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S&P 500
7,569
NDX 100
28,180
VIX
20.82
US 10Y
4.95%
DXY
101.86
WTI
$96.60
Gold
$3,932
USD/KRW
1,344
SOX
10,599
Trump Approve
35.4%
AI LEADERS · MOVES & PAPERS

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

LAST UPDATE 2026-11-03 08:00 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 · Robotics & physical AI

No career moves match these filters yet.

Papers · Robotics & physical AI

Paper2026-10-29 · arXiv (Cornell University)

A technology for jointly training robot data using diverse controllers

Cross-Controller Transfer via In-Context Behavior Adaptation
Chelsea Finn +2 more
What
Developed a technology to utilize data from robots using different control methods for learning without conflicts.
Key result
The average success rate improved by 33 percentage points compared to conventional methods.
Why it matters
Demonstrates the potential to more effectively utilize robot data collected from diverse environments.AI summary
Paper2026-10-12 · Micromachines

Analysis of Construction and Medical Application Technologies for Bio-Integrated Micro-Robots

Construction and Functional Engineering of Bio-Integrated Micro/Nanorobots for Theranostic Applications
What
Reviewed the fabrication and theranostic applications of bio-integrated micro- and nanorobots that combine biological characteristics with technology.
Key result
Presented engineering strategies and manufacturing guidelines that implement mobility, in vivo imaging, and therapeutic functions using cell-based components.
Why it matters
Demonstrates the potential direction for utilizing bio-integrated micro-robots in the fields of precision medical intervention and personalized medicine.AI summary
Paper2026-10-07 · arXiv (Cornell University)

Development of Long Visual Memory Processing Technology for Real-Time Robot Control

Long-WAM: Scaling the Context of World-Action Models
Song Han +15 more
What
Researchers have developed Long-WAM, a model that efficiently utilizes past visual information of robots under real-time constraints.
Key result
When pre-training the foundational model that processes visual information using an autoregressive (AR) method, the utilization of past memory is maximized, and robot task success rates are improved.
Why it matters
It demonstrated that real-time operation is possible in various hardware environments, which can contribute to future complex long-horizon robot control tasks.AI summary
Paper2026-10-07 · arXiv (Cornell University)

Development of a World Model Integrating Various Robot Behaviors into One

Cross-Embodiment Robot Foundation World Models with Latent Actions
Fei-Fei Li +11 more
What
Developed a robot world model utilizing an integrated latent action space that can be shared by various types of robots.
Key result
When applied to new robots, it showed up to a 46.7% improvement in precise manipulation tasks and an 11.7% improvement in LIBERO benchmark performance compared to conventional methods.
Why it matters
Enables efficient learning across diverse robots, contributing to solving major challenges in robotics.AI summary
Paper2026-10-07 · arXiv (Cornell University)

AI technology that transforms virtual robot videos into realistic ones

RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer
Fei-Fei Li +8 more
What
Developed a technology that enhances robot control capabilities by converting robot data from virtual environments into videos that look like the real world.
Key result
Robots trained with this technology achieved an average success rate of 71 percent in real-world environments, showing superior performance compared to existing methods.
Why it matters
This can help alleviate the visual problem where data created in virtual environments was difficult to directly apply to real robots.AI summary
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
Paper2026-10-06 · arXiv (Cornell University)

Open Framework Combining Robot Control and Future Prediction Released

OpenWAM: An Open Framework for Composable World-Action Models
Fei-Fei Li +9 more
What
Developed OpenWAM, an open-source framework for comparing and researching world-action models that handle robot control and future video prediction together.
Key result
The proposed method significantly increased the success rate of robotic manipulation tasks and improved the performance of dynamics models through counterfactual data training.
Why it matters
It provides a common foundation for comparing various robot control and video prediction methods, thereby increasing the efficiency of related technology research.AI summary
Paper2026-10-05 · arXiv (Cornell University)

Development of a new benchmark to study the phenomenon of AI behavioral cloning technology

Behavioral Cloning Mystery
What
Proposed OCBench, a robot manipulation evaluation tool with characteristics similar to human demonstrations.
Key result
Showed that this tool enables scientific research on various phenomena related to behavioral cloning and hypothesis testing in graphics processing unit (GPU) based environments.
Why it matters
Provides a foundation for analyzing previously unclear phenomena in robot learning technology within a controlled environment.AI summary
Paper2026-10-05 · arXiv (Cornell University)

