Search papers, labs, and topics across Lattice.
70 papers published across 8 labs.
Learned world models can achieve real-time control in live games, outperforming behavioral cloning and maintaining high-frequency action loops.
Real-world robot policies can be steered to novel, out-of-distribution behaviors with just 150 preference labels, dramatically increasing object transport height.
Sonar-only SLAM now achieves robust topological mapping by verifying loop closures with a sequence-aware approach, overcoming the inherent ambiguity of acoustic echoes.
Agents can now reliably navigate evolving environments by maintaining a probabilistic 4D belief that accounts for hidden target movements and revises predictions based on intermittent, visibility-aware observations.
Robots can now learn from physical failures in real-time, autonomously revising their own capabilities to achieve unprecedented long-horizon manipulation success.
Learned world models can achieve real-time control in live games, outperforming behavioral cloning and maintaining high-frequency action loops.
Real-world robot policies can be steered to novel, out-of-distribution behaviors with just 150 preference labels, dramatically increasing object transport height.
Sonar-only SLAM now achieves robust topological mapping by verifying loop closures with a sequence-aware approach, overcoming the inherent ambiguity of acoustic echoes.
Agents can now reliably navigate evolving environments by maintaining a probabilistic 4D belief that accounts for hidden target movements and revises predictions based on intermittent, visibility-aware observations.
Robots can now learn from physical failures in real-time, autonomously revising their own capabilities to achieve unprecedented long-horizon manipulation success.
Humans can directly control complex embodied agents using a handful of learned, composable skills discovered through self-play, bypassing the need for low-level action control.
Current VLMs, despite impressive general capabilities, fail to grasp the nuanced social dynamics required for robot navigation, lagging behind simple rule-based systems.
Imagined failures in a world model can pinpoint exactly which robot skill components need new demonstrations, boosting success from 13% to 75% with minimal data.
VLA models fail not by ignoring instructions, but by misbinding them to incorrect action trajectories, a problem solved by training with counterfactually paired demonstrations.
Neural predictors can be made dynamically feasible without ground-truth controls by a frozen, post-hoc operator that enforces learned dynamics manifolds via iterative control correction.
State representation quality, not goal representation, is the critical bottleneck in offline navigation GCRL, with simple positional encodings boosting performance significantly.
Most multimodal models fail to truly understand tool use in real-world egocentric videos, with even Gemini-3.1-Pro struggling to ground actions in visual evidence.
A single visual interface, action views, bridges the gap between video generation models and diverse robotic embodiments, enabling unified action modeling and execution.
Most current robotic manipulation policies fail to retain crucial historical information, leading to significant performance drops in partially observable tasks, but a novel memory framework boosts success rates across diverse challenges.
A 4B grounding foundation model achieves state-of-the-art visual grounding, outperforming larger models and boosting downstream robotic and autonomous driving performance by up to 24.8% in OOD settings.
Robots can adapt to unseen tasks purely through simulated imagination, boosting real-world manipulation success from 20% to 77% without requiring new human demonstrations or external environment rollouts.
Robot policies can now test and refine manipulation plans entirely within a learned visual simulator across ten distinct embodiments, gaining up to 21.4 percentage points in task success without any target-domain fine-tuning.
Robots no longer need human intervention to learn from their mistakes: foundation-model-guided exploration over modular skills can autonomously synthesize corrective rollouts that distill directly into low-level policy weights.
Multi-agent generative models can now provably satisfy complex, coupled hard constraints without centralized runtime coordination—even zero-shot scaling to agent counts never seen during training.
Standard 5-second planning horizons miss over 90% of real-world passenger instructions, exposing a severe architectural failure mode in language-conditioned autonomous driving.
Driver interventions are not cruise control failures—they provide rich, implicit supervision that overcomes the persistent over-smoothing problem in end-to-end driving models.
Predicting future motion instead of reconstructing past trajectories allows a frozen humanoid controller to cut tracking error by 62% while unlocking zero-shot behavior composition via simple latent addition.
Decoupling group-level interaction dynamics from individual body kinematics is the missing ingredient for multiperson generative models to generalize across arbitrary agent counts and severely corrupted observations.
Closed-loop vision verification is no longer bottlenecked by intractable generative surrogates: compact stochastic world models cut parameter counts by 130× while resolving over 80% of previously unverified RGB state spaces.
Humanoid robots can now seamlessly perform complex pick-and-place tasks by jointly retargeting their own motion and the manipulated objects' trajectories, achieving near-perfect interaction fidelity.
General-purpose VLMs can achieve state-of-the-art zero-shot robot manipulation by directly interpreting observations and issuing actions, eliminating the need for specialized policies or grounding tools.
Co-evolved robot communication protocols fail to transfer robustly to the physical world, even with sensory calibration, revealing that ecological and navigational context is as crucial as the signaling process itself.
Robotic agents can now learn complex manipulation strategies from just a few demonstrations by treating them as evidence to be analyzed, not just training data.
Moving quality-diversity optimization from raw policy parameters into the latent space of a behavioral foundation model rescues evolutionary search from total collapse in sparse, deceptive environments while eliminating critic training entirely.
Robots can master delicate tool manipulation directly from unpaired human videos, achieving a 73% performance leap over SOTA by anchoring control in object-centric 3D pose priors rather than raw end-to-end pixels.
Embodied agent bottlenecks in 3D environments stem from downstream physical execution rather than high-level reasoning once visual-symbolic grounding eliminates perceptual ambiguity.
Enforcing contractive dynamical structures onto learned latent disturbance representations delivers predictive disturbance accommodation without sacrificing the exponential stability guarantees essential for real-world adaptive control.
Real-world robot failures can be autonomously converted into interactive digital twins for targeted RL, boosting out-of-distribution manipulation by 18.75% while completely bypassing manual teleoperated recovery.
Purely autoregressive VLAs no longer require awkward hybrid diffusion heads to achieve fine-grained robot control: structuring discrete action tokenization as causally annealed flow matching recovers continuous diffusion precision while preserving end-to-end token efficiency.
Coding agents are failing to demonstrate robust engineering skills in robotics, with RLE-Bench revealing significant gaps in multimodal reasoning and artifact integration beyond simple control.
Real-world multi-robot coordination in vision-and-language tasks is now formally defined and benchmarked, revealing that current systems struggle with even basic dependency and resource constraints.
Iterative intent denoising enables decentralized multi-agent pathfinding agents to coordinate complex joint actions, overcoming the limitations of one-shot sampling and achieving near-perfect task completion.
