8 papers
FocusOPD: Structure-Guided On-Policy Distillation for Diffusion Models
Xinrui Lin, Shumin Wang, Suqi Song +2
Multi-teacher on-policy distillation enables rectified-flow models with diffusion-transformer architectures to combine task-specific capabilities within a single student. However,…
When Pairwise Preferences Cycle: A Reliability-Anchored Preference Walk for Video GRPO
Xinrui Lin, Fan Li, Shumin Wang +2
Pairwise preference feedback has become a widely adopted supervision signal for reinforcement learning in video generation models, as pairwise comparisons provide a more reliable a…
ThinkAfford: Affordance-Centric Reasoning for Fine-Grained 3D Grounding in Cluttered Scenes
Xinrui Lin, Sha Zhang, Shumin Wang +3
Task-driven 3D affordance grounding aims to localize the functional region in a cluttered 3D scene that enables an action specified by a natural-language instruction. Existing meth…
Environment-Aware Adaptive Pruning with Interleaved Inference Orchestration for Vision-Language-Action Models
Yuting Huang, Leilei Ding, Zhipeng Tang +7
While Vision-Language-Action (VLA) models hold promise in embodied intelligence, their large parameter counts lead to substantial inference latency that hinders real-time manipulat…
UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous Driving
Hanqi Zhu, Wuyang Zhang, Xinran Zhang +5
The rapid advancements in autonomous driving have introduced increasingly complex, real-time GPU-bound tasks critical for reliable vehicle operation. However, the proprietary natur…
VLMPlanner: Integrating Visual Language Models with Motion Planning
Zhipeng Tang, Sha Zhang, Jiajun Deng +5
Integrating large language models (LLMs) into autonomous driving motion planning has recently emerged as a promising direction, offering enhanced interpretability, better controlla…
A Framework for Benchmarking and Aligning Task-Planning Safety in LLM-Based Embodied Agents
Yuting Huang, Leilei Ding, Zhipeng Tang +5
Large Language Models (LLMs) exhibit substantial promise in enhancing task-planning capabilities within embodied agents due to their advanced reasoning and comprehension. However,…
CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning
Xinrui Lin, Yangfan Wu, Huanyu Yang +3
Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios. Howe…