4 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
What Matters for Latent Actions in Robot Learning
Xizhou Bu, Qingda Hu, Lei Zhou +13
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact s…
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
Lianghao Luo, Xizhou Bu, Ruyan Liu +5
Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or even failed trajectories. Dis…
Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu +11
Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation b…
Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding
Qingda Hu, Ziheng Qiu, Zijun Xu +7
State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the a…
LAOF: Robust Latent Action Learning with Optical Flow Constraints
Xizhou Bu, Jiexi Lyu, Fulei Sun +3
Learning latent actions from large-scale videos is crucial for the pre-training of scalable embodied foundation models, yet existing methods often struggle with action-irrelevant d…
Aligning Human Intent from Imperfect Demonstrations with Confidence-based Inverse soft-Q Learning
Xizhou Bu, Wenjuan Li, Zhengxiong Liu +2
Imitation learning attracts much attention for its ability to allow robots to quickly learn human manipulation skills through demonstrations. However, in the real world, human demo…