Spatial intelligence and robotic world models
Fei-Fei Li
Coauthor of Dream What I Can and T2-z, studying visual planning and robotic world models.
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- Past year
- 6
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Dream What I Can: Sparse Visual Planning for Long-Horizon Robot Manipulation
DreamCan is a hierarchical policy for long-horizon robot manipulation that plans with sparse visual subgoals grounded by reachability and task utility. From action-free robot video datasets, it learns dataset-supported reachability and task…
T2-z: Test-Time Optimization of Environment Latents for Robotic World Models
T2-z adds an environment latent z_e to an action-conditioned robotic world model and trains it with alternating optimization: learn general action dynamics with z_e fixed, then optimize z_e with the world model frozen. At test time, the wor…
Cross-Embodiment Robot Foundation World Models with Latent Actions
The authors introduce LAC-WM, a robot world model that uses a learned unified latent action space shared across embodiments. Compared to EAC-WM, which conditions on explicit motion labels via embodiment-specific action encoders, LAC-WM impr…
RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer
RoboRender converts simulation trajectories into photorealistic, robot-oriented multi-view RGB videos for zero-shot visual sim-to-real policy transfer. The model is conditioned on simulated depth, language instructions, and robot RGB masks,…
OpenWAM: An Open Framework for Composable World-Action Models
OpenWAM is an open world-action modeling framework that couples a common causal robot-video backbone (from Wan2.2-5B) with configurable video–action interaction programs (joint, VTA, ATV, decoupled) using a shared Mixture-of-Transformers ar…
T$^2$Mem: Learning Test-Time Memory for Robotics
T²Mem is a test-time training framework that turns a memory-free pretrained vision-language-action policy into a robotics controller with fast-weight test-time memory. It uses an observation-grounded interface to encode vision-language hist…