Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration

This paper addresses the limitations of current humanoid robot control frameworks, which primarily rely on reactive mechanisms and lack autonomous interaction capabilities due to data scarcity. We propose Humanoid-VLA, a novel framework that integrates language understanding, egocentric scene perception, and motion control, enabling universal humanoid control. Humanoid-VLA begins with language-motion pre-alignment using non-egocentric human motion datasets paired with textual descriptions, allowing the model to learn universal motion patterns and action semantics. We then incorporate egocentric visual context through a parameter efficient video-conditioned fine-tuning, enabling context-aware motion generation. Furthermore, we introduce a self-supervised data augmentation strategy that automatically generates pseudoannotations directly derived from motion data. This process converts raw motion sequences into informative question-answer pairs, facilitating the effective use of large-scale unlabeled video data. Built upon whole-body control architectures, extensive experiments show that Humanoid-VLA achieves object interaction and environment exploration tasks with enhanced contextual awareness, demonstrating a more human-like capacity for adaptive and intelligent engagement.

PGA-SciRE: Harnessing LLM on Data Augmentation for Enhancing Scientific Relation Extraction

Relation Extraction (RE) aims at recognizing the relation between pairs of entities mentioned in a text. Advances in LLMs have had a tremendous impact on NLP. In this work, we propose a textual data augmentation framework called PGA for improving the performance of models for RE in the scientific domain. The framework introduces two ways of data augmentation, utilizing a LLM to obtain pseudo-samples with the same sentence meaning but with different representations and forms by paraphrasing the original training set samples. As well as instructing LLM to generate sentences that implicitly contain information about the corresponding labels based on the relation and entity of the original training set samples. These two kinds of pseudo-samples participate in the training of the RE model together with the original dataset, respectively. The PGA framework in the experiment improves the F1 scores of the three mainstream models for RE within the scientific domain. Also, using a LLM to obtain samples can effectively reduce the cost of manually labeling data.