Intelligent Robotics and Vision Lab @ UTDallas

Jishnu Jaykumar Padalunkal@jishnu_jaykumar

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Sai Haneesh Allu@saihaneesh_allu·5d

Excited to show our HRT1 framework live today at #ECSAIDays2026. If you're curious how robots mimic human actions, especially outside controlled setups, we’ll be running real demos and sharing some of the practical challenges behind it. Visit us at: 📍 ECS West 2.100, UT Dallas 🗓️ April 30 from 11 AM @UT_Dallas @IRVLUTD

Stepping out of the lab and into the real world. Today at #ECSAIDays2026, we’ll be presenting a live demo of #HRT1 — a system that transfers human demonstrations to robot actions for mobile manipulation. 🔗 irvlutd.github.io/HRT1/ This short timelapse is a behind-the-scenes glimpse of what it actually takes. From moving the robot across campus, navigating buildings, setting up hardware, testing repeatedly, to making everything work outside controlled environments — a lot goes into what eventually looks like a “simple demo.” Especially in smaller labs, it’s all hands-on, end-to-end effort. Also, when it’s just two of us running a two-person setup and trying to film it, the camera doesn’t always cooperate 😄 — but the live demo will be much more fun. Grateful to be building this with @saihaneesh_allu at the @IRVLUTD If you’re around, drop by and see it live. 📍 ECSW, UT Dallas ⏳ Apr 30, 2026 Thanks to @Sriraam_UTD, @VibhavGogate, @YuXiang_IRVL and Tyler Summers for the opportunity and support. @UT_Dallas @UTDCompSci @UTDResearch

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Jishnu Jaykumar Padalunkal@jishnu_jaykumar·5d

Stepping out of the lab and into the real world. Today at #ECSAIDays2026, we’ll be presenting a live demo of #HRT1 — a system that transfers human demonstrations to robot actions for mobile manipulation. 🔗 irvlutd.github.io/HRT1/ This short timelapse is a behind-the-scenes glimpse of what it actually takes. From moving the robot across campus, navigating buildings, setting up hardware, testing repeatedly, to making everything work outside controlled environments — a lot goes into what eventually looks like a “simple demo.” Especially in smaller labs, it’s all hands-on, end-to-end effort. Also, when it’s just two of us running a two-person setup and trying to film it, the camera doesn’t always cooperate 😄 — but the live demo will be much more fun. Grateful to be building this with @saihaneesh_allu at the @IRVLUTD If you’re around, drop by and see it live. 📍 ECSW, UT Dallas ⏳ Apr 30, 2026 Thanks to @Sriraam_UTD, @VibhavGogate, @YuXiang_IRVL and Tyler Summers for the opportunity and support. @UT_Dallas @UTDCompSci @UTDResearch

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Jishnu Jaykumar Padalunkal@jishnu_jaykumar·22 Nis

🛠️ While working on #iTeach (lnkd.in/gj9s2eJR), where unseen object instance segmentation (UOIS) was a key task, I kept hitting the same wall: every UOIS dataset had its own loader and quirks. Getting data ready for training meant writing custom glue for each — time that should have gone into the models themselves. ✨ So I put together uois_toolkit — a small PyTorch library that wraps 5 popular UOIS datasets (Tabletop, OCID, OSD, Robot Pushing, iTeach-HumanPlay) behind one API. 🚀 Features: 📦 Load any dataset in 3 lines 📊 Compute F1, IoU, Precision, and Recall with a single call ⚡ Plug directly into PyTorch Lightning 🤖 Works out of the box with robotics pipelines 💡 The idea is simple — take the friction out of data pipelines so more time can go into model building. 🎉 It has picked up around 2K downloads on PyPI since release, which was a nice surprise. 🙏 Sharing it a bit more openly now in case others find it helpful. ❤️ Huge thanks to the original dataset authors, whose open codebases made this possible. And to Avaya Aggarwal and Animesh Maheshwari for testing and feedback along the way. 🔗 GitHub: lnkd.in/grPU7rx5 📥 PyPI: lnkd.in/gugFzGWr @IRVLUTD @UT_Dallas #Robotics #RobotPerception #ComputerVision #PyTorch #Lightning #OpenSource

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Yu Xiang@YuXiang_IRVL·18 Nis

Great to have @Jesse_Y_Zhang visiting us @IRVLUTD today! He shared his journey toward generalist robotics reward models (RoboCLIP, ReWiND, Robometer), followed by a great buffet with the lab.

