Action-conditioned latent world models predict future visual representations, enabling zero-shot goal-conditioned robot planning and control. However, their predictions for fine-grained spatial and rotational actions are unreliable for full 7-DoF end-effector control. To address this gap, we introduce DUET-DINO, a simu...
Nisarga Nilavadi, Ralf Römer, Moritz Reuss et al.· 0 citations
Recent 3D foundation models enable generalizable geometric reasoning from RGB images but remain limited in persistent memory, scalability, and renderable scene modeling. We present a memory-centric 3D foundation model for scalable robotic localization, reconstruction, and Gaussian rendering. Its core is an adaptive wor...
Tianchen Deng, Guo-Le Shen, Yi-Lin Shen et al.· 0 citations
While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly represent actions as target positions or poses. Even methods that incorporate force sensing either use it solely as an observation or, when predicting forces as part of th...
Johannes Hechtl, Yannik Blei, Simon Ball et al.· 0 citations
This work uses intervention-based metrics that measure how visual observations, instructions, and visual memory causally influence navigation decisions and shows that these navigation policies are sensitive to all input modalities and do not depend on a single one.
This work introduces MissionBench, a benchmark for mission-level evaluation of MLLMs in aerial 3D environments, and shows that mission-level competence requires coordinating multiple capabilities beyond spatial perception, including multi-step planning and adaptive reasoning.
Suman Navaratnarajah, Taehyoung Kim, Jona Ruthardt et al.· arXiv.org· 0 citations
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