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Jiahua Wang

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3 papers

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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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IJCAI Conference 2024 Conference Paper

MARS: Multimodal Active Robotic Sensing for Articulated Characterization

Hongliang Zeng
Ping Zhang
Chengjiong Wu
Jiahua Wang
Tingyu Ye
Fang Li

Precise perception of articulated objects is vital for empowering service robots. Recent studies mainly focus on point cloud, a single-modal approach, often neglecting vital texture and lighting details and assuming ideal conditions like optimal viewpoints, unrepresentative of real-world scenarios. To address these limitations, we introduce MARS, a novel framework for articulated object characterization. It features a multi-modal fusion module utilizing multi-scale RGB features to enhance point cloud features, coupled with reinforcement learning-based active sensing for autonomous optimization of observation viewpoints. In experiments conducted with various articulated object instances from the PartNet-Mobility dataset, our method outperformed current state-of-the-art methods in joint parameter estimation accuracy. Additionally, through active sensing, MARS further reduces errors, demonstrating enhanced efficiency in handling suboptimal viewpoints. Furthermore, our method effectively generalizes to real-world articulated objects, enhancing robot interactions. Code is available at https: //github. com/robhlzeng/MARS.

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ICLR Conference 2023 Conference Paper

Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

Chenxiao Yang
Qitian Wu
Jiahua Wang
Junchi Yan

Graph neural networks (GNNs), as the de-facto model class for representation learning on graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional message passing layers to allow features to flow across nodes. While conventional wisdom commonly attributes the success of GNNs to their advanced expressivity, we conjecture that this is not the main cause of GNNs' superiority in node-level prediction tasks. This paper pinpoints the major source of GNNs' performance gain to their intrinsic generalization capability, by introducing an intermediate model class dubbed as P(ropagational)MLP, which is identical to standard MLP in training, but then adopts GNN's architecture in testing. Intriguingly, we observe that PMLPs consistently perform on par with (or even exceed) their GNN counterparts, while being much more efficient in training. This finding provides a new perspective for understanding the learning behavior of GNNs, and can be used as an analytic tool for dissecting various GNN-related research problems including expressivity, generalization, over-smoothing and heterophily. As an initial step to analyze PMLP, we show its essential difference to MLP at infinite-width limit lies in the NTK feature map in the post-training stage. Moreover, through extrapolation analysis (i.e., generalization under distribution shifts), we find that though most GNNs and their PMLP counterparts cannot extrapolate non-linear functions for extreme out-of-distribution data, they have greater potential to generalize to testing data near the training data support as natural advantages of the GNN architecture used for inference.

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