Search papers, labs, and topics across Lattice.
Track unseen objects through total occlusion without CAD models, using just a handful of 2D points.
Reachability maps don't have to trade off precision, speed, and flexibility: RichMap achieves all three.
Scaling robot learning with human data isn't a simple "more is better" equation; alignment with robot learning objectives is key.
Forget complex, bespoke mechanisms: ZipFold offers a simple, scalable path to adaptive robots that morph their shape and stiffness on demand.
Robots can now "see" hidden objects and understand articulation by learning from human egocentric video, even if they can't physically explore those areas themselves.
Freeing robots from pre-assigned tasks slashes completion times in multi-agent settings, with a new algorithm improving performance on almost 90% of tested scenarios.
Heuristic maritime routes lead to extreme fuel waste in nearly 5% of voyages, but this RL approach cuts that risk by almost 10x.
Forget hand-engineered features: this approach learns symbolic representations for robotic planning directly from pixels using VLMs, enabling impressive zero-shot generalization to new environments and goals.
Forget simulated manipulation鈥擬anipulationNet offers a global infrastructure for benchmarking robots in the real world, complete with standardized hardware and software, to finally measure progress toward general manipulation.
NeuroSkill(tm) offers real-time, edge-based human-AI interaction by directly modeling human state of mind from BCI data, enabling more nuanced and empathetic agentic responses.
Decomposing Bellman values into a graph of simpler objectives lets agents master complex, high-dimensional tasks with less tuning and better safety.
Stop repeating avoidable mistakes in public robot deployments: here's a community-vetted checklist to guide your next study.
Forget hand-engineering initial conditions for robust RL: this method *learns* which conditions are feasible while simultaneously training a safe policy.
