1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation
Haoyu Zhao, Xingyue Zhao, Hangyu Li +6
Scaling robot learning requires massive, diverse trajectory data, yet collection is currently bottlenecked by physical teleoperation, where every demonstration binds operator time…
RynnWorld-4D: 4D Embodied World Models for Robotic Manipulation
Haoyu Zhao, Xingyue Zhao, Siteng Huang +3
Robotic manipulation in the open world requires not only recognizing what a scene looks like, but also anticipating how its 3D structure moves under interaction. We argue that sync…
Control of Microrobots with Reinforcement Learning under On-Device Compute Constraints
Yichen Liu, Kesava Viswanadha, Zhongyu Li +2
An important function of autonomous microrobots is the ability to perform robust movement over terrain. This paper explores an edge ML approach to microrobot locomotion, allowing f…
GenDexHand: Generative Simulation for Dexterous Hands
Feng Chen, Zhuxiu Xu, Tianzhe Chu +7
Data scarcity remains a fundamental bottleneck for embodied intelligence. Existing approaches use large language models (LLMs) to automate gripper-based simulation generation, but…
High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic Manipulation Learning with Gaussian Splatting
Haoyu Zhao, Cheng Zeng, Linghao Zhuang +11
The scalability of robotic learning is fundamentally bottlenecked by the significant cost and labor of real-world data collection. While simulated data offers a scalable alternativ…