Search papers, labs, and topics across Lattice.
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Explicitly coupling geometry and appearance can dramatically enhance the robustness of 3D reconstruction against pose drift in long sequences.
Achieving a staggering 96.5% human acceptance rate, EmbodiedGen V2 transforms how we create and utilize 3D environments for embodied AI training.
NativeMEM achieves a staggering success rate of 98.7% on real robots by compressing visual histories into single tokens, revolutionizing long-horizon robotic manipulation.
HoloAgent-0 transforms how robots interpret and act on language instructions, enabling seamless execution of complex tasks in real-world settings.
Achieving high-fidelity 3D scene reconstruction from monocular video, ManiSplat enables robots to interact with their environments in a more controllable and realistic manner.
Forget painstakingly aligning objects in 3D scene generation; 3D-Fixer uses fragmented geometry as a spatial anchor, boosting accuracy while keeping things efficient.
Robots can now learn complex manipulation skills entirely in simulation, thanks to a compositional world model that accurately predicts future states and evaluates progress, leading to a 35-45% performance boost in real-world tasks.
