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.

RT @abhishekunique7: So you’ve trained your favorite diffusion/flow based policy, but it’s just not good enough 0-shot. Worry not, in our n….

RT @yunchuzh: How should a robot perceive the world? What kind of visual representation leads to robust visuomotor policy learning for robo….

RT @rui_xin31: Think PII scrubbing ensures privacy? 🤔Think again‼️ In our paper, for the first time on unstructured text, we show that you….

Some more 'cherry' picked field test adventures with Spot! :).

RT @abhishekunique7: Constructing interactive simulated worlds has been a challenging problem, requiring considerable manual effort for ass….

RT @avibose22: 🧠 Your LLM should model how you think, not reduce you to preassigned traits.📢 Introducing LoRe: a low-rank reward modeling f….

RT @kjha02: Our new paper (first one of my PhD!) on cooperative AI reveals a surprising insight: Environment Diversity > Partner Diversity.….

RT @ChongZitaZhang: Awesome. Going beyond the stupid metric maps is exactly the next steps in field robotics.

RT @khimya: Need yo robot to navigate in outdoor environments with limited-horizon knowledge? Check out our work ~Long Range Navigator (LRN….

RT @kabirahuja004: 📢 New Paper!. Tired 😴 of reasoning benchmarks full of math & code? In our work we consider the problem of reasoning for….

We had a lot of fun field testing our work with the awesome Spot robot from @BostonDynamics! It was really hard to make the robot fail and we were really impressed by the robustness! . Spot also had some fun encounters with other machines :)

This project was a fun effort with Matt Schmittle, Nathan Hatch, Rosario Scalise, @mateoguaman, @sidharthtalia , @khimya, @siddhss5, Byron Boots.

This work is a collaboration between the Personal Robotics Lab (@siddhss5) and Robot Learning Lab (Byron Boots) at the University of Washington @uw @uwcse.

🤖 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.

🔥 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.

❗️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.

RT @chuning_zhu: Scaling imitation learning has been bottlenecked by the need for high-quality robot data, which are expensive to collect.….

RT @anshulnasery: Model merging is a great way to combine multiple models' abilities, however, existing methods only work with models fine-….

RT @esfrankel: Want to quickly sample high-quality images from diffusion models, but can’t afford the time or compute to distill them? Intr….