Embodied LingBot-World: Bridging Simulation and Real-World Robotics through Egocentric World Models

TL;DR: What if you trained a robot in LingBot-World and it could walk into your living room and still know what to do? Adapt LingBot-World for egocentric, robot-centric simulation using video and control inputs from real robots (inspired by Humanoid World Models and Ctrl-World). First step: Sim-to-real transfer experiments in manipulation or navigation.

Research Question: Can LingBot-World be extended to serve as an embodied, egocentric world model for training and evaluating physical robots, improving the fidelity and transferability of simulated policies to the real world?

Hypothesis: Using egocentric video simulation and robot control signal conditioning, LingBot-World can bridge the sim-to-real gap, resulting in better policy transfer than traditional third-person or environment-centric simulations.

Experiment Plan: - Collect egocentric robot video and control data (e.g., from real or simulated humanoids).

• Fine-tune LingBot-World for egocentric prediction and action-conditioned video generation.
• Train manipulation or navigation policies in simulation, transfer to real hardware, and compare performance against conventional simulators.
• Use metrics like policy success rate, transfer efficiency, and qualitative assessments of realism.

References:

• Ali, M. Q., Sridhar, A., Matiana, S., Wong, A., Al-Sharman, M. (2025). Humanoid World Models: Open World Foundation Models for Humanoid Robotics. arXiv.org.
• Guo, Y., Shi, L., Chen, J., Finn, C. (2025). Ctrl-World: A Controllable Generative World Model for Robot Manipulation. arXiv.org.
• Robbyant Team Zelin Gao, Q. Wang, Y. Zeng, et al. (2026). Advancing Open-source World Models.