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At the very early stage of RPL (amazon-far/RPL), @younggyo and I discussed a similar idea during multi-terrain rl training — using terrain-level as a proxy of task success rate to guide terrain configuration sampling at environment resets. It indeed improved the policy performance on multiple terrains, though our two-stage distillation pipeline ultimately worked better in our experiments, at the cost of separate expert training. Nice to see success-guided sampling studied systematically and at this scale!
RPL code is released at amazon-far/RPL, including experts training, multi-depth distillation and sim2sim. If you have an Xbox controller lying around, it’s a lot of fun playing it in Mujoco. Its perceptive features were adopted by some cocurrent work like LadderMan (ladderman-robot.github.io) and PHP (php-parkour.github.io) with promising results. RPL was also accepted to CoRL2026. See you in Austin.
This work is done during my internship at Amazon FAR with my amazing collaborators @rutavms @ShuaiyiH @SOTA_kke @mangahomanga @andrea_bajcsy @max_simchowitz & @GuanyaShi 🙌 🌐 Website: yilin-wu98.github.io 📄 Paper: arxiv.org 💻 Code: amazon-far/imprint Let’s start with the tasks 👇 2/10
Perceptive Humanoid Parkour (PHP) is now open source! Humanoids can autonomously climb 1.25m walls, vault obstacles, and chain complex parkour skills using onboard depth perception. Code: amazon-far/php_parkour Demo: php-parkour.github.io
Perceptive Humanoid Parkour (PHP) is now OPEN-SOURCE! 🔥 Try training your own parkour robot! 🤖 🔗 Project: php-parkour.github.io 💻 Code: amazon-far/php_parkour 🎮 Try it in your browser: php-parkour.github.io t.co