Search papers, labs, and topics across Lattice.
Get the performance boost of expensive sampling-based RL policies for a fraction of the compute by learning to prune action candidates early in the diffusion denoising process.
Generate navigable, 3D-consistent simulations of real-world locations with arbitrary weather and dynamic object configurations using only geo-registered video data.
Runners stick to their pace 60% better and enjoy the workout more when coached by a robot dog than when using an Apple Watch.
Scaling robot learning with human data isn't a simple "more is better" equation; alignment with robot learning objectives is key.
Unlock interactive digital twins from messy, real-world videos: FunRec automatically turns egocentric RGB-D recordings into simulation-ready 3D scenes.
Stochastic resetting鈥攔andomly teleporting RL agents back to the start鈥攕urprisingly speeds up learning, even when it wouldn't help a non-learning agent.
Forget slow, model-dependent curation: FAKTUAL offers a fast, model-free way to boost robot imitation learning by directly maximizing the entropy of demonstration datasets.
Ditch the anchors and NMS: AutoReg3D reimagines 3D object detection as a sequence generation problem, opening the door for language-model techniques in 3D perception.
Turns out, the best memory design for robotic manipulation depends heavily on the task, with no single architecture dominating across the board.
Unlock compliant robot control without force sensors or complex learning, using only motor signals already available in most modern robots.
By unifying hand motion estimation and generation into a single diffusion framework, UniHand handles heterogeneous inputs and challenging conditions like occlusions better than task-specific models.
Robots can now navigate complex outdoor environments and find objects using natural language queries, even without prior maps or precise depth sensing.
A single RL policy trained on procedurally generated tools in simulation can achieve zero-shot dexterous manipulation of diverse real-world tools, rivaling task-specific policies.
Verification at test time can be a surprisingly effective alternative to scaling policy learning for vision-language-action alignment, yielding substantial gains in both simulated and real-world robotic tasks.
A deployable robotic arm achieves sub-15mm accuracy at 1.8m reach, opening the door to autonomous lunar construction.
Q-functions and implicit policy extraction are game-changers for batch online RL in robotics, unlocking significant performance gains over imitation-based approaches.
