Search papers, labs, and topics across Lattice.
ARDY achieves real-time, controllable 3D human motion generation that adapts seamlessly to dynamic text prompts and complex kinematic constraints.
GaP outperforms traditional methods in variational automation tasks by leveraging directed computation graphs for real-time adaptability and improved success rates.
Achieving a 2x reduction in cycle times for cable routing tasks, SILO marks a significant leap in sim-to-real transfer for complex linear-deformable manipulation.
ROSA revolutionizes robot factory operations by boosting productivity up to 12.06x through innovative shared GPU-pool serving and factory-focused scheduling.
ASPIRE achieves a staggering 31% success rate on unseen long-horizon tasks, compared to just 4% for prior methods, highlighting its superior adaptability and efficiency.
Policies trained in SimFoundry's automated environments achieve up to 40% higher success rates in real-world tasks by leveraging affordance-preserving scene variations.
Physically aligned video models can boost robotic manipulation success rates by over 50% compared to traditional methods.
FAR-LIO cuts odometry latency by 38.4% while improving accuracy, setting a new standard for real-time performance in autonomous racing.
GRAFT enables robots to manipulate unseen objects with just one demonstration by leveraging geometric similarities, outperforming traditional semantic retrieval methods.
Coding agents can now autonomously refine robotic manipulation policies to achieve a staggering 99% success rate on complex tasks, revolutionizing real-world robotics.
Post-training on synthesized safety-critical scenarios can dramatically enhance the reliability of autonomous driving systems, reducing failures in rare but critical events.
A single generalist model outperforms specialized systems, achieving over 35% improvement in real-world robotic task success.
Combining learning and geometric optimization, this framework achieves a 60.9% grasp success rate, outperforming traditional methods by a significant margin.
Extracting action signals from 32,041 hours of human video enables CAIP to outperform leading vision encoders in robotic manipulation tasks by over 30%.
Tactile-reactive policies can boost robotic manipulation success rates by over 30% through innovative data collection and a new Mixture-of-Transformers architecture.
SPARC reduces noisy labels by leveraging task structure, enabling robots to learn from more reliable demonstrations and outperforming traditional methods in real-world applications.
Decoupling modality processing in VLA models leads to a staggering 95.2% success rate in complex manipulation tasks, far surpassing traditional synchronous approaches.
DEHP dramatically boosts the success rates of high-precision robotic tasks by dynamically adjusting execution horizons based on task complexity.
GRAIL achieves an impressive 84% success rate in real-world object pick-up tasks using only synthetic data, revolutionizing humanoid robot training.
Cosmos 3 sets a new benchmark for omnimodal models, outperforming existing state-of-the-art in both Text-to-Image and Image-to-Video tasks.
Achieving zero-shot generalization in robotic grasping across diverse gripper designs could revolutionize how robots interact with their environments.
Steering imaginations in video world models can reveal critical failure points in robotic actions that traditional methods might overlook.
Achieving a 40x speedup in training for deformable simulations could revolutionize real-time applications in robotics and animation.
VLN agents can navigate more accurately in zero-shot settings by "looking forward, now, and backward," mimicking human navigational strategies.
Existing robotic methods falter in tackling fundamental physical reasoning challenges, as evidenced by KinDER's rigorous benchmark evaluation.
Forget clunky animation pipelines – MotionBricks lets you assemble real-time, high-quality character motions like LEGOs, even controlling robots.
Open-vocabulary 3D instance segmentation just got 100x faster, thanks to a new transformer architecture that ditches region proposals and fragmented masks.
Fusing MPC with RL yields safer and more efficient autonomous driving at intersections, outperforming both standalone MPC and end-to-end RL, and surprisingly generalizing better to new scenarios.
RoboLab exposes critical performance gaps in leading robotic models, revealing that high-fidelity simulations can better assess generalization than traditional benchmarks.
Training autonomous vehicles can be dramatically sped up: MOSAIC achieves state-of-the-art driving performance with 80% less data by intelligently selecting training examples based on scaling laws.
Finally, a method disentangles dynamic egocentric scenes into background, hand, and object components, enabling fine-grained understanding and editing.
Swap out slow, one-token-at-a-time generation in VLMs for a 6x speed boost, without sacrificing quality, using a surprisingly simple direct conversion to block-diffusion decoding.
Finally, a video generation model lets you puppeteer objects and their reactions independently, all while freely moving the camera.
Achieve 49% and 19% better Chamfer distance than state-of-the-art dynamic surface reconstruction methods on Hi4D and CMU Panoptic datasets, respectively, by enforcing temporal consistency in Gaussian Splatting.
A hybrid cuVSLAM-based visual SLAM system achieves superior mapping accuracy in real-world logistics environments, outperforming other VO/VSLAM approaches.
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.
Training generalist robots just got a whole lot easier: RoboCasa365 offers a massive, diverse, and reproducible benchmark for household mobile manipulation.
Forget simulated manipulation—ManipulationNet offers a global infrastructure for benchmarking robots in the real world, complete with standardized hardware and software, to finally measure progress toward general manipulation.
Learning robotic reward functions from a million trajectories reveals that comparing entire trajectories, not just individual frames, unlocks better generalization and learning from suboptimal data.
Forget tedious manual segmentation: ArtisanGS lets you lasso objects in 3D Gaussian Splats with AI-powered 2D selections that propagate into 3D, giving you unprecedented control over editing.
Forget synthetic data that looks like it came from a PS2 game: NVIDIA's new Cosmos-Predict2.5 generates high-fidelity videos for training embodied AI, opening the door to more realistic and reliable simulations.
