6 papers
ActionMap: Robot Policy Learning via Voxel Action Heatmap
Pei Yang, Hai Ci, Yanzhe Chen +3
Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state int…
MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU
Kun Cheng, Songshuo Lu, Sicong Liao +7
Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggle with this task, while execut…
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
Yanzhe Chen, Kevin Yuchen Ma, Qi Lv +4
While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to bridge the embodiment gap. Since r…
Hume: Introducing System-2 Thinking in Visual-Language-Action Model
Haoming Song, Delin Qu, Yuanqi Yao +9
Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancem…
Round Attention: A Novel Round-Level Attention Mechanism to Accelerate LLM Inference
Yaohua Tang, Zhicheng Hu, Kun Cheng +4
The increasing context window size in large language models (LLMs) has improved their ability to handle complex, long-text tasks. However, as the conversation rounds continue, it i…
Few-Shot Vision-Language Action-Incremental Policy Learning
Mingchen Song, Xiang Deng, Guoqiang Zhong +5
Recently, Transformer-based robotic manipulation methods utilize multi-view spatial representations and language instructions to learn robot motion trajectories by leveraging numer…