6 papers
Continue or Replan? Bernoulli-Continuation Policy Learning for Adaptive Horizon Execution
Weichen Xu, Zhenhua Liu, Lin Luo +8
Existing chunk-based Vision-Language-Action (VLA) models execute a fixed number of actions (i.e., execution horizon) before replanning, turning replanning into a task-agnostic peri…
FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation
Ruicheng Li, Qixiu Li, Ruichun Ma +8
Vision-language-action (VLA) models have achieved impressive generalization in robotic manipulation, and recent memory-augmented VLAs have relaxed the Markovian assumption by condi…
MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation
Xingyuming Liu, Ruichun Ma, Heyu Guo +7
Humans naturally leverage diverse sensing modalities to interact with the physical world, while most Vision-Language-Action (VLA) models for robotics rely solely on RGB observation…
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos
Qixiu Li, Yu Deng, Yaobo Liang +14
This paper presents a novel approach for pretraining robotic manipulation Vision-Language-Action (VLA) models using a large corpus of unscripted real-life video recordings of human…
UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping
Wenbo Wang, Fangyun Wei, Lei Zhou +9
We introduce UniGraspTransformer, a universal Transformer-based network for dexterous robotic grasping that simplifies training while enhancing scalability and performance. Unlike…