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Jishnu Jaykumar Padalunkal@jishnu_jaykumar·16 Nis

🤖 Robots don't fail in the lab. They fail in the wild — clutter, occlusion, constantly changing environments. The real question: Can robots learn directly from these failures during deployment? How about teaching robots the way we'd teach a child — by showing them where they went wrong? 🧵👇

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Jishnu Jaykumar Padalunkal@jishnu_jaykumar·7 Nis

✨Honored to receive the Louis Beecherl, Jr. Graduate #Fellowship (2026–2027) from the Erik Jonsson School of Engineering and Computer Science at 🟠@UT_Dallas 🙌Grateful for the support as I continue my #PhD research in #RobotPerception and #Learning at the @IRVLUTD | @UTDResearch

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Jishnu Jaykumar Padalunkal@jishnu_jaykumar·4 Nis

A day well spent with @IRVLUTD at #TEROS26! Presented #HRT1, and had some great idea exchanges with fellow roboticists 🤖✨

A group photo in the end @IRVLUTD

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Yu Xiang@YuXiang_IRVL·4 Nis

A group photo in the end @IRVLUTD

Attending the Texas Regional Robotics Symposium at UT Arlington today!

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Yu Xiang@YuXiang_IRVL·9 Mar

We noticed that DINOv3 was surprisingly strong at matching object features. This inspired L2G (Local Matches to Global Masks). With a few reference images, a robot can search a room for the target object. 🔗 Project: irvlutd.github.io/L2G/ 💻 Code: github.com/IRVLUTD/L2G

Jesse Zhang@Jesse_Y_Zhang

DINOv3 seems very good at matching objects across environments as well

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Intelligent Robotics and Vision Lab @ UTDallas@IRVLUTD·3 Mar

We’re excited to see Robometer out! A pleasure to collaborate on scaling general-purpose robotic reward models to 1M+ trajectories. Looking forward to seeing how the community builds on this!

A reward model that works, zero-shot, across robots, tasks, and scenes? Introducing Robometer: Scaling general-purpose robotic reward models with 1M+ trajectories. Enables zero-shot: online/offline/model-based RL, data retrieval + IL, automatic failure detection, and more! 🧵 (1/12)

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Luis Felipe Casas@lfcasas7

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Intelligent Robotics and Vision Lab @ UTDallas@IRVLUTD·17 Şub

Congratulations to Felipe!!

Thrilled to have won the Louis Beecherl, Jr. Graduate Fellowship for 2025–2026 from the Erik Jonsson School at @UT_Dallas ! This prestigious merit-based award has been a game-changer for my PhD in Computer Science and research at the Intelligent Robotics and Vision Lab—real financial support and recognition. Grateful to the committee! Current/future UTD grad students: Apply for the 2026-2027 Graduate fellowships! It eases the journey and rewards excellence. Don't miss out. 🚀 #UTDallas #Fellowship

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Intelligent Robotics and Vision Lab @ UTDallas@IRVLUTD·2 Ara

An excellent resource for ROS 2 beginners!

If you want to learn ROS2 with MoveIt2 and Gazebo simulation, check out the five homework in my robotics course: labs.utdallas.edu/irvl/courses/f… By the end, you'll have a Fetch robot picking up a cracker box. Everything runs in Docker on Windows or Mac. #robotics #ROS2

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Intelligent Robotics and Vision Lab @ UTDallas@IRVLUTD·27 Kas

We celebrated Thanksgiving early at an Asian buffet last week. Happy Thanksgiving everyone! 🦃

Intelligent Robotics and Vision Lab @ UTDallas tweet media

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Jishnu Jaykumar Padalunkal@jishnu_jaykumar·21 Kas

After ~2 years in stealth, I’m making #Robokit public. 🔗 github.com/jishnujayakuma… A small toolkit that ended up supporting many of @IRVLUTD's research. Supports: CLIP, GroundingDINO, MobileSAM, DepthAnything, FeatUp, SAMv2. Coming soon: SAM3, SAM3D + more FM tools. #Robotics

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Intelligent Robotics and Vision Lab @ UTDallas@IRVLUTD·10 Kas

(11/11) #HRT1 shows that robots don't always need huge training pipelines. With the right system, a single human demo goes a long way. One demo → mobile manipulation → real-world execution. @saihaneesh_allu @jishnu_jaykumar @YuXiang_IRVL

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Intelligent Robotics and Vision Lab @ UTDallas@IRVLUTD·10 Kas

(10/11) The standout aspect: No Training Needed: Full mobile manipulation with no learned policy — one shot, no RL, no finetuning. One human demo → real robot execution. And while most progress is on tabletop tasks, #HRT1 shows how far unified mobility + manipulation can go.

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