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| <?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.4">Jekyll</generator><link href="https://labicon.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://labicon.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2024-10-17T19:49:45+00:00</updated><id>https://labicon.github.io/feed.xml</id><title type="html">blank</title><subtitle>The official website for the University of California, Berkeley ICON Lab under Dr. Negar Mehr. Based on [*folio](https://github.com/bogoli/-folio) design. </subtitle><entry><title type="html">Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems</title><link href="https://labicon.github.io/blog/2024/POLICEd-CDC/" rel="alternate" type="text/html" title="Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems"/><published>2024-07-24T12:20:00+00:00</published><updated>2024-07-24T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2024/POLICEd-CDC</id><content type="html" xml:base="https://labicon.github.io/blog/2024/POLICEd-CDC/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/abs/2407.20456">Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems</a>”</em> got accepted at the 2024 Conference on Decision and Control (CDC)! Please read more about this work at <a href="https://iconlab.negarmehr.com/CDC-POLICEd-RL/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="CDC,"/><category term="Safe-RL,"/><category term="Constraint-Satisfaction,"/><category term="Black-Box,"/><category term="POLICEd-RL"/><summary type="html"><![CDATA[In Conference on Decision and Control (CDC), 2024]]></summary></entry><entry><title type="html">POLICEd RL Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints</title><link href="https://labicon.github.io/blog/2024/POLICEd-RL/" rel="alternate" type="text/html" title="POLICEd RL Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints"/><published>2024-05-15T12:20:00+00:00</published><updated>2024-05-15T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2024/POLICEd-RL</id><content type="html" xml:base="https://labicon.github.io/blog/2024/POLICEd-RL/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/abs/2403.13297">POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints</a>”</em> got accepted at the Robotics: Science and Systems (RSS) 2024! Please read more about this work at <a href="https://iconlab.negarmehr.com/POLICEd-RL/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="RSS,"/><category term="Safe-RL,"/><category term="Constraint-Satisfaction,"/><category term="Black-Box,"/><category term="POLICEd-RL"/><summary type="html"><![CDATA[In Robotics Science and Systems (RSS), 2024]]></summary></entry></feed> | ||
| <?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" xml:lang="en"><generator uri="https://jekyllrb.com/" version="4.3.4">Jekyll</generator><link href="https://labicon.github.io/feed.xml" rel="self" type="application/atom+xml"/><link href="https://labicon.github.io/" rel="alternate" type="text/html" hreflang="en"/><updated>2024-11-04T06:05:54+00:00</updated><id>https://labicon.github.io/feed.xml</id><title type="html">blank</title><subtitle>The official website for the University of California, Berkeley ICON Lab under Dr. Negar Mehr. Based on [*folio](https://github.com/bogoli/-folio) design. </subtitle><entry><title type="html">Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems</title><link href="https://labicon.github.io/blog/2024/POLICEd-CDC/" rel="alternate" type="text/html" title="Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems"/><published>2024-07-24T12:20:00+00:00</published><updated>2024-07-24T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2024/POLICEd-CDC</id><content type="html" xml:base="https://labicon.github.io/blog/2024/POLICEd-CDC/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/abs/2407.20456">Learning to Provably Satisfy High Relative Degree Constraints for Black-Box Systems</a>”</em> got accepted at the 2024 Conference on Decision and Control (CDC)! Please read more about this work at <a href="https://iconlab.negarmehr.com/CDC-POLICEd-RL/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="CDC,"/><category term="Safe-RL,"/><category term="Constraint-Satisfaction,"/><category term="Black-Box,"/><category term="POLICEd-RL"/><summary type="html"><![CDATA[In Conference on Decision and Control (CDC), 2024]]></summary></entry><entry><title type="html">POLICEd RL Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints</title><link href="https://labicon.github.io/blog/2024/POLICEd-RL/" rel="alternate" type="text/html" title="POLICEd RL Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints"/><published>2024-05-15T12:20:00+00:00</published><updated>2024-05-15T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2024/POLICEd-RL</id><content type="html" xml:base="https://labicon.github.io/blog/2024/POLICEd-RL/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/abs/2403.13297">POLICEd RL: Learning Closed-Loop Robot Control Policies with Provable Satisfaction of Hard Constraints</a>”</em> got accepted at the Robotics: Science and Systems (RSS) 2024! Please read more about this work at <a href="https://iconlab.negarmehr.com/POLICEd-RL/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="RSS,"/><category term="Safe-RL,"/><category term="Constraint-Satisfaction,"/><category term="Black-Box,"/><category term="POLICEd-RL"/><summary type="html"><![CDATA[In Robotics Science and Systems (RSS), 2024]]></summary></entry></feed> |
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