5 papers
VT-WAM: Visual-Tactile World Action Model for Contact-Rich Manipulation
Shuai Tian, Yupeng Zheng, Yuhang Zheng +7
Contact-rich manipulation requires policies to react to local deformation, pressure, slip, and friction, yet these cues are temporally sparse and often invisible in visual observat…
Posterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning
Yuhui Chen, Haoran Li, Zhennan Jiang +4
Expressive generative models have advanced robotic manipulation by capturing complex, multi-modal action distributions over temporally extended trajectories. However, fine-tuning t…
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
Xin Liu, Yixuan Li, Yuhui Chen +3
Designing suitable rewards poses a significant challenge in reinforcement learning (RL), especially for embodied manipulation. Trajectory success rewards are suitable for human jud…
Towards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning
Yuan Liu, Haoran Li, Shuai Tian +5
Pretrained on large-scale and diverse datasets, VLA models demonstrate strong generalization and adaptability as general-purpose robotic policies. However, Supervised Fine-Tuning (…
World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
Zhennan Jiang, Kai Liu, Yuxin Qin +6
Robotic manipulation policies are commonly initialized through imitation learning, but their performance is limited by the scarcity and narrow coverage of expert data. Reinforcemen…