5 citations · 10 across the 9 of their papers we have counts for
10 papers
ROMA: LLM System for Real-World Object-Centric Multi-Sensory Active Perception
Ruoxuan Feng, Yutong Chen, Ruihua Song +4
Humans inherently understand the physical world through an active process. When sensory evidence is insufficient to infer physical properties, we naturally interact with the enviro…
AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception
Ruoxuan Feng, Yuxuan Zhou, Siyu Mei +6
Real-world contact-rich manipulation demands robots to perceive temporal tactile feedback, capture subtle surface deformations, and reason about object properties as well as force…
RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation
Shihan Wu, Xuecheng Liu, Shaoxuan Xie +81
Despite the critical role of bimanual manipulation in endowing robots with human-like dexterity, large-scale and diverse datasets remain scarce due to the significant hardware hete…
Phoenix: A Motion-based Self-Reflection Framework for Fine-grained Robotic Action Correction
Wenke Xia, Ruoxuan Feng, Dong Wang +1
Building a generalizable self-correction system is crucial for robots to recover from failures. Despite advancements in Multimodal Large Language Models (MLLMs) that empower robots…
AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors
Ruoxuan Feng, Jiangyu Hu, Wenke Xia +5
Visuo-tactile sensors aim to emulate human tactile perception, enabling robots to precisely understand and manipulate objects. Over time, numerous meticulously designed visuo-tacti…
Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulation
Ruoxuan Feng, Di Hu, Wenke Ma +1
Humans possess a remarkable talent for flexibly alternating to different senses when interacting with the environment. Picture a chef skillfully gauging the timing of ingredient ad…
Diagnosing and Re-learning for Balanced Multimodal Learning
Yake Wei, Siwei Li, Ruoxuan Feng +1
To overcome the imbalanced multimodal learning problem, where models prefer the training of specific modalities, existing methods propose to control the training of uni-modal encod…
Enhancing multimodal cooperation via sample-level modality valuation
Yake Wei, Ruoxuan Feng, Zihe Wang +1
One primary topic of multimodal learning is to jointly incorporate heterogeneous information from different modalities. However most models often suffer from unsatisfactory multimo…
MMCosine: Multi-Modal Cosine Loss Towards Balanced Audio-Visual Fine-Grained Learning
Ruize Xu, Ruoxuan Feng, Shi-Xiong Zhang +1
Audio-visual learning helps to comprehensively understand the world by fusing practical information from multiple modalities. However, recent studies show that the imbalanced optim…
Revisiting Pre-training in Audio-Visual Learning
Ruoxuan Feng, Wenke Xia, Di Hu
Pre-training technique has gained tremendous success in enhancing model performance on various tasks, but found to perform worse than training from scratch in some uni-modal situat…