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Papers
Paper2026-10-07 · arXiv (Cornell University)
Cross-Embodiment Robot Foundation World Models with Latent Actions
- 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)
RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer
- 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)
OpenWAM: An Open Framework for Composable World-Action Models
- 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
Paper2026-09-29 · arXiv (Cornell University)
T$^2$Mem: Learning Test-Time Memory for Robotics
- 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 · arXiv (Cornell University)
DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
- 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
