1 citations · 2 across the 21 of their papers we have counts for
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Test-Time Training as Residual Memory for Robot Policies
Haoxuan Wang, Gengyu Zhang, Ramana Rao Kompella +2
Memory is essential for long-horizon robotic manipulation, where successful actions may depend on past events that are no longer recoverable from the current observation. As episod…
Looking Back to Move Forward: Temporal Verification for Generative Robot Policies
Haoxuan Wang, Wayne Wu, Yan Yan +1
Generative policies have emerged as a promising paradigm for robot learning, combining expressive generative action modeling with scalable imitation learning from large demonstrati…
Cue the Flow: Steering Flow-Matching Policies for Open-World Delivery Manipulation
Haoxuan Wang, Griffin Galimi, Junhua Huang +4
Open-world goods delivery requires mobile manipulators to follow free-form user instructions and manipulate potentially novel objects. Existing dual-system approaches use high-leve…
VLA Knows Its Limits: Adaptive Execution Horizons for Robot Policies
Haoxuan Wang, Gengyu Zhang, Yan Yan +2
Action chunking has recently emerged as a standard practice in flow-based Vision-Language-Action (VLA) models. However, the effect and choice of the execution horizon - the number…
Real-Time Robot Execution with Masked Action Chunking
Haoxuan Wang, Gengyu Zhang, Yan Yan +3
Real-time execution is essential for cyber-physical systems such as robots. These systems operate in dynamic real-world environments where even small delays can undermine responsiv…