Stars and mentions
Last 30 days, Oct 5 – Nov 3
Package downloads
Top repositories
amazon-far’s repositories ranked by new stars, as on trending.
Contributes to
Repositories outside amazon-far’s account where amazon-far opened or reviewed pull requests in the past 365 days.
Who noticed amazon-far
Everyone who starred or forked amazon-far’s repositories since the archive began in 2011.
Notable accounts
Ordered by the stars their own repositories have received.
Biggest fans
Accounts that starred the most of these repositories.
Breakout weeks
Each repository’s best week of new stars, with the release published around it.
Achievements
Awards earned by the repositories amazon-far owns. Select an award to embed its badge.
No awards yet
Repositories earn awards by reaching the top three in a completed trending day or week.
A repository earns an award for finishing first, second or third in a completed trending day or week, ranked by new stars, new forks or pull request authors. Awards follow a repository through renames and transfers and count toward its current owner. Weeks run Monday to Sunday, UTC.
Mentions
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