Deploying to gh-pages from @ db61030 🚀

Author: maulikbhatt585

assets/jupyter/blog.ipynb.html

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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="4.4.1">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>2025-11-08T23:50:27+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">DDAT Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories</title><link href="https://labicon.github.io/blog/2025/DDAT/" rel="alternate" type="text/html" title="DDAT Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories"/><published>2025-04-10T12:20:00+00:00</published><updated>2025-04-10T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2025/DDAT</id><content type="html" xml:base="https://labicon.github.io/blog/2025/DDAT/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/pdf/2502.15043.pdf">DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories</a>”</em> got accepted at the Robotics: Science and Systems (RSS) 2025! Please read more about this work at <a href="https://iconlab.negarmehr.com/DDAT/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="RSS,"/><category term="Diffusion-models,"/><category term="Robot-trajectory,"/><category term="Long-horizon-planning,"/><category term="Dynamical-feasibility,"/><category term="DDAT"/><summary type="html"><![CDATA[In Robotics Science and Systems (RSS), 2025]]></summary></entry><entry><title type="html">CurricuLLM Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models</title><link href="https://labicon.github.io/blog/2025/CurricuLLM/" rel="alternate" type="text/html" title="CurricuLLM Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models"/><published>2025-01-25T12:20:00+00:00</published><updated>2025-01-25T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2025/CurricuLLM</id><content type="html" xml:base="https://labicon.github.io/blog/2025/CurricuLLM/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/abs/2409.18382">CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models</a>”</em> got accepted at the IEEE International Conference on Robotics and Automation (ICRA) 2025! Please read more about this work at <a href="https://iconlab.negarmehr.com/CurricuLLM/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="ICRA,"/><category term="Curriculum-Learning,"/><category term="LLM-for-Robotics,"/><category term="Reinforcement-Learning,"/><category term="CurricuLLM"/><summary type="html"><![CDATA[In IEEE International Conference on Robotics and Automation (ICRA), 2025]]></summary></entry><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="4.4.1">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>2025-11-12T05:34:11+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">DDAT Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories</title><link href="https://labicon.github.io/blog/2025/DDAT/" rel="alternate" type="text/html" title="DDAT Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories"/><published>2025-04-10T12:20:00+00:00</published><updated>2025-04-10T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2025/DDAT</id><content type="html" xml:base="https://labicon.github.io/blog/2025/DDAT/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/pdf/2502.15043.pdf">DDAT: Diffusion Policies Enforcing Dynamically Admissible Robot Trajectories</a>”</em> got accepted at the Robotics: Science and Systems (RSS) 2025! Please read more about this work at <a href="https://iconlab.negarmehr.com/DDAT/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="RSS,"/><category term="Diffusion-models,"/><category term="Robot-trajectory,"/><category term="Long-horizon-planning,"/><category term="Dynamical-feasibility,"/><category term="DDAT"/><summary type="html"><![CDATA[In Robotics Science and Systems (RSS), 2025]]></summary></entry><entry><title type="html">CurricuLLM Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models</title><link href="https://labicon.github.io/blog/2025/CurricuLLM/" rel="alternate" type="text/html" title="CurricuLLM Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models"/><published>2025-01-25T12:20:00+00:00</published><updated>2025-01-25T12:20:00+00:00</updated><id>https://labicon.github.io/blog/2025/CurricuLLM</id><content type="html" xml:base="https://labicon.github.io/blog/2025/CurricuLLM/"><![CDATA[<p>Our paper titled <em>“<a href="https://arxiv.org/abs/2409.18382">CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models</a>”</em> got accepted at the IEEE International Conference on Robotics and Automation (ICRA) 2025! Please read more about this work at <a href="https://iconlab.negarmehr.com/CurricuLLM/">on its project page</a>.</p>]]></content><author><name></name></author><category term="paper-posts"/><category term="ICRA,"/><category term="Curriculum-Learning,"/><category term="LLM-for-Robotics,"/><category term="Reinforcement-Learning,"/><category term="CurricuLLM"/><summary type="html"><![CDATA[In IEEE International Conference on Robotics and Automation (ICRA), 2025]]></summary></entry><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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+layout: post
+date: 2025-10-21
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+Our paper titled _“<a href="https://arxiv.org/pdf/2410.05554">MultiNash-PF: A Particle Filtering Approach for Computing Multiple Local Generalized Nash Equilibria in Trajectory Games</a>”_ was presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025!

news/2025-10-21_IROS_MultiNashPF-presented.md

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+---
+layout: post
+date: 2025-10-21
+inline: true
+---
+
+Our paper titled _“<a href="https://arxiv.org/pdf/2410.05547">Understanding and Imitating Human-Robot Motion with Restricted Visual Fields</a>”_ was presented at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025! Please read more about this work on its <a href="https://arm.stanford.edu/HRMotion">project page</a>.