CrossFormers Whitepaper Review: One Policy for Every Robot
- Sep 11, 2024
- 3 min read
The Short Version
Watch Shantanu's review of the CrossFormers paper from UC Berkeley and Carnegie Mellon.
Understand how a single transformer-based policy controls robots across manipulation, navigation, locomotion, and aviation.
Note the training scale: 900,000 trajectories across 30 robot embodiments.
See how CrossFormers process varied sensor inputs without manual alignment of action spaces.
Compare CrossFormers' 73% success rate against specialist policies on WidowX BridgeV2, Unitree Go1, and Tello Quadcopter.
Read the full whitepaper at arxiv.org/abs/2408.11812.
Start building your own machine learning models with an Aloha Kit from Trossen Robotics.
Who this is for
Robotics ML researchers
Reinforcement and imitation learning engineers
Roboticists exploring generalist policies
Trossen Robotics and Aloha Kit users
Academic teams studying cross-embodied learning
In today's video, Shantanu reviews the groundbreaking paper "CrossFormers: Scaling Cross-Embodied Learning," authored by researchers from UC Berkeley and Carnegie Mellon. CrossFormers introduce a transformer-based policy that can control a wide variety of robots across different tasks—including manipulation, navigation, locomotion, and even aviation. The short version: instead of one policy per robot, CrossFormers train a *single* policy on 900,000 trajectories across 30 robot embodiments, and it outperformed specialist policies with a 73% success rate.
What problem do CrossFormers solve?
Traditionally, robot learning requires specific policies for each robot and each task. CrossFormers break the mold.
By training on the largest, most diverse dataset ever—900,000 trajectories across 30 robot embodiments—a single policy can handle everything from bimanual robots to quadcopters.
How does the CrossFormers architecture work?
The transformer-based architecture adapts to different robot embodiments and control frequencies, creating unmatched flexibility. CrossFormers process a variety of sensor inputs, which eliminates the need for manual alignment of action spaces.
That flexibility is what lets one model move across such different machines without hand-tuning for each one.
How well do CrossFormers perform?
CrossFormers were tested across platforms like the WidowX BridgeV2, Unitree Go1, and Tello Quadcopter. Here's how the results stack up:
Detail | Result |
Training dataset | 900,000 trajectories |
Robot embodiments | 30 |
Test platforms | WidowX BridgeV2, Unitree Go1, Tello Quadcopter |
Success rate | 73% (outperforming specialist policies) |
Why CrossFormers matter for generalist robot policies
Check out the full review and learn how CrossFormers are shaping the future of generalist robot policies. Watch now and dive into cutting-edge robotic machine learning.
Want to build your own? Start making your own machine learning models with an Aloha Kit from Trossen Robotics, and use the Trossen SDK to get started.
References
Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation — https://arxiv.org/abs/2408.11812
References
Frequently Asked Questions
What is the Crossformers whitepaper review about?
It's Shantanu's review of 'CrossFormers: Scaling Cross-Embodied Learning,' a paper by UC Berkeley and Carnegie Mellon researchers introducing a transformer-based policy that controls many robots across different tasks.
What tasks can CrossFormers handle?
A single CrossFormers policy handles manipulation, navigation, locomotion, and even aviation, working across everything from bimanual robots to quadcopters.
How large is the CrossFormers training dataset?
CrossFormers train on the largest, most diverse dataset ever—900,000 trajectories across 30 robot embodiments.
How well do CrossFormers perform?
Tested across platforms like the WidowX BridgeV2, Unitree Go1, and Tello Quadcopter, CrossFormers outperformed specialist policies with a 73% success rate.
What makes CrossFormers different from traditional robot learning?
Traditionally, robot learning requires specific policies for each robot and task, but CrossFormers use one transformer-based policy that adapts to different embodiments and control frequencies without manual alignment of action spaces.
How can I start building my own machine learning models?
You can start making your own machine learning models with an Aloha Kit from Trossen Robotics.
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