Announcing OpenThinker3-7B, the new SOTA open-data 7B reasoning model: improving over DeepSeek-R1-Distill-Qwen-7B by 33% on average over code, science, and math evals. We also release our dataset, OpenThoughts3-1.2M, which is the best open reasoning dataset across all data scales. Full details are in our ✨new paper✨ - below we share the highlights: BTW, it also works on non-Qwen models😉 (1/N)

Meet ProVox: a proactive robot teammate that gets you 🤖❤️‍🔥 ProVox models your goals and expectations before a task starts — enabling personalized, proactive help for smoother, more natural collaboration. All powered by LLM commonsense. Recently accepted at @ieeeras R-AL! 🧵1/7

Image/video models can transfer knowledge from Internet data to robot agents by generating goal images. But what happens when images have harmful visual artifacts? We present GHIL-Glue, a method to align image/video models and low-level policies. ghil-glue.github.io/

Learned visuomotor robot policies are sensitive to observation viewpoint shifts, which happen all the time. Can visual priors from large-scale data help? Introducing VISTA: using zero-shot novel view synthesis models for view-robust policy learning! #CoRL2024 🧵👇

After two years, it is my pleasure to introduce “DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset” DROID is the most diverse robotic interaction dataset ever released, including 385 hours of data collected across 564 diverse scenes in real-world households and offices

Access to *diverse* training data is a major bottleneck in robot learning. We're releasing DROID, a large-scale in-the-wild manipulation dataset. 76k trajectories, 500 scenes, multi-view stereo, language annotations etc Check it out & download today! 💻: droid-dataset.github.io/

ICRA 2022 AV workshop videos now online: 🚗 keynotes: icra2022av.org/#h.198firfh7f… 🚗 authors: icra2022av.org/#h.r8uf7ehwpn… 🚗 all: youtu.be/qzo61V7G1EM?t=538 Thanks to lead organizer @corina_gurau, virtual org @wulfebw @hu_anth @pondruska @blazejosinski, PC members authors attendees!

How should models be trained? We show weighting model errors by their effect on downstream control improves accuracy where it matters most (gif) improves end performance arxiv.org/abs/2204.13319 youtu.be/OSzh6Jkp5io w @wulfebw @MercatJean @logan_m_ellis @svlevine @adnothing

*Deadline Extension* Call for a second round of submissions to the #ICRA2022 workshop on Fresh Perspectives on the Future of Autonomous Driving. Submit by April 25 AoE: icra2022av.org/ @rowantmc @hu_anth @wulfebw @pondruska @felipealcm @nick_rhinehart @szagoruyko5

Calling papers for the @ieee_ras_icra workshop on Fresh Perspectives on the Future of Autonomous Driving in May! Submit by 22 March: self-driving-cars.org/worksh… with @corina_gurau @hu_anth @szagoruyko5 @felipealcm @nick_rhinehart @pondruska @wulfebw Błażej Osiński #ICRA2022