Author name cluster

Kexiang Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2026 Conference Paper

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors

Haoyu Zhao
Linghao Zhuang
Xingyue Zhao
Cheng Zeng
Haoran Xu
Yuming Jiang
Jun CEN
Kexiang Wang

A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors and object affordances. In the first stage, a trajectory imitator is pre-trained on a large corpus of human hand motions to instill a strong prior for natural movement. In the second stage, a residual module is trained to adapt these general human-like motions to specific object instances. This refinement is critically guided by two components: our Negative Affordance-aware Segmentation (NAA) module, which identifies functionally inappropriate contact regions, and a privileged teacher-student distillation process that ensures the final vision-based policy is highly successful. Extensive experiments demonstrate that AffordDex not only achieves universal dexterous grasping but also remains remarkably human-like in posture and functionally appropriate in contact location. As a result, AffordDex significantly outperforms state-of-the-art baselines across seen objects, unseen instances, and even entirely novel categories.

PDF Details DOI

AAAI Conference 2018 Conference Paper

Table-to-Text Generation by Structure-Aware Seq2seq Learning

Tianyu Liu
Kexiang Wang
Lei Sha
Baobao Chang
Zhifang Sui

Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of ﬁeld-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of ﬁeld-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a ﬁeld gate and its corresponding ﬁeld value in order to incorporate ﬁeld information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and ﬁeld level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the WIKIBIO dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on https: //github. com/tyliupku/wiki2bio.

PDF Details