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S&P 500
7,569
NDX 100
28,180
VIX
20.83
US 10Y
4.95%
DXY
101.87
WTI
$96.76
Gold
$3,940
USD/KRW
1,344
SOX
10,600
Trump Approve
35.4%
AI LEADERS · MOVES & PAPERS

AI Leaders: Moves & Papers — latest job changes and research

LAST UPDATE 2026-11-04 08:05 KST PEOPLE 77 · PAPERS 76 · MOVES 0
Where the key people at AI labs, big tech and universities move, and what they publish, are early signals of where technology and competitive advantage are heading. Career moves and papers are collected automatically every day, checked by the editor and summarized in plain language with links to the originals.

Highlights

Career moves · Academia · Robotics & physical AI

No career moves match these filters yet.

Papers · Academia · Robotics & physical AI

Paper2026-10-06 · arXiv (Cornell University)

Development of an algorithm that accelerates AI robot behavior learning

QF3: Fast Flow RL with Filtered Q-Gradients
What
Researchers have developed QF3, a new reinforcement learning algorithm that efficiently learns robot behavior patterns.
Key result
It has been demonstrated that it can learn humanoid robot locomotion and motion tracking policies about 10 times faster than existing methods.
Why it matters
It shows the potential to learn and improve robot behavior control technology faster and more efficiently.AI summary
Paper2026-10-06 · arXiv (Cornell University)

Whole-Body Humanoid Loco-Manipulation Technology Utilizing Human Demonstrations

Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data
What
Developed a technology that enables humanoid robots to manipulate objects and move by utilizing human demonstration videos through a visual planner and a reinforcement learning tracker.
Key result
Successfully performed complex whole-body manipulation tasks such as box carrying, box grasping, and stepping over a suitcase on a real Unitree G1 robot.
Why it matters
Demonstrates that robots can improve their ability to handle objects while maintaining balance in complex environments based on human behavior data.AI summary
Paper2026-10-02 · arXiv (Cornell University)

Proposing GOTT, a reusable object grasping technology for multiple robot hands

GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive
What
To enable multi-fingered robot hands, similar to human hands, to stably grasp and move objects in desired directions, the authors proposed GOTT, a contact technology that can be commonly used across different robots and hand shapes.
Key result
In simulations and real-world robot experiments, GOTT established stable contacts across various objects, arm-hand platforms, and unseen hand shapes, and consistently improved overall task success rates compared to pre-planned grasping methods.
Why it matters
This study demonstrates the potential to reuse the same technology even when robot hand types change by using a common motion component not dependent on a specific robot. However, it is difficult to judge the commercialization level or real industrial impact from the abstract alone.AI summary
Paper2026-10-01 · arXiv (Cornell University)

An RPG framework where robots improve their own skills through simulation practice

Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
What
Proposed an RPG framework that autonomously improves robotic task systems without additional human intervention.
Key result
In new initial condition tests across 22 manipulation tasks, the success rate increased from 28.6% after the first practice to 95.0% after 15 rounds, and all 30 real-world robot trials succeeded.
Why it matters
This study demonstrates the potential to improve robot control software with less human effort, although actual industrial application or commercialization levels are difficult to judge from the abstract alone.AI summary
Paper2026-09-30 · arXiv (Cornell University)

Researchers Propose TacEx to Enhance Robot Learning Efficiency Through Tactile Curiosity

Tactile Curiosity Drives Robot Interaction
What
The researchers proposed TacEx, an exploration framework that allows robots to develop curiosity about tactile sensations (the sense of touching objects) and learn how to touch and pick up objects on their own.
Key result
According to the abstract, TacEx collects interaction data rich in contact without task rewards or expert demonstrations, enables learning of pick-and-place policies using this data without further environment interactions, and improves the post-training performance of vision-language-action models.
Why it matters
Traditional robot learning spends a lot of time moving randomly in the air, demonstrating that exploring with a focus on contact can improve learning efficiency. However, it is difficult to judge the level of practical application or commercialization potential based on the abstract alone.AI summary
Paper2026-09-29 · arXiv (Cornell University)

Scaling humanoid object carrying learning by expanding a few real videos with AI

Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
What
Proposed a method (PRISM) where AI generates various virtual videos based on a few real videos of a person carrying objects, and uses them for robot learning.
Key result
Reports that a single control policy trained this way successfully enabled a real humanoid robot to pick up, transport, and place boxes, drums, kegs, and balls without additional real-world learning.
Why it matters
Significant in that it demonstrates the potential to increase training data using a few real videos in situations where collecting large amounts of real robot demonstration data is difficult.AI summary
Paper2026-09-28 · The International Journal of Robotics Research

A comprehensive review summarizing 'world model' research for robot learning

World model for robot learning: A comprehensive survey
Pieter Abbeel +17 more
What
This is a survey paper that systematically organizes 'world models'—models that predict how the environment will change in response to actions—from the perspective of robot learning.
Key result
According to the abstract, it outlines how world models are used in robot policies, simulators, evaluation, and data generation, summarizes the evolutionary trajectory of robot video-based world models, and highlights representative datasets, benchmarks, and evaluation methods.
Why it matters
This serves as a resource that unifies scattered robot learning research approaches into a single framework, helping to grasp the research trends. However, the abstract does not prove the performance or commercial achievements of individual technologies.AI summary

People

Why follow AI leaders' moves and papers?

In AI, people are the technology. Which company a key researcher joins, or which lab they found, often turns into products, funding rounds and acquisitions months later. This page tracks job changes across frontier labs, new labs, academia, AI chips, and people in Korea and China.

Papers show what a person is focused on right now. We sort papers into 10 investable fields such as HBM & memory, GPUs & accelerators, inference efficiency, agents and robotics, and give a three-line summary — what, key result, why it matters — based on the abstract, with links to the original and PDF. This is information for finding ideas, not a recommendation to buy or sell any security.

Frequently Asked Questions

Q. Who do you track?

Key researchers and executives at frontier labs (OpenAI, Google DeepMind, Anthropic, Meta and others), new labs, academia, AI chip companies, and in Korea and China. Executives who rarely publish are tracked for career moves only.

Q. Where does the data come from, and how often is it updated?

Papers come from Semantic Scholar and arXiv, and careers from Wikidata (CC0) plus official announcements and press reports, collected automatically every day. Only items checked and approved by the editor are published.

Q. Who writes the paper summaries?

An AI (Google Gemini) drafts each summary from the paper's abstract, and the editor checks it before publishing. Other languages are AI translations of the checked Korean summary. Always confirm exact figures and context in the original paper.

This page compiles published papers, official announcements and press reports for information only. Paper summaries are written by AI from the abstract and reviewed by the editor; they may differ from the original, so check the paper for anything important. Nothing here is a recommendation to buy or sell any security or investment advice. Career moves cite official announcements or major press reports; some career records come from Wikidata (CC0). Summaries on this page were translated by AI from the editor-reviewed Korean version.

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