Sudharshan Suresh

I'm a featured interview in our latest behind-the-scenes release! We break down the ML and perception that drives the whole-body manipulation behaviors from last year. It starts with a neat demo of Atlas's range-of-motion and our vision foundation models. youtu.be/oe1dke3Cf7I?si…

Most world models for robot manipulation learn physics from pixels. But pixels don’t see it all. Can we ground these models in the "feeling" of contact to disambiguate visually identical states? Visuo-Tactile World Models (VT-WM): robot imagination in a shared space👇

Check out UMI-FT, the newest member of the UMI family! 🐣 We integrated a coin-sized🪙, low-cost, 6-axis force-torque sensor behind each UMI Fin Ray finger. The sensor is physically compact, mechanically robust, and extremely easy-to-calibrate against ATI -- directly measures physically meaningful force/torque outputs (N/Nm). UMI-FT enables in-the-wild robot learning with force-aware manipulation data, without needing in-the-wild robots or relying on traditional, bulky, thousands-dollar F/T sensors! A cross-paradigm bonus: because the coinFT sensor outputs physically meaningful force/torque values (N/Nm), you can directly run standard admittance control to get F/T-aware compliant robot fingers -- no learning required, immediately useful! Checkout @Hojung_Choi_' post for deets!

It's been amazing to work with @HaozhiQ and I've learnt a lot from him, excited for his new lab!

@luisenp Really sorry to hear Luis, working with you was so meaningful for my PhD work. Hope you are doing alright!

After 7 years at FAIR, I've been affected by the recent AI layoffs. If you are interested in robotics learning, let's chat :)

@gabe_mrgl Really cool work Gabe!

Excited to share SoftMimic -- a new approach for learning compliant humanoid policies that interact gently with the world.

@mangahomanga @JohnsHopkins @JHUCompSci Congrats Homanga!!

I'll be joining the faculty @JohnsHopkins late next year as a tenure-track assistant professor in @JHUCompSci Looking for PhD students to join me tackling fun problems in robot manipulation, learning from human data, understanding+predicting physical interactions, and beyond!

Same for ego data, UMI data, etc. An open secret is “we used ego4d” actually means filtering out the 1% of videos that are vaguely useful for learning. Occlusions, suboptimality, sensor noise, and so many pitfalls! Modeling + collect co-design is only gold standard currently.

I'm super excited to announce mjlab today! mjlab = Isaac Lab's APIs + best-in-class MuJoCo physics + massively parallel GPU acceleration Built directly on MuJoCo Warp with the abstractions you love.

I'll be giving a talk @CoRL2025 about developing large behavior models on Atlas. Come by the dexterous manipulation workshop at 11:30 to see the talk, dex-manipulation.github.io/corl2025/

@JasonMa2020 Congrats Jason! Awesome to see Dyna grow.

We have raised $120M to accelerate our mission of building and delivering high-performance general-purpose robots to the physical world. Within one year, we have made research breakthroughs, showing that it is possible to achieve real-world reliability with large VLAs, and demonstrated commercial and deployment traction, with our DYNA-1 models running live in sites at SF, LA, and Sacramento. This is just the beginning, and I never felt more optimistic about a future where AI-powered robots can positively impact human productivity. From when I started PhD where robot policies barely worked even in highly controlled settings to now deploying DYNA robots with the confidence of out-of-box model performance, when I think about the trajectory of robotics, it’s astonishing how quickly we’ve gone from if it works in the lab to it works in the world. The next frontier isn’t about proving robots can move—it’s about proving they can reliably help in real-world environments, at scale, across industries. That’s what we’re building at @DynaRobotics The impact of this shift will be massive: - Unlocking productivity across logistics, manufacturing, and beyond. - Expanding what small teams and businesses can achieve. - Freeing humans to focus on higher-level creativity, problem-solving, and connection. The mission is bigger than any single deployment. It’s about ushering in an era where general-purpose robots are as ubiquitous and trusted as computers or smartphones. We’re just getting started, and I couldn’t be more excited for what’s ahead. Join us!🚀🤖

Can we scale up mobile manipulation with egocentric human data? Meet EMMA: Egocentric Mobile MAnipulation EMMA learns from human mobile manipulation + static robot data — no mobile teleop needed! EMMA generalizes to new scenes and scales strongly with added human data. 1/9

Lucas and co. wrote a great blogpost on the careful science and engineering behind language-conditioned policies for whole-body manipulation! There's a lot more work on the horizon; our team is hiring researchers to scale egocentric human data and VLMs for robotics. Reach out!

I had a great time presenting at the workshop - Check out my talk and the other amazing panelists here! x.com/HaozhiQ/status…

I'll be speaking at the RSS Dexterous Manipulation Workshop tomorrow, discussing our recent work with Atlas!

Viser is such an amazing tool for the community - congrats to @brenthyi and co!

TRI's latest Large Behavior Model (LBM) paper landed on arxiv last night! Check out our project website: toyotaresearchinstitute.github.io/lbm1/ One of our main goals for this paper was to put out a very careful and thorough study on the topic to help people understand the state of the technology, and to share a lot of details for how we're achieving it. youtube.com/watch?v=BEXFnr…

Current robot policies often face a tradeoff: they're either precise (but brittle) or generalizable (but imprecise). We present ViTaL, a framework that lets robots generalize precise, contact-rich manipulation skills across unseen environments with millimeter-level precision. 🧵

Today we're excited to share a glimpse of what we're building at Generalist. As a first step towards our mission of making general-purpose robots a reality, we're pushing the frontiers of what end-to-end AI models can achieve in the real world. Here's a preview of our early results in autonomous general-purpose dexterous capabilities – fast, reactive, smooth, precise, bi-manual coordinated sensorimotor control.

How to learn dexterous manipulation for any robot hand from a single human demonstration? Check out DexMachina, our new RL algorithm that learns long-horizon, bimanual dexterous policies for a variety of dexterous hands, articulated objects, and complex motions.