Papers29

#vision-language model

PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence

Xiaopeng Lin, Shijie Lian et al.Dec 18arXiv

Robots learn best from what they would actually see, which is a first-person (egocentric) view, but most AI models are trained on third-person videos and get confused.

#egocentric vision#first-person video#vision-language model

RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics

Intermediate

Enshen Zhou, Cheng Chi et al.Dec 15arXiv

RoboTracer is a vision-language model that turns tricky, word-only instructions into safe, step-by-step 3D paths (spatial traces) robots can follow.

#RoboTracer#spatial trace#3D spatial referring

InfiniteVL: Synergizing Linear and Sparse Attention for Highly-Efficient, Unlimited-Input Vision-Language Models

Intermediate

Hongyuan Tao, Bencheng Liao et al.Dec 9arXiv

InfiniteVL is a vision-language model that mixes two ideas: local focus with Sliding Window Attention and long-term memory with a linear module called Gated DeltaNet.

#InfiniteVL#linear attention#Gated DeltaNet

MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment

Intermediate

Ruicheng Zhang, Mingyang Zhang et al.Dec 7arXiv

Robots need lots of realistic, long videos to learn, but collecting them is slow and expensive.

#hierarchical video generation#robotic manipulation#long-horizon planning

Self-Improving VLM Judges Without Human Annotations

Intermediate

Inna Wanyin Lin, Yushi Hu et al.Dec 2arXiv

The paper shows how a vision-language model (VLM) can train itself to be a fair judge of answers about images without using any human preference labels.

#vision-language model#VLM judge#reward model

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