📝Blog: https://t.co/7iJzdE4WLH
Robots are deploying faster than ever, but how we evaluate them hasn't kept up. The challenge: rigorous evaluations are costly, end-to-end foundation models are black boxes that are hard to unit-test, and our benchmarks may not line up with reality.
💪 So we put together an actionable guide. Whether you're writing a paper, prepping a demo, or working toward deployment, it offers practical frameworks, tips, and resources to:
📍 Plan your evaluation strategy (from blue-sky research to safety-critical deployment)
📍 Build confidence when resources are tight
📍 Connect your evaluations to real-world needs
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.
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A great summary of our work on AI-powered robot navigation aboard the International Space Station: https://t.co/WD1rEJmvlj
@StanfordEng@StanfordAILab@NASA
🚀 First demonstration of learning-accelerated trajectory optimization in space! Our team used a neural network to warm-start trajectory optimization for the @NASA's Astrobee free-flying robot on-board the @Space_Station, cutting solver iterations by up to 60% while maintaining safety constraints.
🎥 Video: https://t.co/t0OFR8fHBl
📄 Paper: https://t.co/ohBE1iowOB
📅 Submitted to iSpaRo 2025
With @SomritaBanerjee and @ACauligi
We won the best paper award at the AI4Space Workshop! Here's our framework for how ML models can *detect* and *adapt* to changing input distributions, using OOD detection, subsampling, and continual learning. https://t.co/29IEJYduq9 #eccv@drmapavone@StanfordASL@AerospaceCorp