I unfortunately had to cancel my #RSS2026 trip last minute, but fortunately my excellent students, collaborators and postdocs will be representing our work much better than me anyways :) We have 4 papers that you might enjoy (Sydney time): 1. Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation sim-dist.github.io/ (Mon 7/13 3:15-4:00pm) @ty_westenbroek @jakejlevy 2. TMRL: Diffusion Timestep-Modulated Pre-training Enables Exploration for Efficient Policy Fine-tuning weirdlabuw.github.io/tmrl/ (Thu 7/16 3:15-4:00pm) @matthewh6_ @Jesse_Y_Zhang 3. PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies polaris-evals.github.io/ (Tue 7/14, 11:50-12:30pm) @prodarhan @KarlPertsch 4. Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparison robometer.github.io/ (Wed 7/15, 3:15-4:00pm) @Jesse_Y_Zhang @aliangdw @yigitkkorkmaz Please go ask them many difficult questions! :) Also - go see the amazing @Jesse_Y_Zhang at his RSS pioneers poster Tue 4-5pm!

I’m presenting SimDist at #RSS2026 today! This framework takes failing policies to ~90% success with only 15-30 minutes of data. Meet me at the poster or reach out to chat! 💬 Presentation: “World Models & Memory”, 3:15-4:00PM 📜 Poster: 6:30-7:30PM 🌐 sim-dist.github.io/

Punchline: distill world models from simulation to enable fast, stable real-world robot adaptation. Simulation is nearly always wrong. But in Simulation Distillation, we ask a simple question: How do we perform simulation pretraining such that real-world adaptation becomes trivially easy? sim-dist.github.io Let's take a closer look (1/n)

Real-world RL is still too brittle and data-hungry for long-horizon, contact-rich tasks. We introduce Simulation Distillation (SimDist), which turns large-scale simulated experience into reusable world-model priors for rapid real-world adaptation. By combining online planning with dynamics adaptation, SimDist achieves high success rates on tasks requiring precision, force, and reactivity. Play with our interactive visualization to see for yourself: sim-dist.github.io (1/n)