We just released FastTD3: a simple, fast, off-policy RL algorithm to train humanoid policies that transfer seamlessly from simulation to the real world.

younggyo.me/fast_td3

FuSe policies reason jointly over vision, touch, and sound, enabling tasks such as multimodal disambiguation, generation of object descriptions upon interaction, and compositional cross-modal prompting (e.g., “press the button with the same color as the soft object”).

Ever wondered what robots 🤖 could achieve if they could not just see – but also feel and hear?

Introducing FuSe: a recipe for finetuning large vision-language-action (VLA) models with heterogeneous sensory data, such as vision, touch, sound, and more.

Details in the thread 👇

By combining MaxInfoRL with DrQv2 and DrM, this achieves state-of-the-art model-free performance on hard visual control tasks such as DMControl humanoid and dog tasks, improving both sample efficiency and steady-state performance.

While standard Boltzmann exploration (e.g., SAC) focuses only on action entropy, MaxInfoRL maximizes entropy in both state and action spaces! This proves to be crucial when dealing with complex exploration settings.

🚨 New reinforcement learning algorithms 🚨

Excited to announce MaxInfoRL, a class of model-free RL algorithms that solves complex continuous control tasks (including vision-based!) by steering exploration towards informative transitions.

Details in the thread 👇

While standard Boltzmann exploration (e.g., SAC) focuses only on action entropy, MaxInfoRL maximizes entropy in both state and action spaces! This proves to be crucial when dealing with complex exploration settings.