6 papers
Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models
Yuhan Liu, Xinyu Zhang, Litao Liu +1
Vision-Language-Action (VLA) policies offer strong general-purpose manipulation priors, but often fail on tight-tolerance, contact-rich assembly due to long-horizon credit assignme…
Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation
Litao Liu, Yifan Han, Pengfei Yi +9
Task-conditioned manipulation requires grounding instructions to task-relevant functional parts rather than object categories. This setting is scene-dependent and often one-to-many…
DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
Sixu Lin, Yunpeng Qing, Litao Liu +4
Recent progress in Reinforcement Learning (RL) provides a principled approach to optimizing Vision-Language-Action (VLA) models, facilitating a shift from trajectory imitation to a…
Viewpoint Matters: Dynamically Optimizing Viewpoints with Masked Autoencoder for Visual Manipulation
Pengfei Yi, Yifan Han, Junyan Li +2
Robotic manipulation continues to be a challenge, and imitation learning (IL) enables robots to learn tasks from expert demonstrations. Current IL methods typically rely on fixed c…
A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning
Shaopeng Zhai, Qi Zhang, Tianyi Zhang +7
Robotic real-world reinforcement learning (RL) with vision-language-action (VLA) models is bottlenecked by sparse, handcrafted rewards and inefficient exploration. We introduce VLA…
FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation
Litao Liu, Wentao Wang, Yifan Han +5
Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a single policy. This simplifies the…