Search papers, labs, and topics across Lattice.
5
0
7
0
A single set of learned manipulation skills transfers to the real world with 78% success, bypassing the need for task-specific fine-tuning or re-training on real data.
Sparsifying communication topologies in LLM multi-agent systems can lead to significant efficiency gains without sacrificing accuracy, challenging conventional design approaches.
Kuafu's ability to synthesize robust policies from language and task semantics achieves over 82% success in complex manipulation tasks, revolutionizing how we approach control-to-learning paradigms.
A-SR achieves nearly a doubling of accuracy in symbolic regression tasks, showcasing a transformative approach to LLM-guided formula discovery.
Finally, a feed-forward method can generate realistic, simulation-ready garment patterns directly from single "in-the-wild" images, bypassing the need for multi-view inputs or expensive optimization.
