๐ŸŽ“How I Study AIHISA
๐Ÿ“–Read
๐Ÿ“„Papers๐Ÿ“ฐBlogs๐ŸŽฌCourses
๐Ÿ’กLearn
๐Ÿ›ค๏ธPaths๐Ÿ“šTopics๐Ÿ’กConcepts๐ŸŽดShorts
๐ŸŽฏPractice
๐Ÿ“Daily Log๐ŸŽฏPrompts๐Ÿง Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Papers4

AllBeginnerIntermediateAdvanced
All SourcesarXiv
#closed-loop control

World Action Models are Zero-shot Policies

Intermediate
Seonghyeon Ye, Yunhao Ge et al.Feb 17arXiv

DreamZero is a robot brain that learns actions by predicting short videos of the future and the matching moves at the same time.

#World Action Models#DreamZero#video diffusion

Causal World Modeling for Robot Control

Intermediate
Lin Li, Qihang Zhang et al.Jan 29arXiv

Robots used to copy actions from videos without truly understanding how the world changes, so they often messed up long, multi-step jobs.

#robot world model#autoregressive diffusion#causal masking

RoboBrain 2.5: Depth in Sight, Time in Mind

Intermediate
Huajie Tan, Enshen Zhou et al.Jan 20arXiv

RoboBrain 2.5 teaches robots to see depth precisely and to keep track of time-aware progress, so plans turn into safe, accurate actions.

#Embodied AI#3D spatial reasoning#metric grounding

Act2Goal: From World Model To General Goal-conditioned Policy

Intermediate
Pengfei Zhou, Liliang Chen et al.Dec 29arXiv

Robots often get confused on long, multi-step tasks when they only see the final goal image and try to guess the next move directly.

#goal-conditioned policy#visual world model#multi-scale temporal hashing