Rohan Baijal

A Boston Dynamics robot dog stands on a walkway through grass at the base of large cherry trees full of blooms

Close-up of the chest and head of a Boston Dynamics robot dog framed by trees full of cherry blossoms

Three smiling researchers dressed in casual clothing, one pulling a wagon, walk behind a Boston Dynamics robot dog with campus buildings and trees in the background

A researcher holding a handheld controller follows a Boston Dynamics robot dog down a building ramp while another researcher standing a few meters away holding a laptop looks on

If you visited the @uwcherryblossom.bsky.social, did you “spot” an unusual visitor among the blooms? Researchers in the @uofwa.bsky.social #UWAllen #robotics group recently took advantage of some nice weather to take our Boston Dynamics robot dog for a stroll around campus. #AI 1/5

02.05.2025 23:28 — 👍 11    🔁 4    💬 2    📌 1

Thank you!

Yea we also went through a lot of the papers that tried to do long range perception for the LAGR project.

Really cool to take inspiration from works almost 20 years old but still very relevant :)

18.04.2025 18:33 — 👍 1    🔁 0    💬 0    📌 0

This project was a fun effort with Matt Schmittle, Nathan Hatch, Rosario Scalise, @mateoguaman.bsky.social, Sidharth Talia, @khimya.bsky.social, @siddhss5.bsky.social and Byron Boots.

🧵6/6

18.04.2025 17:56 — 👍 1    🔁 0    💬 0    📌 0

This work is a collaboration between the Personal Robotics Lab (@siddhss5.bsky.social) and Robot Learning Lab at the University of Washington @uwrobotics.bsky.social @uwcse.bsky.social

🧵5/6

18.04.2025 17:56 — 👍 1    🔁 0    💬 1    📌 0

🤖 Real-world tested: LRN cuts down interventions on Spot and a large tracked vehicle.

✅ Plug & play: Works with nearly any local stack that accepts goal waypoints.

🔄 Auto-labeled: Trained from raw FPV videos using CoTracker to trace camera paths.

🧵4/6

18.04.2025 17:56 — 👍 1    🔁 0    💬 1    📌 0

🔥 Key insight: Robots can reason further by learning to identify distant affordable frontiers as intermediate goals.

🧠 How it works: It uses a pre-trained SAM2 backbone + small head to find frontiers in images. Given a goal, it selects the highest-scoring one to navigate to.

🧵3/6

18.04.2025 17:56 — 👍 1    🔁 0    💬 1    📌 0

❗️Problem: Robots navigating with no prior maps relying only on local sensors have a limited mapping range (due to sparse/noisy depth) causing myopic decisions.

🧵2/6

18.04.2025 17:56 — 👍 1    🔁 0    💬 1    📌 0

Long Range Navigator (LRN) 🧭— an approach to extend planning horizons for off-road navigation given no prior maps. Using vision LRN makes longer-range decisions by spotting navigation frontiers far beyond the range of metric maps.
personalrobotics.github.io/lrn/

🧵1/6

18.04.2025 17:56 — 👍 3    🔁 4    💬 1    📌 3

Excited to attend the talk!

11.01.2025 23:01 — 👍 2    🔁 0    💬 0    📌 0

Sweet!! 🙌🤩

26.12.2024 13:47 — 👍 3    🔁 0    💬 0    📌 0

Happy holidays from UW Robotics!

24.12.2024 18:15 — 👍 11    🔁 2    💬 0    📌 0

Q&A: New AI training method lets systems better adjust to users’ values University of Washington researchers created a method for training AI systems — both for large language models like ChatGPT and for robots — that can better reflect users’ diverse values. It...

UW News put out a Q&A about our recent work on Variational Preference Learning, a technique for personalizing Reinforcement Learning from Human Feedback (RLHF) washington.edu/news/2024/12...

18.12.2024 21:51 — 👍 30    🔁 8    💬 1    📌 0