1 citations · 1 across the 9 of their papers we have counts for
9 papers
Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policies
Yue Yang, Diego Romeres, Chiori Hori +3
Vision-Language-Action (VLA) policies fuse multimodal sensory inputs, but training on limited and homogeneous robot demonstrations encourages spurious inter-sensor correlations rat…
DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation
Yu Fang, Wanxi Dong, Jiaqi Liu +7
Reinforcement learning holds great promise for improving robot policies beyond the limits of imitation learning. However, its practical adoption remains bottlenecked by the lack of…
FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
Chenyang Ma, Yue Yang, Radu Corcodel +4
Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanu…
WatchAct: A Benchmark for Behavior-Grounded Robot Manipulation
Baiqi Li, Ce Zhang, Yu Fang +4
A robot working alongside people must reason about what they have done, in what order, and with what intent. Video carries the spatial layouts, object histories, and gestures that…
PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps
Junlin Long, Zeyu Zhang, Xu Deng +5
Embodied visual navigation, where an agent perceives a complex environment and acts to reach a goal from raw sensory input, underpins a wide range of applications such as household…
ReBot: Scaling Robot Learning with Real-to-Sim-to-Real Robotic Video Synthesis
Yu Fang, Yue Yang, Xinghao Zhu +4
Vision-language-action (VLA) models present a promising paradigm by training policies directly on real robot datasets like Open X-Embodiment. However, the high cost of real-world d…
BOSS: Benchmark for Observation Space Shift in Long-Horizon Task
Yue Yang, Linfeng Zhao, Mingyu Ding +2
Robotics has long sought to develop visual-servoing robots capable of completing previously unseen long-horizon tasks. Hierarchical approaches offer a pathway for achieving this go…
ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning
Yue Yang, Bryce Ikeda, Gedas Bertasius +1
Robot Imitation Learning (IL) is a crucial technique in robot learning, where agents learn by mimicking human demonstrations. However, IL encounters scalability challenges stemming…
Augmented Reality Demonstrations for Scalable Robot Imitation Learning
Yue Yang, Bryce Ikeda, Gedas Bertasius +1
Robot Imitation Learning (IL) is a widely used method for training robots to perform manipulation tasks that involve mimicking human demonstrations to acquire skills. However, its…