Search papers, labs, and topics across Lattice.
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Integration of diverse robot policies can be streamlined from hours to minutes, revolutionizing how we deploy and evaluate robotic systems.
Trajectory anchoring bias in VLA models can be mitigated by transforming future trajectory decisions into verifiable selections, leading to more reliable reasoning in autonomous driving.
Trustworthiness in embodied intelligence isn't just about performance; it's about managing risk across a multi-layered framework that ensures safety and reliability in real-world applications.
RxBrain achieves a groundbreaking integration of language and visual reasoning, enabling agents to generate embodied plans that seamlessly connect abstract tasks with physical actions.
RoboDojo reveals that integrating simulation and real-world tasks can significantly enhance the evaluation of robot manipulation policies, bridging the gap between theoretical performance and practical deployment.
UMMs exhibit significant exposure bias in multi-turn interactions, revealing critical performance gaps that existing benchmarks overlook.
Forget painstakingly creating 3D assets for robot training - ManiTwin automates the process, turning single images into simulation-ready objects at scale.
