Uranus
D-Robotics AI Lab's open robot simulator that autoregressively generates video observations from joint-trajectory input at 24 FPS.
Overview
D-Robotics AI Lab published Uranus (arXiv:2609.24815, v3 dated September 23, 2026), a data-driven robot simulator built around a joint-trajectory-conditioned autoregressive diffusion model. It takes future joint-position trajectories as streaming input and autoregressively generates one latent frame per step (corresponding to four RGB frames) without a fixed horizon, reaching 24 FPS after inference optimization, and provides a unified interface for synchronized multi-view generation across different robot embodiments and camera configurations. The authors released code, model weights, and inference tools openly alongside quantitative and qualitative evaluation on both in-distribution and out-of-distribution data.
This is a sourced directory entry. Features and capabilities are reported in the linked sources; this entry does not document independent Aymorphic testing.
Key features
- Joint-trajectory-conditioned autoregressive diffusion model for video generation
- Streaming rollout: one latent frame (four RGB frames) generated per step, no fixed horizon
- 24 FPS after inference optimization
- Unified interface for synchronized multi-view generation across robot embodiments and camera setups
- Open code, model weights, and inference tools
Use cases
- Generating realistic visual rollouts for robot policy training and evaluation
- Testing robot control policies against out-of-distribution simulated scenarios
- Multi-camera, multi-embodiment simulation without per-setup custom tooling
Pricing
Free and open source; code, weights, and inference tools released publicly.
Pricing reflects the source record and may change. Confirm current terms with the provider.
Dates and sources
- Released
- 23 Sept 2026
- Record updated
- 25 Sept 2026
- Record last checked
- 25 Sept 2026