Search papers, labs, and topics across Lattice.
The Temporal Ratio reveals how attention shifts between future and present frames can predict a model's ability to generalize compositional tasks in video-action contexts.
Achieving a staggering 98.75% success rate in dexterous manipulation tasks, LAMP redefines how we approach real-world learning in robotics.
Outperforming previous methods, UniLM-Nav achieves zero-shot last-mile navigation by effectively integrating multimodal reasoning and task context.
Cortex outperforms traditional models by enabling zero-shot execution of complex long-horizon tasks, bridging the gap between high-level planning and low-level execution.
Extracting interaction cues from a frozen video model enables robots to achieve up to 90.6% success in manipulation tasks without costly rollout processes.
SEAM reduces boundary jerk by 28% and transition discontinuity by 27%, all while preserving task success and computational efficiency.
HUGS achieves a remarkable balance between grasp success and diversity, synthesizing 3.2 million grasps that can adaptively handle objects from screws to large boxes.
HALO-WA boosts robotic manipulation success rates from 26.4% to 87.1% by effectively adapting to real-world errors in just over an hour of training.
Many robotic policies that seem successful in manipulation tasks actually compromise safety, with SoftVTBench revealing a stark contrast between goal completion and physical safety metrics.
ACE achieves a remarkable 70% success rate in constraint retrieval tasks without any task-specific retraining, showcasing the power of zero-shot workflow reasoning in robotic manipulation.
Bridge-WA achieves superior task performance by predicting where and how the world will change, enabling robots to focus on relevant scene dynamics rather than irrelevant visual details.
HEFT enables full-size humanoids to perform complex movements with heavy payloads, overcoming significant challenges in motion tracking and stability.
WorldSample achieves a 28% boost in policy success rates while slashing training steps by nearly 60% through innovative real-synthetic data integration.
Attention mechanisms can drastically improve pose sensing accuracy in the face of challenging visual conditions like occlusion and weak textures.
Unconstrained egocentric video generation now achieves unprecedented fidelity and control by disentangling hand and camera motion with a novel 3D-aware representation.
Achieving 100% task success in closed-loop execution, Embodied.cpp revolutionizes how embodied AI models are deployed across diverse hardware platforms.
TTP enables robots to learn dexterous manipulation from human tactile experiences, achieving unprecedented performance in complex tasks.
OVOW achieves unprecedented accuracy and speed in reconstructing 4D scenes from a single video, making it a game-changer for physics simulation in AI.
DynFly achieves a remarkable 4.69 improvement in navigation performance, showcasing how dynamic-aware trajectory generation can transform UAV navigation in complex urban environments.
A single BDDL specification can drastically enhance the efficiency and effectiveness of embodied task planners, achieving a 25.9% performance improvement over existing baselines.
Urban facade reconstruction can achieve superior geometric accuracy by integrating lightweight structural supervision, overcoming common pitfalls of traditional methods.
GeoEdit achieves unprecedented geometric accuracy and identity fidelity in object editing, overcoming the limitations of existing diffusion-based methods.
Fine-grained contact states can be distinguished through the dynamic correlation of tactile motion, transforming how we approach contact-rich manipulation in robotics.
CI-MSE dramatically improves the correlation between offline validation and real-world performance, making it a game-changer for robot policy evaluation.
STEAM redefines how robots learn from mixed-quality data, achieving up to 59% higher success rates in real-world tasks by effectively identifying reliable progress.
OpenSPM achieves 85.6% task success with a control frequency of 1033.3 Hz, revolutionizing high-frequency robotic manipulation with minimal computational demands.
ICMPG achieves a groundbreaking balance between semantic fidelity and physical realism in motion synthesis, outperforming traditional methods in both standard and zero-shot scenarios.
A transparent probe-success rule boosts robot policy selection success rates by over 14 percentage points, revealing the hidden power of pre-deployment evaluations.
Pressure integration in humanoid motion imitation significantly enhances accuracy and stability, revealing the limitations of traditional vision-based methods.
Effective service zone design can outperform battery upgrades in profitability, especially under varying demand conditions.
Internal biological constraints can dramatically reduce errors in hand pose estimation, enabling robust tracking in metric space.
DeformGen transforms the landscape of deformable manipulation by enabling effective policy learning through innovative state augmentation and trajectory adaptation techniques.
Achieving a 14-point boost in grounding accuracy, VistaRef redefines how we approach spatial orientation in AR and human-robot interaction.
Mistakes in human demonstrations can enhance robot learning when properly harnessed, revealing a new dimension of value estimation that traditional methods overlook.
WVM outperforms existing models by accurately assessing task progressions and improving robotic manipulation from both expert and suboptimal data.
High-diversity training improves safety in VLA models, but sub-optimal trajectory synthesis still hinders task success.
TSD reveals that focusing on just 25% of the data can yield superior performance in robotic manipulation tasks, challenging the notion that more data always leads to better outcomes.
Shifting the focus from marginal probabilities to joint trajectory probabilities, dVLA-RL achieves unprecedented success rates in robotic manipulation tasks.
ARP not only aligns visual observations with action representations but also refines execution precision, leading to unprecedented performance in robotic manipulation tasks.
Achieving over 555 FPS in tactile simulations, TaCauchy delivers unprecedented accuracy in mechanical stress computation for robotics applications.
EventVLA's foresight-driven memory mechanism boosts long-horizon task success rates by 40% by dynamically capturing critical visual events before they vanish.
MemoryWAM achieves superior performance in robotic manipulation tasks by efficiently leveraging both short-term and long-term memory without sacrificing computational efficiency.
Humanoid robot data standards could unlock the full potential of physical AI by transforming isolated datasets into a cohesive, reusable resource for robotic learning and interaction.
ImageWAM shows that image editing can outperform video generation in robot action prediction, cutting costs and improving efficiency.
HandTouch outperforms existing tactile encoders, achieving an 85.23% Recall@5 in similarity retrieval—an improvement of over 10%—demonstrating the power of combining egocentric vision with tactile data.
Task knowledge can be efficiently reused across heterogeneous agents, slashing tracking errors by up to 99.79% while using significantly less data.
Jointly optimizing the world model and action model is essential for mastering long-horizon tasks, revealing a critical gap in traditional WA training methods.
Models with similar success rates can exhibit vastly different strengths and weaknesses, revealing the hidden complexities of mobile manipulation capabilities.
PearlVLA achieves state-of-the-art performance in action generation by refining plans in latent space, enabling low-latency execution without sacrificing deliberation quality.
R2RDreamer achieves spatial generalization improvements in manipulation tasks by leveraging 3D-aware data augmentation without the pitfalls of complex scene setups or sim-to-real gaps.
