Ryan Lindeborg

Robotics research

[Ryan Lindeborg @ryanlindeborg]

Excited to be at #RSS2026 in Sydney! Come check out our work on predicting robot side effects at the Rethinking Safety workshop, and TiPToP at the FM4RoboPlan workshop!

[Jesse Zhang @Jesse_Y_Zhang]

I'm attending #RSS2026 for a couple papers/talks and as an RSS Pioneer! Please reach out to chat and/or come to the following:

- Monday 9:40 AM: Spotlight pres on detecting side effects at the Rethinking Safety Workshop by @ryanlindeborg (https://t.co/RNZR3P7owE)

- Tues 4-5PM: Come visit my RSS Pioneers Poster! @RSSPioneers

- Wed 3:15PM: Robometer Reward Fxn talk + poster after by @yigitkkorkmaz and @aliangdw (https://t.co/nd1jpgRM0C)

- Thurs 3:15PM: TMRL pre-training for post-training talk + poster after by @matthewh6_ (https://t.co/goYhXBw7TZ)

- Friday 9:30AM: Giving an invited talk at SemRob Workshop (https://t.co/aseCY9NRCj)

- Friday 5:30PM: Invited talk at the Diffusion Workshop (https://t.co/jpKl1zmTWz) on TMRL

[Ryan Lindeborg @ryanlindeborg]

TiPToP shows strong performance on the MolmoSpaces benchmark! Interesting to see how a method based on inference-time search with no robot data finetuning stacks up vs. VLAs and world action models. Nishanth has a nice, nuanced analysis on what we can take away from these results

[Nishanth Kumar @nishanthkumar23]

https://t.co/sxKin1uGk4

[Nishanth Kumar @nishanthkumar23]

State-of-the-art robot policies often need hundreds of hours of data. What if we needed none?

Introducing TiPToP: a manipulation system that zero-shots open-world tasks from pixels and language using vision foundation models and GPU-parallelized Task and Motion Planning (TAMP).

[Jesse Zhang @Jesse_Y_Zhang]

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)

[Jesse Zhang @Jesse_Y_Zhang]

Excited to have written the first blog post for USC’s new robotics blog!

[Jesse Zhang @Jesse_Y_Zhang]

Having humans annotate data to pre-train robots is expensive and time-consuming!

Introducing SPRINT:
A pre-training approach using LLMs and offline RL to equip robots w/ many language-annotated skills while minimizing human annotation effort!

URL: https://t.co/KuxuUeaXmA

🧵👇

[Lucy Shi @lucy_x_shi]

Can robots be farsighted? We introduce SkiMo (Skill + Model-based RL), which allows more accurate and efficient long-horizon planning through temporal abstraction. SkiMo learns temporally-extended, sparse-reward tasks with 5x fewer samples!

🧵👇

[Ryan Lindeborg @ryanlindeborg]

V excited for this - was just trying to get mujoco up and running on my box. Anything that can be done to streamline setup and use will have a high impact on research

[Google DeepMind @GoogleDeepMind]

We’ve acquired the MuJoCo physics simulator (https://t.co/knwXLZMr4L) and are making it free for all, to support research everywhere. MuJoCo is a fast, powerful, easy-to-use, and soon to be open-source simulation tool, designed for robotics research: https://t.co/Of3Q1W2GIR