Omar Rayyan

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/dre… RoboArena: robo-arena.github.io/leaderboard MolmoSpaces: molmospaces.allen.ai/leaderboard

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/dre… RoboArena: robo-arena.github.io/leaderboard MolmoSpaces: molmospaces.allen.ai/leaderboard

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.