Hitesh @heetezition

musing in ai + robotics. documenting the journey @tofufolabs hitesh.one dreams are important 🌄 Joined June 2011

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Hitesh @heetezition

22 hours ago

pls suggest better credit cards

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Hitesh @heetezition

22 hours ago

@tata_neu is such a scammy app. you try and book flights and they charge you 30% higher than what you could find on MMT. They showing 10k for 16th Oct, none of the flights match the price

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Hitesh @heetezition

2 days ago

assembling the so-101 is the hardware version of installing CUDA before chatgpt. It makes your soul scream.

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Some exciting personal news: I'm starting a newsletter in the Understanding AI family: Understanding Robots! (@binarybits will be an occasional AV correspondent) Up first, I explain GPT-Astra 6's impressive robot capabilities and what it might mean for the future of robotics

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Hitesh @heetezition

3 days ago

@ItsSwaty @ANI no, attacker was the copilot.

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Hitesh @heetezition

3 days ago

@solsol_choi climax that I didn’t expect

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Hitesh @heetezition

4 days ago

@DrewPavlou 😂😂

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Hitesh @heetezition

4 days ago

@braedenhall_ is it localizing indoors autonomously?? if yes then how??

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Hitesh @heetezition

4 days ago

ChatGPT has gone so good. Pura apple vibes

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Hitesh @heetezition

5 days ago

Delhi women should check out safetygadgets.in

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Hitesh @heetezition

6 days ago

@ErenChenAI this is so cool

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Hitesh @heetezition

6 days ago

booth #1102

RAI Institute @rai_inst

6 days ago

Happy #IROS2026! Stop by the RAI Institute booth #1102 to grab some stickers and check out some of our custom-built robots including the UMV, Roadrunner, and Koala grippers. You can also catch Roadrunner in action in the robot demo area. Check the Exhibit Hall demo schedule for

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Hitesh @heetezition

6 days ago

IROS 2026 is ongoing. So here's the top 10 papers/posters you can try exploring.. 1) Scaling Cross-Embodiment World Models for Dexterous Manipulation Human hands + very different robot hands are represented as 3D particles, giving one world model a shared space for planning across embodiments. arxiv.org/abs/2511.01177 2) AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models A latent world model scores candidate robot actions during offline VLA post-training instead of requiring endless physical rollouts. arxiv.org/abs/2603.08519 3) OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Models Instead of forcing a VLA to learn every camera viewpoint, reconstruct the scene in 3D and render canonical views first. og-vla.github.io 4) RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation A nice middle ground between cheap 2D video models and expensive full-3D world models: use a geometry-aware 2.5D representation. arxiv.org/abs/2510.09036 5) TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation Touch tokens are activated when contact happens, instead of feeding tactile information into the VLA all the time. arxiv.org/abs/2603.12665 6) AnyCamVLA: Zero-Shot Camera Adaptation for Viewpoint-Robust VLAs Move the robot camera and the policy can break. AnyCamVLA re-renders the new observation toward the viewpoint the VLA was trained on. arxiv.org/abs/2603.05868 7) LangGap: Diagnosing and Closing the Language Gap in VLAs Some VLAs can score extremely well while barely using the instruction. LangGap keeps the scene fixed and changes the language to expose that shortcut. arxiv.org/abs/2603.00592 8) VG3S: Visual Geometry Grounded Gaussian Splatting for Semantic Occupancy Prediction Frozen vision foundation models provide geometric priors that help build better Gaussian-based 3D occupancy representations. arxiv.org/abs/2603.06210 9) EquiBim: Learning Symmetry-Equivariant Policy for Bimanual Manipulation If you mirror the scene and swap the robot’s arms, the action should mirror too. Simple physical symmetry turned into a learning constraint. arxiv.org/abs/2603.08541 10) Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies The robot does not just predict where its hand should move. It also learns what the desired tactile interaction should feel like over time. arxiv.org/abs/2604.27224