A new negative-result investigation: closer-to-target (non-Gaussian) action priors did not improve the fine-tuning of Large Behavior Models (LBMs), i.e., robot policies pretrained on large, diverse datasets. Prior work found that replacing the Gaussian prior of diffusion and flow-matching policies with a closer-to-target prior substantially improves policies trained from scratch. We hypothesized that this benefit would carry over to fine-tuning pretrained LBMs. Across 100K+ simulation rollouts and 1250 hardware rollouts, we found that it does not. Website: cxu-tri.github.io/non_gaussi… Paper: arxiv.org/abs/2609.27070 🧵 (1/10)

(1/12) How should we rigorously compare robot policies? Comparison is central to robotics research, but is inherently expensive. We introduce NSCORE, a flexible, general-purpose, and data-efficient method for rigorous policy comparison. Accepted to RSS 2026.

Sample-efficient and reliable policy comparison is essential for both fast design iteration and reproducible baseline comparison, where expensive hardware eval remains the gold standard. Check out this awesome RSS work led by @das_princeton on a new, general framework!

How should we evaluate robots in the age of foundation models? We hosted the RSS RoboEval Workshop with folks from academia, industry, and policy to discuss this. 💪 We put together an actionable guide and insights to get you started on robot evaluations. Link below. 🧵1/N

Robot evaluation is an open problem, especially in the age of foundation models. Check out this blog post sharing actionable insights from our RSS Workshop last year, highlighting challenges and describing best practices. Huge thanks to @hocherie1 for leading this effort!

We extended the deadline to *June 22nd*! Submit your coolest and craziest (in-progress or completed) works on generalist robot safety 😎 Workshop co-organized with the great team: @ArpitBahety @kensukenk @imp_aa @RobobertoMM @ianabraha @LihanZha

What does it actually mean for a modern robot to be safe? As generalist robots move across tasks, environments, and users, safety must encompass many dimensions: collisions, semantic constraints, perceptions of safety, privacy, and more. Join our discussion at RSS 2026!

Excited to announce our #RSS2026 workshop: "Rethinking What It Means to be 'Safe' for Generalist Robots"! 🛡️🤖 Have new work or videos of robot safety failures? Submit by June 12! 👇

Releasing RecGen: a collaboration between @ToyotaResearch, @toyota_europe, and @UvA_Amsterdam tackling a core 3D vision challenge: reconstructing complete multi-object scenes (parts, poses, textures, even occluded geometry) from just 1 to a few RGB-D views. Trained purely on synthetic data, RecGen achieves SOTA on real-world robotics and 6D pose benchmarks, handling occlusions, symmetry, and complex interactions. A step toward scalable, high-fidelity digital twins for robotics, and better evaluation and training of generalist policies. reconstruction-by-generation…

I was thrilled to be back at @MIT for the Robotics Seminar! The talk recording is available now: Rethinking Robot Safety & Alignment in the Era of Generalist Policies piped.video/pZM8sgLAye0?si=GG7t…

A few interesting rollouts from the Foundry-QwenVLA-2.5B multi-task model on seen tasks in sim –  a 🧵. I really like behaviors that involve non-prehensile manipulation, like the little nudges in StoreCerealBoxUnderShelf.

Having control over upstream LLM/VLM training is key to training a good robotics model. We hope VLA Foundry opens the door for researchers and practitioners to answer questions they previously wouldn’t even have thought of asking if upstream pretraining was simply inherited!

TRIで最後に関わったプロジェクトである、VLA Foundryがついにリリースされました！異なる言語モデルやビジョンモデルを手軽に試せるだけでなく、Drake + Blenderを用いたシミュレーション環境で複数タスクの評価も簡単に行えます。ぜひ試してみてください！

This is hugely based on @das_princeton's implementation that came out of the collaboration between TLU tri.global/trustworthy-learn… and @Majumdar_Ani's group at Princeton out of an internship project!

Releasing VLA Foundry: an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. End-to-end control from language pretraining to action-expert fine-tuning — no more stitching together incompatible repos.

Great to see @LeRobotHF using STEP as a tool for statistically rigorous policy comparison! arxiv.org/abs/2503.10966

Congrats to the @LeRobotHF team on this remarkable contribution to the robotics community by open-sourcing "everything" including code, data, and all the valuable knowledge! Our TLU team at TRI is fortunate to have collaborated on statistical evaluation and analysis.

A really solid step toward scalable, high-quality robot data collection — Raiden, from colleagues at TRI @ZakharovSergeyN (and led by @s1wase) lowering the barrier to entry for bimanual data collection, with support for leader–follower setups and SpaceMouse teleop. Big highlight - it natively supports camera calibration and integrates TRI’s learned stereo depth model out of the box, with strong improvements over vanilla ZED SDK. If you're working on robot learning or data collection pipelines, definitely worth a look👇 tri-ml.github.io/raiden/