Search papers, labs, and topics across Lattice.
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HumanScore reveals that traditional kinematic metrics overlook critical failures in humanoid motion tracking, such as unstable support and incorrect contacts.
GeniWorld achieves robust zero-shot generalization in robotic manipulation, outperforming traditional models even with minimal training data.
DeformGen transforms the landscape of deformable manipulation by enabling effective policy learning through innovative state augmentation and trajectory adaptation techniques.
ImageWAM shows that image editing can outperform video generation in robot action prediction, cutting costs and improving efficiency.
Object-centric mask conditioning in MaskWAM dramatically improves policy performance, outperforming traditional WAMs by effectively reducing language ambiguity in complex environments.
Training with just 3% of high-quality motion data can yield superior tracking performance compared to using the entire dataset.
Humanoid-GPT achieves unprecedented zero-shot generalization in motion tracking, outperforming traditional models by leveraging a billion-scale motion dataset.
By explicitly modeling spatial interaction and manipulation intent, AIM achieves a 94% success rate on RoboTwin 2.0, suggesting that robot control can be drastically improved by explicitly reasoning about *where* to interact, not just *how* scenes evolve.
Robots learn better when they first imagine the future and then figure out how to act, unlocking SOTA performance by disentangling forward and inverse dynamics pretraining.
