Introducing MolmoSpaces, a large-scale, fully open platform benchmark for embodied AI research. 🤖 230k indoor scenes, 130k object models, & 42M annotated robotic grasps—all in one ecosystem.

We are open-sourcing Wall-OSS-0.5. Pretrain Once, Act Anywhere. Wall-OSS-0.5 is a VLA model for real-world robotic manipulation, exploring whether pretraining alone can produce robot capabilities directly testable on physical hardware before task-specific fine-tuning. Key technical highlights: • Gradient-bridged co-training • Vision-Aligned RVQ Action Tokenizer • Action-Space Supervision • DMuon distributed optimizer In zero-shot real-robot evaluation, the pretrained checkpoint achieved task-progress scores above 80 on multiple tasks, including Block Sorting, Fruit Sorting, Ring Stacking, and Rope Tightening. Paper, code, blog, and uncut videos: x2robot.com/oss#resources

Benchmarking, evaluating, and developing robotics code is difficult, and part of this is because no simulator really reflects the diversity and scale of real embodiments. Enter MolmoSpaces from AI2: a massive open ecosystem with a range of 230,000 handcrafted and procedurally-generated home environments, including 48,000 manipulable objects. Crucially, MolmoSpaces provides simulation environments which work for both navigation and manipulation. We talked to the team: @YejinKim4, @omarrayyann, and Max Argus, to tell us more. Watch Episode 69 of RoboPapers, with @micoolcho and @DJiafei, now!

Today, a step forward in open robotics - our results show that sim-to-real zero shot transfer for manipulation is possible. MolmoBot is our open model suite for robotics, trained entirely in simulation on MolmoSpaces.🧵

𝐃𝐫𝐞𝐚𝐦𝐙𝐞𝐫𝐨 𝐢𝐬 #𝟏 𝐨𝐧 𝐛𝐨𝐭𝐡 𝐌𝐨𝐥𝐦𝐨𝐒𝐩𝐚𝐜𝐞𝐬 𝐚𝐧𝐝 𝐑𝐨𝐛𝐨𝐀𝐫𝐞𝐧𝐚 🏆 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗻𝗼𝘁𝗮𝗯𝗹𝗲: DreamZero-DROID is trained 𝑓𝑟𝑜𝑚 𝑠𝑐𝑟𝑎𝑡𝑐ℎ using only the DROID dataset. No pretraining on large-scale robot data, unlike competing VLAs. This demonstrates the strength of video-model backbones for generalist robot policies (VAMs/WAMs). More broadly, training 𝑜𝑛𝑙𝑦 on real data and evaluating on (1) transparent, distributed benchmarks like 𝐑𝐨𝐛𝐨𝐀𝐫𝐞𝐧𝐚 or (2) scalable sim-benchmarks like 𝐌𝐨𝐥𝐦𝐨𝐒𝐩𝐚𝐜𝐞𝐬 is an exciting step toward fairer and more reproducible evaluation of generalist policies, one that the community can hillclimb together to measure progress. Special thanks to the Ai2 MolmoSpaces team (@notmahi @omarrayyann @YejinKim4 Max Argus) and the RoboArena team (@pranav_atreya) for helping with the set-up and getting these evaluations! Special shout out to @youliangtan @NadunRanawakaA @chuning_zhu, who led these efforts from the GEAR side :) We also release our DreamZero-AgiBot checkpoint & post-training code to enable very efficient few-shot adaptation. Post-train on just ~30 minutes of play data for your specific robot, and see the robot do basic language following and pick-and-place 🤗(See YAM experiments in our paper for more detail). We also provide the entire codebase & preprocessed dataset to replicate the DreamZero-DROID checkpoint. 🌐 dreamzero0.github.io 💻 github.com/dreamzero0/dreamz… RoboArena: robo-arena.github.io/leaderb… MolmoSpaces: molmospaces.allen.ai/leaderb…

It’s hard to find true zero-shot end-to-end policies – ones that work without any fine-tuning in fully novel, simulated environments, even for single tasks! We test two policy families, the π family from @physical_int and the recent Contact-Anchored Policies (CAP) from NYU & UCB. On all our tasks, we are making steady progress – but we are nowhere close to saturation yet.

MolmoSpaces provides singular scale and diversity. We built a benchmark that puts that scale to use. MolmoSpaces-Bench evaluates zero-shot policies across thousands of environments previously unseen to them under systematic variation, providing insights that go beyond a success rate % More Below: