Necessity of Broad and Rich Simulated Environments for Scaling Embodied Agents

Determine whether obtaining sufficient breadth and richness across many simulated environments and commercial video games is necessary to effectively scale embodied agents that connect language, perception, and action. This investigation should assess the necessity claim by evaluating agents trained on narrow versus broad and rich distributions of environments and tasks, and quantify impacts on generalization, robustness, and capability scaling.

Background

The SIMA project aims to train instructable agents that map natural language instructions and visual observations to keyboard-and-mouse actions across diverse 3D environments, including commercial video games and research simulators. A central hypothesis motivating this design is that broad, rich environmental diversity may be critical for developing general embodied AI capabilities.

In the context of robotics and embodied AI, the authors explicitly conjecture that leveraging many simulated environments and video games provides the breadth and richness necessary for effective scaling. This claim frames an open question about whether such diversity is a necessary condition for scaling embodied agents, as opposed to being merely beneficial or sufficient. Validating this conjecture would inform the design of training regimes and datasets for generalist embodied agents.

References

Instead, SIMA makes progress towards embodied AI by leveraging many simulated environments and commercial video games to obtain the sufficient breadth and richness that we conjecture to be necessary for effectively scaling embodied agents---with the hope that lessons learned (and possibly even the agents themselves) will be applicable to robotic embodiments in the future.

— Scaling Instructable Agents Across Many Simulated Worlds  (2404.10179 - Team et al., 2024) in Related work, Robotics paragraph (Section 2.5)

External restrictions interrupted the project before held-out evaluation could be conducted. We therefore leave generalization of the post-trained agent to unseen tasks for future work.

— EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics  (2609.27308 - You et al., 23 Sep 2026) in Appendix, Section "Synthetic Robotics Tasks Construction and Coding-Agent Training," subsection "Training Setup and Results"

Despite these advancements, several fundamental challenges are still unresolved. While VLA models, with the help of language grounding, enable semantic generalization across tasks, it is unclear whether this paradigm can truly scale to the diversity of the real world.

— Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models  (2609.03927 - Mehta et al., 3 Sep 2026) in Discussion and Challenges following Section 5.3, Vision-Language-Action Models

These results leave instruction following unresolved in the tested configurations, without ruling out improvements from broader data distributions or alternative training procedures.

— RoboFollow: Unveiling the Instruction Following Mirage in Embodied Agents  (2609.25636 - Guo et al., 22 Sep 2026) in Section 5.2, “Can Stronger VLMs and Existing Optimizations Help?”