Search papers, labs, and topics across Lattice.
63 papers published across 7 labs.
Latent world models scale predictably with compute via a second-order power law, enabling accurate forecasting of robotic planning performance and zero-shot task execution on real hardware.
A single-word instruction change can tank VLA performance by 98%, but a simple LLM-based rephrasing pipeline boosts success by up to 27% without retraining.
Current multimodal agents can build sophisticated perception-control pipelines but fail to reliably execute physical tasks, losing object states and mistaking unfinished goals for completion.
Autoregressive video pretraining unlocks the full potential of long contexts for real-time robot control, boosting success rates by 15% and enabling complex manipulation previously impossible.
Fingertip touch dynamics, not just vision, unlock a 10.5% boost in dexterous robotic grasp success.
Latent world models scale predictably with compute via a second-order power law, enabling accurate forecasting of robotic planning performance and zero-shot task execution on real hardware.
A single-word instruction change can tank VLA performance by 98%, but a simple LLM-based rephrasing pipeline boosts success by up to 27% without retraining.
Current multimodal agents can build sophisticated perception-control pipelines but fail to reliably execute physical tasks, losing object states and mistaking unfinished goals for completion.
Autoregressive video pretraining unlocks the full potential of long contexts for real-time robot control, boosting success rates by 15% and enabling complex manipulation previously impossible.
Fingertip touch dynamics, not just vision, unlock a 10.5% boost in dexterous robotic grasp success.
Quadrupedal robots can achieve up to 20% lower energy consumption by learning speed-dependent gaits with a hierarchical RL policy that explicitly optimizes cost of transport.
Physical robot learning can now autonomously discover and exploit informative experiments to continuously improve foundation models, achieving 77% success on RoboCasa365 and zero-shot transfer to real hardware.
Frontier multimodal agents struggle to autonomously acquire and utilize task-relevant information in unfamiliar physical environments, failing 77% of the time on a new exploration benchmark.
Embodied agents can now remember objects they never touched by tracking their resting locations and inferring moves from repeated evidence, boosting downstream task accuracy by over 10%.
Co-evolved communication protocols exhibit surprising robustness when transferred from 2D to 3D physics, with performance scaling linearly with episode length and social cues acting predominantly as jam-assistance.
A single 500-hour visual-tactile dataset boosts zero-shot contact prediction from 13% to 38% IoU and quadruples robot manipulation success rates, proving scale and consistency are paramount for embodied learning.
A simulation flywheel recursively bootstraps embodied data, generating richer scenes and tasks for robot pretraining and evaluation.
Most video world models fail basic physics tests, with even the best models exhibiting significant physical inconsistencies across mechanics, optics, and fluids.
Humanoid locomotion learned from scratch and transferred to hardware in 10x less time, thanks to a novel off-policy flow RL approach.
Embodied agents can now reliably achieve complex, multi-stage goals in dynamic environments by learning to predict and ground targets from decoupled visual context, even when the goal is out of view.
A 48.9M parameter robot policy matches systems 163x larger by prioritizing fine-grained, compressed visual representations over scale or generative priors.
End-to-end driving models can learn interpretable safety costs over reachable futures, drastically reducing collisions without fine-tuning.
A single RL policy masters energy-efficient path following across diverse tracks and even complex vehicle dynamics, outperforming NMPC and baselines without explicit track-specific training.
Robot world models can now predict photorealistic RGB *and* accurate metric depth, bridging the gap between visually plausible rollouts and geometrically consistent 3D environments.
Language instructions now directly control real-time, high-fidelity 360° video generation, enabling fluid interactive exploration from a single panorama.
Accelerating VLA models without sacrificing task success is now certifiable, guaranteeing over $85\%$ of reference performance with $10\times$ speedups.
Predicting subskill transition timing unlocks reliable execution of 4x longer action chunks in VLA models, drastically cutting robotic idle time by 5x.
Achieve 25x speedup in World Action Models with <1% performance drop by enabling one-step action generation and asynchronous expert execution.
Dual-arm robots can generalize to unseen task compositions by combining skill-conditioned parameters and arm-wise attention, achieving 39% success on physical hardware.
Hierarchical JEPAs unlock efficient long-horizon visual planning by learning distinct latent spaces for different temporal scales, dramatically boosting success rates and reducing compute.
Humanoid robots can now learn complex loco-manipulation tasks from sparse demonstrations by iteratively evolving their own training data and policies.
Asynchronous execution fundamentally alters action semantics for world-model controllers, causing planning models to hallucinate future actions and recurrent models to misattribute transitions.
TSFMs for predictive control fail to capture system dynamics without explicit control excitation in their inference context, even with low forecasting error.
A dual VAE architecture effectively bridges the sim-to-real gap for robotic navigation, enabling high performance with minimal real-world data.
Robots can learn to sacrifice immediate gains for superior team coordination, outperforming centralized and reactive planners even with over a thousand agents.
A single, static adversarial patch can cripple world action models across diverse tasks and architectures by inducing a stable, global representation shift, even without querying the target policy.
Robots can now learn complex manipulation tasks from a single demonstration video by recursively accessing fine-grained sub-event details, boosting success rates by up to 9%.
Directly learning robot action policies from raw video without explicit action labels unlocks scalable world action models.
A single set of learned manipulation skills transfers to the real world with 78% success, bypassing the need for task-specific fine-tuning or re-training on real data.
Robot skills can be dramatically improved post-training by autonomously generating practice tasks, diagnosing failures with privileged simulator states, and iteratively refining symbolic skills and system prompts, achieving near-perfect success on novel manipulation tasks.
Achieve precise, intuitive 3D control over video generation by moving rigid object meshes, eliminating depth ambiguity and viewpoint inconsistencies.
Robot agents achieve 14% higher success rates and use 65% fewer tokens by composing primitives and performing local retries within an interactive code execution framework.
Humanoid robots can learn entirely new complex manipulation skills at test-time by evolving reward programs, unlocking latent capabilities of pre-trained controllers.
Judgment models can solve complex problems when the answer is directly evaluable from the input, but fail catastrophically when simulation is required, even if they possess the underlying knowledge.
A novel Four-Dimensional Architecture Selection Framework correctly recommends 70% of deployed autonomous driving architectures, offering critical guidance for system design.
Current egocentric video generation models fail to execute multi-step manipulation tasks realistically, often skipping crucial actions and exhibiting poor physical dynamics.
A robot can learn its partner's hidden physical limitations simply by watching it collaborate, enabling seamless coordination on novel tasks without explicit communication.
Jointly training on simulation and experimental data unlocks balanced prediction in physical systems, outperforming sequential transfer and preserving crucial simulation-only knowledge.
Diffusion models can plan collision-free, multi-agent trajectories without any training data by leveraging analytical local scores derived from geometric constraints.
Semantic communication via VLM orchestrators unlocks robust, long-horizon coordination for multi-robot systems, outperforming prior single-robot approaches on complex manipulation tasks.
Humanoid robots struggle to bridge the gap between selecting the right tool and executing complex tasks, with current policies showing significant performance degradation on unseen tools and unrelated instructions.
Robots can now plan complex, long-horizon tasks in novel environments by predicting sparse visual sub-goals, achieving 70% success in zero-shot real-world experiments.
An understanding-driven approach unlocks systematic spatial-memory management for world models, dramatically improving long-video generation quality.
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.