A Performance-Guided Data Collection Method to Enhance Robot Learning Efficiency

Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Chelsea Finn +6 more
What
Researchers proposed Mulligan, a technique that improves efficiency in robot manipulation learning by collecting data centered around failed and untried states.
Key result
The Mulligan technique improved the robot's final task success rate from 10 percent to 34 percent compared to conventional methods.
Why it matters
It demonstrates a technological advancement that helps human trainers use time and effort more efficiently when teaching robots.AI summary
Paper2026-10-05 · arXiv (Cornell University)

Development of H-JEPA, an AI model that learns hierarchical structures for complex visual planning

H-JEPA: End-to-End Learning of Hierarchical World Models for Visual Planning
Yann LeCun +4 more
What
Developed H-JEPA, a hierarchical world model that learns by dividing abstraction levels according to time horizons.
Key result
In simulation environments and real robot video experiments, planning success rates and computational efficiency improved compared to existing single-level models.
Why it matters
It can contribute to increasing efficiency in robot control and visual tasks that require complex long-term planning.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
Paper2026-09-29 · arXiv (Cornell University)

T2Mem: A Technique for Training Robots to Remember Past Scenes and Perform Tasks

T$^2$Mem: Learning Test-Time Memory for Robotics
Fei-Fei Li +6 more
What
For robot manipulation tasks that require remembering past observation information, we propose a framework (T2Mem) that adds memory capabilities through test-time training to pretrained robot policies that previously lacked memory functions.
Key result
The average success rate across 16 RoboMME tasks increased from 17.93% for the baseline policy without memory to 56.83%, outperforming recurrent memory methods reported in the benchmark.
Why it matters
This is significant in showing that robots' ability to utilize past information not currently visible on screen can be fostered using only demonstration data without external memory devices, though the abstract only covers research-stage results, so commercialization or market effects cannot be confirmed.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
Paper2026-09-28 · arXiv (Cornell University)

DexAgent automatically generates robot hand motion training data from human demonstration videos

DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
Fei-Fei Li +4 more
What
We propose an agentic framework that takes a single first-person human video and a task description to generate physically feasible robot motion trajectories for robot learning data.
Key result
Policies trained on data generated by DexAgent across 11 real-world tasks showed a 3.5 times higher success rate compared to baseline methods.
Why it matters
It demonstrates the potential to automate the process of converting human demonstration videos into robot learning data, though it is difficult to judge the industrial application level or commercialization timeline from the abstract alone.AI summary
Paper2026-09-28 · arXiv (Cornell University)

JEPA research makes autonomous driving world models 100 times faster

AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving
Yann LeCun +3 more
What
Evaluated predictive world models (JEPA family) used in autonomous driving and proposed an improved architecture, AD-E2E-JEPA, that significantly reduces computational load.
Key result
The proposed architecture reduced the number of patches used for planning by 1/16 and the embedding dimension by 1/4, increasing inference speed by about 100 times while maintaining planning performance.
Why it matters
This research shows a direction for reducing the computational cost of autonomous driving AI, though the actual in-vehicle application effect and commercialization potential are difficult to judge from the abstract alone.AI summary
Paper2026-08-24

Learning to Act While the Robot Thinks

Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
Sergey Levine +19 more
What
Researchers have developed ARLI, a latency-aware reinforcement learning framework that solves problems caused by the thinking time (inference latency) of robot models.
Key result
This new method enables robots to successfully continue learning even in environments with latency, showing performance equal to or better than an ideal latency-free environment.
Why it matters
It opens up the possibility for complex and massive robot AI to autonomously improve performance in real-world fields without freezing or stuttering.AI summary
Paper2026-08-03

Uncovering Why Action Chunking Improves Performance in Robotic Control

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
What
Through experiments, the study analyzed the causes of performance improvement in 'action chunking', a technique where robotic control predicts and executes multiple actions at once.
Key result
It showed that existing hypotheses are incorrect, revealed that the core success factor of action chunking is the 'implicit ensembling' effect which learns various temporal relationships, and proposed a new policy utilizing this.
Why it matters
By uncovering the key factors that enhance the performance of robotic control technology, it provides a theoretical foundation for developing efficient robot learning models in the future.AI summary
Paper2026-06-11 · arXiv.org

Development of a new technology to more accurately guide robot behavior

Improving Robotic Generalist Policies via Flow Reversal Steering
What
To improve the performance of robotic generalist policies trained on diverse robot datasets, a method called Flow Reversal Steering (FRS), which transforms suboptimal actions into better ones, was evaluated in control settings.
Key result
Using FRS can translate rough instructions from humans or vision-language models into good robot behavior, showing an absolute task success rate improvement of up to 95% with less than 1 minute of training.
Why it matters
It demonstrates the potential to help robots perform better in new tasks by utilizing human instructions or existing techniques.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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