51 citations · 166 across the 80 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…
ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies
Zhe Tao, Feiran Wang, Gaowen Liu +3
Robots need to anticipate how their actions will change the world, since manipulation success hinges on the resulting contacts and object motions. However, existing Vision-Language…
An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
Zhekai Duan, Kevin Ziyang Xie, Xinyu Tan +5
Adapting a pretrained Vision-Language-Action (VLA) model to a new robot, environment, or task requires demonstrations that are collected locally and often discarded. Federated lear…
Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?
Guoheng Sun, Kaixi Feng, Shwai He +8
Vision-Language-Action (VLA) models enable instruction-driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs whose capacity far exceeds wh…
Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems
Jiabao Ji, Yongchao Chen, Yang Zhang +4
Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints, including robot-robot collisions, robot-obstacle collisions, and…
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…