MolmoAct 2 is an open Action Reasoning Model that reasons in 3D before directing robot actions, handles bimanual tasks without per-task fine-tuning, and runs up to 37x faster than MolmoAct. For robotics researchers and ML engineers.
700 hours of bimanual robot demonstrations, all open, is the kind of training resource the robotics field has been missing.
What it is: MolmoAct 2 is an open Action Reasoning Model from Ai2 that reasons in 3D before directing physical robot actions, trained in part on the MolmoAct 2-Bimanual YAM dataset, the largest open-source bimanual robotics dataset released to date.
Most robotics foundation models are trained on proprietary data that no one outside the lab can inspect or build on. That makes reproducing results nearly impossible and limits who can meaningfully contribute to the field.
Ai2 built MolmoAct 2 differently, starting with the data. The MolmoAct 2-Bimanual YAM dataset covers 700 hours of two-arm manipulation demonstrations, folding towels, scanning groceries, clearing tables, charging smartphones, and more. It contains over 30 times the robot data used to train the original MolmoAct.
What makes it different: Bimanual capability is baked into the base model rather than added through per-task fine-tuning. The language annotations were reannotated to increase unique instruction labels from 71,000 to around 146,000, which makes the model more robust to real-world phrasing variation.
The dataset was supplemented with a broader mix covering different arms, camera setups, and control schemes so the model generalises beyond the training hardware.
Key features:
700-hour MolmoAct 2-Bimanual YAM dataset, fully open
Native bimanual manipulation without per-task fine-tuning
Reannotated language instructions for phrasing robustness
MolmoAct 2-Think variant with adaptive depth perception tokens
I wonder how this dataset handles the variability in real-world object interactions—does it include failure cases or only successful demonstrations? That could be huge for robust policy learning.
Really useful for generalist training for industrial robots. Usually covering robotic arm manipulation and covering the inverse kinematics is big hassle. Would definately explore this model for Robot training.
About MolmoAct 2 on Product Hunt
“Open robotics model that reasons in 3D before acting”
MolmoAct 2 launched on Product Hunt on May 9th, 2026 and earned 95 upvotes and 3 comments, placing #9 on the daily leaderboard. MolmoAct 2 is an open Action Reasoning Model that reasons in 3D before directing robot actions, handles bimanual tasks without per-task fine-tuning, and runs up to 37x faster than MolmoAct. For robotics researchers and ML engineers.
MolmoAct 2 was featured in Robots (10.6k followers) and Artificial Intelligence (469k followers) on Product Hunt. Together, these topics include over 99k products, making this a competitive space to launch in.
Who hunted MolmoAct 2?
MolmoAct 2 was hunted by Himani Sah. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Himani Sah
700 hours of bimanual robot demonstrations, all open, is the kind of training resource the robotics field has been missing.
What it is: MolmoAct 2 is an open Action Reasoning Model from Ai2 that reasons in 3D before directing physical robot actions, trained in part on the MolmoAct 2-Bimanual YAM dataset, the largest open-source bimanual robotics dataset released to date.
Most robotics foundation models are trained on proprietary data that no one outside the lab can inspect or build on. That makes reproducing results nearly impossible and limits who can meaningfully contribute to the field.
Ai2 built MolmoAct 2 differently, starting with the data. The MolmoAct 2-Bimanual YAM dataset covers 700 hours of two-arm manipulation demonstrations, folding towels, scanning groceries, clearing tables, charging smartphones, and more. It contains over 30 times the robot data used to train the original MolmoAct.
What makes it different: Bimanual capability is baked into the base model rather than added through per-task fine-tuning. The language annotations were reannotated to increase unique instruction labels from 71,000 to around 146,000, which makes the model more robust to real-world phrasing variation.
The dataset was supplemented with a broader mix covering different arms, camera setups, and control schemes so the model generalises beyond the training hardware.
Key features:
700-hour MolmoAct 2-Bimanual YAM dataset, fully open
Native bimanual manipulation without per-task fine-tuning
Reannotated language instructions for phrasing robustness
MolmoAct 2-Think variant with adaptive depth perception tokens
Reference hardware setup published: YAM arms, overhead and close-up cameras, tabletop workspace
Benefits:
Researchers can study, reproduce, and build on the training data directly
Dataset covers varied arms, cameras, and control schemes for broader generalisation
Open action tokenizer released alongside model weights
Training code coming soon under open-source license
Who it's for: Robotics researchers and ML engineers who need open training data and reproducible recipes to build or improve manipulation models.
The data problem in robotics AI is as significant as the model problem. Releasing both together is what makes this launch worth tracking.