Stanford ASL

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Stanford ASL

Stanford ASL

@StanfordASL

The Autonomous Systems Lab (ASL) develops methodologies for the analysis, design, and control of autonomous systems. @Stanford

Beigetreten Nisan 2018
35 Folgt1K Follower
Stanford ASL
Stanford ASL@StanfordASL·
🔔Scalable and safe deployment of generative robot policies in the real world requires that we actively monitor their behavior and issue warnings when they are failing. Check out @agiachris and @RohanSinhaSU latest work on runtime monitoring for generative robot policies.
Christopher Agia@agiachris

In real-world deployment settings, generative robot policies are bound to encounter out-of-distribution scenarios that cause them to fail in unexpected ways. We present Sentinel, a runtime monitor that detects unknown failures of generative policies at deployment time!💂(1/6)

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Stanford ASL
Stanford ASL@StanfordASL·
💡For human-robot interaction, human preferences need to be captured at all levels of the robot planning stack: task, motion, and control! Check out Text2Interaction from Jakob and @agiachris
Christopher Agia@agiachris

How can robots incorporate human preferences into their plans? Introducing Text2Interaction: a long-horizon, skill-based planner that meets human preferences at the task, motion, and control levels zero-shot using code writing LLMs. Project site: sites.google.com/view/text2inte…

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Rohan Sinha
Rohan Sinha@RohanSinhaSU·
❓ How can we enable real-time reactive control with LLMs for dynamic robotic systems? At #RSS2024 we present AESOP: A 2-stage (🐢🐇) reasoning framework that uses LLMs to increase closed-loop robot trustworthiness in OOD scenarios! Site: tinyurl.com/aesop-llm 🧵(1/6)
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Daniele Gammelli
Daniele Gammelli@DanieleGammelli·
Can we leverage Transformer models to boost trajectory generation for spacecraft rendezvous? In our recent @IEEEAeroConf paper, we introduce ART🎨(Autonomous Rendezvous Transformer) to solve complex trajectory optimization problems. Website🌐rendezvoustransformer.github.io A thread 👇
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Amine Elhafsi
Amine Elhafsi@AmineElhafsi·
🔍 How can we detect system-level reasoning failures to improve the robustness of robotic systems in safety-critical settings? We use LLMs as intelligent runtime monitors to reason over and identify potentially problematic elements in a scene! 🧠 tinyurl.com/llm-anomaly
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Rohan Sinha
Rohan Sinha@RohanSinhaSU·
📢 Announcing the first @corl_conf workshop on Out-of-Distribution Generalization in Robotics: Towards Reliable Learning-based Autonomy! #CoRL2023 🎯 How can we build reliable robotic autonomy for the real world? 📅 Short papers due 10/6/23 🌐 tinyurl.com/corl23ood 🧵(1/4)
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Daniele Gammelli
Daniele Gammelli@DanieleGammelli·
Excited to share that our paper on Graph-Reinforcement Learning was accepted at #ICML2023! We present a broadly applicable approach to solve graph-structured MDPs through the combination of RL and classical optimization. Website: sites.google.com/stanford.edu/g… 🧵👇(1/n)+quoted tweet
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Daniele Gammelli@DanieleGammelli

Can we learn efficient algorithms to solve classical optimization problems over graphs? In our recent @LogConference paper, we propose graph reinforcement learning as a general framework to solve network control problems! 📜 openreview.net/forum?id=1sPcf… 🧵👇 (1/n)

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Rohan Sinha
Rohan Sinha@RohanSinhaSU·
Out-of-distribution inputs derail predictions of ML models. How can we cope with OOD data in robotics? How do we even define what makes data OOD? We provide a perspective paper arguing a system-level view of OOD data in robotics! 🧵 (1/5) Now on Arxiv: arxiv.org/abs/2212.14020
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Daniele Gammelli
Daniele Gammelli@DanieleGammelli·
Can we learn efficient algorithms to solve classical optimization problems over graphs? In our recent @LogConference paper, we propose graph reinforcement learning as a general framework to solve network control problems! 📜 openreview.net/forum?id=1sPcf… 🧵👇 (1/n)
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James Harrison
James Harrison@jmes_harrison·
New on arXiv: we present a learning control approach capable of safe and efficient online adaptation. Our approach combines elements of classical adaptive control, modern robust MPC, and Bayesian meta-learning to yield guaranteed-safe online adaptation! arxiv.org/abs/2212.01371 🧵
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Navid Azizan
Navid Azizan@NavidAzizan·
Can we verify the safety of a deep neural network for deployment in safety-critical settings? This is a non-convex problem in general, and there have been many existing relaxations constructed for it. 1/2
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Stanford ASL
Stanford ASL@StanfordASL·
Can we leverage tools from statistical inference to build safety critical warning systems with a guaranteed ε false negative rate using as few as 1/ε data points? Check out our new paper on sample-efficient safety assurances using conformal prediction! arxiv.org/abs/2109.14082…
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Boris Ivanovic
Boris Ivanovic@iamborisi·
Kicking off the new year with a paper accepted to #ICRA2022! In it, we propagate perceptual state uncertainty through trajectory forecasting, making use of a new statistical distance-based loss formulation to do so. Check it out on arXiv: arxiv.org/abs/2110.03267. See you in May!
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Entdecken

@agiachris @RohanSinhaSU @IEEEAeroConf @corl_conf @drmapavone @spenMrich @StanfordEng @LogConference