Co-founder and CTO @RewardAI_ | Prev PhD @Stanford
Stanford, CA
Joined January 2021
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Chen Wang retweeted
imo, the the impressive part here is grip stability during locomotion, since every step down jolts the load in the hands.
also stairs are turning into one of the most useful test benches in humanoid robotics.
Their OM-1 model here learns from people wearing a sensorized glove that records hand pose, touch and images, with no teleoperation or on-robot demonstrations.
Chen Wang retweeted
Very impressive! The reactiveness in package handling shows the importance of pushing tracking to the limit in data collection to capture the subconscious human behaviors!
Chen Wang retweeted
This video exactly shows why we need bipedal humanoid robots (physical labor in very unstructured envs)
Chen Wang retweeted
We deployed OM-1 across an expanding range of humanoid tasks, both indoors and outdoors.
From last-mile delivery out of a Cybertruck to manipulation on stairs, OM-1 enables humanoids to reliably tackle useful real-world tasks that other models or form factors struggle with.
Excited to show OM-1 taking on last-mile delivery! From picking up packages to carrying them down, it coordinates the whole body and adapts on the fly. We’re pushing into more tasks and environments behind the scenes. More to come!
Chen Wang retweeted
It was really exciting to see our humanoids pick up a package from the stairs and open the fridge to grab a drink. These everyday moments bring our any-robot intelligence to life.
Chen Wang retweeted
I still remember landing at SFO on day one, being greeted by @zipengfu , @chenwang_j , and the beautiful sunshine.
Despite the challenges posed by the California sunlight ☀️, OM-1 brings robust whole-body humanoid capabilities outdoors, from last-mile delivery to picking up things from the ground. Excited to share our latest progress!
Chen Wang retweeted
Super impressive! The co-design of mechanics, sensing, and data are fantastic, and the policy is incredibly dynamic and smooth! Living the dream toward human-level dexterity! Big congrats to @chenwang_j, @zipengfu and the team!
Chen Wang retweeted
That towel flick has personality. Why does the robot make it look so casual?
Chen Wang retweeted
daaamn, just realised this is on 1x speed
smoooth
Chen Wang retweeted
ロボットがバーテンダーの手捬きを再現しているこの映像、人間の作業データだけで学んでいる。しかも映像は等倍。
Reward AIのロボット基盤モデル「OM-1」。一言でいうと、ロボットの学習データをロボットから取るのをやめた。
・センサー付きグローブを着けた人間が普通に作業する、そのデータだけで学習
・テレオペも実機データもゼロ
・卓上アーム / 産業用アーム / ヒューマノイドにゼロショットで転用
・複数ロボットの協調作業まで射程に入っている
ポイントは「ゼロショット」のところ。
従来のロボット基盤モデルは、テレオペで実機を動かして集めたデータが前提だった。だからデータが機体に紐づく。機体を変えれば集め直し。OM-1はデータの入口を人間の手に置いたので、まだ存在しない機体のためのデータを今日から貯められる、とのことだ。
Chen Wang retweeted
Learning from human data could let robot learning scale beyond robot data collection. Cross-embodiment models make this even more interesting - shared skills across different robot bodies. A very promising direction!
Chen Wang retweeted
Amazing!!!
Chen Wang retweeted
This is huge! 🤯
Reliance on real robot data and cross embodiment are big issues of robotics action models! Super excited to see OM1 solving both of them!
The motion is also fast and looks smart!
Great work @zipengfu @chenwang_j!
Chen Wang retweeted
This is really good; very difficult task for a robot
Chen Wang retweeted
Stealth startup @RewardAI_ has unveiled OM-1, an embodied foundation model learned directly from human manipulation data. Zero teleoperation or on-robot data involved.
Spun out of Stanford’s DexCap research, the stack pairs a wearable 7-DoF "Omnibody Hand" with an RL control layer to generalize across tabletop arms, industrial robots, and humanoids.
Chen Wang retweeted
Very impressed by the smoothness and speed of the policy, congrats to friends @chenwang_j @zipengfu @yifengzhu_ut etc at @RewardAI_!
Chen Wang retweeted
Very smooth actions!
Chen Wang retweeted
Really cool stuff! Huge congratulations to the team @zipengfu @chenwang_j @yifengzhu_ut behind this. It’s great to see strong academic research making such meaningful progress in robotics!