Proud of @madrish02 and the entire team's work on TACTIC. What I find most compelling about TACTIC is that contact is not treated as just another sensor input or a penalty term, it is built into every layer of the planning stack. In the representation, RGB-D, proprioception, and distributed tactile sensing are fused through a proximity mask that focuses the model on the interaction geometry that actually matters: where contact is happening, and where it may happen next. In sampling, contact Jacobians shape MPPI exploration, biasing candidate actions toward directions that can meaningfully regulate forces and steering unsafe samples away from high-force contacts. In prediction, hybrid rollouts combine learned interaction dynamics with analytical robot kinematics: learn what is hard to model, while preserving the structure we already know. Interestingly, this hybrid variant idea that adds a kinematics-based goal cost during planning also improves planning with other learned world models, including V-JEPA2 on Reach and DINO-WM on Granular. And in scoring, trajectories are evaluated not only for task progress, but also for contact evolution, force regulation, joint limits, and collisions. That contact-centricity across representation, sampling, prediction, and scoring is, to me, the most exciting part of TACTIC. Very proud of the team and excited to see this work at #RSS2026! @EmpriseLab @Cornell_Bowers @Cornell_CS