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QinPeng Yi

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AAAI Conference 2025 Conference Paper

GMAP: Generalized Manipulation of Articulated Objects in Robotic Using Pre-trained Model

Hongliang Zeng
Ping Zhang
Fang Li
QinPeng Yi
Tingyu Ye
Jiahua Wang

Perception and interaction with articulated objects present a unique challenge for service robots. Although recent research has emphasized understanding articulated shapes and affordance proposals, existing methods only address isolated aspects, failing to develop comprehensive strategies for robotic perception and manipulation of articulated objects. To bridge this gap, we propose GMAP, which systematically integrates the entire process from command to perception and manipulation. Specifically, we first perform precise part-level segmentation of the object and identify the geometric and kinematic parameters of articulated joints. Then, by evaluating point-level affordance proposals, we determine the interaction poses for the robot's end-effector. Finally, the robot's execution trajectory is dynamically computed by combining commands with joint parameters and interaction points. Additionally, a key innovation of GMAP is addressing the scarcity of annotated data. We designed a multi-scale point cloud feature extraction module and introduced pre-training and fine-tuning techniques, significantly enhancing the generalization capability of the perception model. Extensive experiments demonstrate that GMAP achieves state-of-the-art (SOTA) performance in both the perception and manipulation of articulated objects and adapts to real-world scenarios.

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ECAI Conference 2025 Conference Paper

Training Robotic Self-Evolving with GRPO

Qinpeng Yi
Ping Zhang
Junwei Chen

Current embodied robots heavily depend on pre-trained models, whose capabilities are inherently constrained by the data they were originally trained on. However, truly intelligent robots are expected to improve themselves autonomously when encountering novel environments where these pre-trained models fall short. This is the capability we define as self-evolving ability. In this paper, we investigate the self-evolving capacity of robotic vision models. Specifically, we simulate this process using the R3ED dataset and propose a training framework in which a policy learns to navigate through unfamiliar environments to collect informative data that can be used to refine the vision model. Our training pipeline is built upon the GRPO algorithm and incorporates historical states into the policy design to enhance contextual awareness. Furthermore, we introduce a novel reward mechanism based on supervision discrepancy to guide effective data collection. Experimental results validate the effectiveness of our proposed reinforcement training strategy. Our work highlights the potential of designing intelligent robots that can improve themselves without the intervene of human beings. Nevertheless, we acknowledge that robotic self-evolving remains a nascent and underexplored area, with significant room for further future research and the discovery of more optimal approaches.

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