Search papers, labs, and topics across Lattice.
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Instruction-following in robot manipulation can be rigorously tested with InstructMove, revealing the true capabilities of VLA models beyond visual cues.
Achieving a 49% increase in success rate on out-of-distribution tasks, Faster-WAM redefines efficiency in future-aware robot manipulation models.
Achieving a staggering 96.5% human acceptance rate, EmbodiedGen V2 transforms how we create and utilize 3D environments for embodied AI training.
Explicitly coupling geometry and appearance can dramatically enhance the robustness of 3D reconstruction against pose drift in long sequences.
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.
