2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…