4 papers · 1 filter
Generalists Act, Specialists Intervene: Modular Stage-Selective Reinforcement Learning for Vision-Language-Action Manipulation
Chongyu Zhu, Jaden Hinds, Hyegang Kim +3
Vision-language-action (VLA) models often struggle in the precision-critical phases of multi-stage manipulation tasks. To mitigate this issue, VLA models can be used in conjunction…
WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
Chongyu Zhu, Ramy ElMallah, Hyegang Kim +5
Deformable Linear Objects (DLOs), such as wires and cables, are central to industrial assembly. Unlike rigid objects, whose state is captured by a 6-DoF pose, DLOs have an infinite…
Offline Discovery of Interpretable Skills from Multi-Task Trajectories
Chongyu Zhu, Mithun Vanniasinghe, Jiayu Chen +1
Hierarchical Imitation Learning is a powerful paradigm for acquiring complex robot behaviors from demonstrations. A central challenge, however, lies in discovering reusable skills…
Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Junhyuk So, Chiwoong Lee, Shinyoung Lee +2
Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies wi…