiple platforms. Extensive experiments validate AirTouch’s performance, achieving high precision in hand pose estimation and a 91% success rate in complex teleoperation tasks, all with a hardware cost as low as $39. These results highlight AirTouch as a scalable and practical solution for enhancing robotic teleoperation across industrial, medical, and hazardous scenarios.

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ICRA Conference 2025 Conference Paper

Depth Restoration of Hand-Held Transparent Objects for Human-to-Robot Handover

Ran Yu
Haixin Yu
Shoujie Li
Yan Huang
Ziwu Song
Wenbo Ding 0001

Transparent objects are common in daily life, while their optical properties pose challenges for RGB-D cameras to capture accurate depth information. This issue is further amplified when these objects are hand-held, as hand occlusions further complicate depth estimation. For assistant robots, however, accurately perceiving hand-held transparent objects is critical to effective human-robot interaction. This paper presents a Hand-Aware Depth Restoration (HADR) method based on creating an implicit neural representation function from a single RGB-D image. The proposed method utilizes hand posture as an important guidance to leverage semantic and geometric information of hand-object interaction. To train and evaluate the proposed method, we create a highfidelity synthetic dataset named TransHand- $\mathbf{1 4 K}$ with a real-tosim data generation scheme. Experiments show that our method has better performance and generalization ability compared with existing methods. We further develop a real-world human-to-robot handover system based on HADR, demonstrating its potential in human-robot interaction applications.

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NeurIPS Conference 2025 Conference Paper

HCRMP: An LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving

Zhiwen Chen
Hanming Deng
Zhuoren Li
Huanxi Wen
Guizhe Jin
Ran Yu
Bo Leng

Integrating the understanding and reasoning capabilities of Large Language Models (LLM) with the self-learning capabilities of Reinforcement Learning (RL) enables more reliable driving performance under complex driving conditions. There has been a lot of work exploring LLM-Dominated RL methods in the field of autonomous driving motion planning. These methods, which utilize LLM to directly generate policies or provide decisive instructions during policy learning of RL agent, are centrally characterized by an over-reliance on LLM outputs. However, LLM outputs are susceptible to hallucinations. Evaluations show that state-of-the-art LLM indicates a non-hallucination rate of only approximately 57. 95\% when assessed on essential driving-related tasks. Thus, in these methods, hallucinations from the LLM can directly jeopardize the performance of driving policies. This paper argues that maintaining relative independence between the LLM and the RL is vital for solving the hallucinations problem. Consequently, this paper is devoted to propose a novel LLM-Hinted RL paradigm. The LLM is used to generate semantic hints for state augmentation and policy optimization to assist RL agent in motion planning, while the RL agent counteracts potential erroneous semantic indications through policy learning to achieve excellent driving performance. Based on this paradigm, we propose the HCRMP (LLM-Hinted Contextual Reinforcement Learning Motion Planner) architecture, which is designed that includes ①Augmented Semantic Representation Module to extend state space. ②Contextual Stability Anchor Module enhances the reliability of multi-critic weight hints by utilizing information from the knowledge base. ③Semantic Cache Module is employed to seamlessly integrate LLM low-frequency guidance with RL high-frequency control. Extensive experiments in CARLA validate HCRMP's strong overall driving performance. HCRMP achieves a task success rate of up to 80. 3\% under diverse driving conditions with different traffic densities. Under safety-critical driving conditions, HCRMP significantly reduces the collision rate by 11. 4\%, which effectively improves the driving performance in complex scenarios.

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ICRA Conference 2025 Conference Paper

PUGS: Zero-Shot Physical Understanding with Gaussian Splatting

Yinghao Shuai
Ran Yu
Yuantao Chen
Zijian Jiang
Xiaowei Song
Nan Wang 0041
Jv Zheng
Jianzhu Ma

Current robotic systems can understand the categories and poses of objects well. But understanding physical properties like mass, friction, and hardness, in the wild, remains challenging. We propose a new method that reconstructs 3D objects using the Gaussian splatting representation and predicts various physical properties in a zero-shot manner. We propose two techniques during the reconstruction phase: a geometryaware regularization loss function to improve the shape quality and a region-aware feature contrastive loss function to promote region affinity. Two other new techniques are designed during inference: a feature-based property propagation module and a volume integration module tailored for the Gaussian representation. Our framework is named as zero-shot physical understanding with Gaussian splatting, or PUGS. PUGS achieves new state-of-the-art results on the standard benchmark of ABO-500 mass prediction. We provide extensive quantitative ablations and qualitative visualization to demonstrate the mechanism of our designs. We show the proposed methodology can help address challenging real-world grasping tasks. Our codes, data, and models are available at https://github.com/EverNorif/PUGS

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ICRA Conference 2024 Conference Paper

SATac: A Thermoluminescence Enabled Tactile Sensor for Concurrent Perception of Temperature, Pressure, and Shear

Ziwu Song
Ran Yu
Xuan Zhang
Kit Wa Sou
Shilong Mu
Dengfeng Peng
Xiao-Ping Zhang 0002
Wenbo Ding 0001

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multi-modal vision-based tactile sensor, SATac, which can simultaneously perceive information on temperature, pressure, and shear. SATac utilizes the thermoluminescence of strontium aluminate to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing the Voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.

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