Join us for an in-person Q&A with the authors of MuJoCo, Tom Erez & Yuval Tassa from @GoogleDeepMind at the Google booth (#411#) today at 3PM! Bring your questions. #ICLR2026# #MuJoCo#

Hours of manual asset authoring => Down to 1–2 minutes with 1 prompt. SR Platform generates interactive, simulation-ready 3D assets for training robotics, complete with articulated objects and physics. Native support for MuJoCo and NVIDIA Isaac Sim. Coming in July!

I recently started dabbling in robotics, and a thought hit me. 🤖💭 Robots, AI agents, and business systems (ERP/WMS) all end up talking past each other in completely different data, don't they? Robots emit point clouds and sensor values on a millisecond scale 📡, AI agents want text with sources 📄, and business systems hold structured records on an hour-to-month scale 📊. Wiring these three together with bespoke integrations gets unrealistic fast — the number of pairs explodes as N×(N−1). 💥 So I ran an experiment: what if everyone dumps their data into the same search system and pulls out what they need by searching? Can search become a common protocol? I called it Multi-World Search (MWS). 🔍 Inside MuJoCo on a single MacBook 💻, I let a Gemini agent drive end to end across 7 business scenarios — a maintenance handoff 🔧, reconciling physical inventory against stale records 📦, and my personal favorite: three robots pinning down a defective lot without any direct communication 🐜. To avoid faking the "it worked!", I attached falsification tests that fail when you change the input. ✅ I'd be glad if it became a starting point for anyone just getting into robotics. Take a peek. 🙏✨ 📝 Blog: #Robotics# #AIAgents#

🏠 Describe a room in plain text, and out comes a complete, physics-ready scene a robot can actually interact with. That's SceneSmith, an ICML 2026 Spotlight from MIT and Toyota Research Institute. Title: nepfaff/scenesmith (SceneSmith) URL: 🏠 Overview SceneSmith is an agentic system that generates simulation-ready indoor scenes from natural language. It produces furniture, wall-mounted mirrors and artwork, ceiling chandeliers, and small tabletop items — all with physical properties like mass and inertia — so the scenes can be used directly for robot training and evaluation. ❓ Challenges Solved Building realistic indoor scenes for robot simulation has meant manual modeling or tedious scene composition, a major bottleneck for scaling robot evaluation and training. SceneSmith removes this by automatically generating diverse, contextually coherent scenes from text prompts. 💡 Methodology & Approach Scene generation runs as a five-stage sequential pipeline. ・Floor plan generation (walls and floor layout) ・Large furniture placement ・Wall-mounted objects (mirrors, artwork, shelves, clocks) ・Ceiling fixtures (chandeliers, pendant lights, ceiling fans) ・Manipulable small objects Checkpoints are saved automatically after each stage, so you can resume or branch midway. Scene reasoning and task decomposition use a VLM agent (GPT-5). 🎯 Use Cases & Tech ・3D assets are generated with the high-quality SAM3D (recommended) or Hunyuan3D-2, with retrieval from HSSD and Objaverse also supported ・AmbientCG PBR materials are applied via CLIP-based semantic search, and articulated objects from ArtVIP and PartNet-Mobility are handled with joint kinematics ・Output is native Drake format, with export to MuJoCo, USD, and Isaac Sim 📊 Highlights ・From a task like "find a fruit from the bowl and place it on a plate," it generates multiple constrained scene variations and supports robot evaluation ・A 151-word prompt yields a community center, even inferring context like placing ping pong paddles and balls near the table ・Geometry generation is distributed across GPUs, with bubblewrap isolation preventing rendering OOM #Robotics# #SceneGeneration#