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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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    <image:image>
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      <image:title>Reachability Decomposition</image:title>
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    <image:image>
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      <image:title>Reachability Decomposition</image:title>
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    <image:image>
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      <image:title>Reachability Decomposition</image:title>
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    <image:image>
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      <image:title>Reachability Decomposition</image:title>
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      <image:title>Reachability Decomposition</image:title>
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    <lastmod>2026-03-25</lastmod>
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      <image:title>Hamilton-Jacobi Reachability Theory</image:title>
      <image:caption>An animation of how we produce a reachable set. First the goal (or unsafe set) is defined, shown here as the blue circle. We then create a level set function that is a signed distance function: negative inside the set and positive outside. We propagate this function using HJ reachability for the desired time horizon. Finally, we take the zero slice of this function to produce the reachable set</image:caption>
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    <lastmod>2021-12-14</lastmod>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
      <image:caption>A nonlinear, ten-dimensional quadrotor simulation navigating an obstacle course in real-time with guaranteed safety.  The planner is a 3D RRT planner with a tracking error bound (think safety bubble) based on the speed of the planner.  Note that the planner mode changes throughout the course to trade off speed with maneuverability.  As the planner senses its environment the local obstacles turn red and the planner replans.</image:caption>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
      <image:caption>On the left we have a high-dimensional vehicle moving through an obstacle course to a goal. Computing the optimal safe trajectory is a slow and sometimes intractable task, and replanning is nearly impossible.  In the center we simplify our model of the vehicle (in this case assuming it can move in straight lines connected at points).  This allows us to plan very quickly, but when we execute the planned trajectory we may find that we cannot actually follow the path exactly, and end up crashing. On the right we have our proposal: plan using the simplified model, but precompute a tracking error bound that captures all potential deviations of the trajectory due to model mismatch.</image:caption>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
      <image:caption>The value function initializes at the cost function (normed distance to the origin) and evolves over time to convergence</image:caption>
    </image:image>
    <image:image>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
      <image:caption>An example of a 3D set at the slice V = 0.75 meters of the value function. We can think of this as a "candidate" tracking error bound.  As the set evolves we see the initial states we must start in (purple) in order to stay within the desired bound (blue)</image:caption>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
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      <image:title>FaSTrack: Fast and Safe Tracking</image:title>
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    <lastmod>2019-06-20</lastmod>
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      <image:title>Confidence-Aware Pedestrian Prediction</image:title>
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      <image:title>Confidence-Aware Pedestrian Prediction</image:title>
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      <image:title>Confidence-Aware Pedestrian Prediction</image:title>
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      <image:title>Confidence-Aware Pedestrian Prediction</image:title>
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      <image:title>People - Ethan Foss, MS (PhD student, Stanford University)</image:title>
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      <image:title>People - Make it stand out</image:title>
      <image:caption>Whatever it is, the way you tell your story online can make all the difference.</image:caption>
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  <url>
    <loc>http://sylviaherbert.com/safety-applications</loc>
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    <lastmod>2020-10-12</lastmod>
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      <image:title>Safety Applications</image:title>
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    <image:image>
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      <image:title>Safety Applications</image:title>
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    <image:image>
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      <image:title>Safety Applications</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1602534740332-HMV2FKVPIH8KVM50MCZQ/biological+systems.png</image:loc>
      <image:title>Safety Applications</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1602534749877-UTMARNH5QXY6RSLZ5APY/manufacturing.png</image:loc>
      <image:title>Safety Applications</image:title>
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    <image:image>
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      <image:title>Safety Applications</image:title>
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    <image:image>
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      <image:title>Safety Applications</image:title>
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    <image:image>
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      <image:title>Safety Applications</image:title>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1602534760621-BH7FEHAXHUZC7H2T9JQ4/skydio.png</image:loc>
      <image:title>Safety Applications</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1602534741702-GSG6902GF5IQ0HEK7JXP/medical+devices.png</image:loc>
      <image:title>Safety Applications</image:title>
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  <url>
    <loc>http://sylviaherbert.com/safe-learning-via-warmstarting</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2023-01-12</lastmod>
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  <url>
    <loc>http://sylviaherbert.com/fellowships</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2020-11-18</lastmod>
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  <url>
    <loc>http://sylviaherbert.com/calendar</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2023-08-25</lastmod>
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  <url>
    <loc>http://sylviaherbert.com/daniel-maldonadonaranjo</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2023-07-11</lastmod>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1665442949315-ELDAZE2G0AJ0SXRELJA9/daniel.jpg</image:loc>
      <image:title>Daniel Maldonado-Naranjo</image:title>
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  <url>
    <loc>http://sylviaherbert.com/nikhil</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2025-08-15</lastmod>
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      <image:title>Nikhil Shinde</image:title>
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    <image:image>
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      <image:title>Nikhil Shinde - Make it stand out</image:title>
      <image:caption>Selecting actions for a surgical robot to both infer tissue structure (e.g. sutured regions) while maintaining safety (without damaging the tissue)</image:caption>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/d11275a5-ff81-40da-a206-244f653221bd/Screenshot+2025-03-12+at+11.00.07%E2%80%AFAM.png</image:loc>
      <image:title>Nikhil Shinde - Make it stand out</image:title>
      <image:caption>Safe online learning for interaction: composing object-centric Gaussian process regression to interact with objects safely despite uncertainty in the interaction</image:caption>
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  <url>
    <loc>http://sylviaherbert.com/sander</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2025-08-15</lastmod>
    <image:image>
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      <image:title>Sander Tonkens</image:title>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/c176118d-d7f9-4fa5-a87f-ffa984c60626/Screenshot+2025-03-12+at+11.06.23%E2%80%AFAM.png</image:loc>
      <image:title>Sander Tonkens - Make it stand out</image:title>
      <image:caption>Refining Data-Driven Control Barrier Functions: An approximate control barrier function and its resulting safe set (visualized above) can be refined based on new information or knowledge of the system. Each iteration improves safety and reduces conservativeness.</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/0d139d81-7e3a-4bd7-9cb3-9d8239f86060/Screenshot+2025-03-12+at+11.06.29%E2%80%AFAM.jpg</image:loc>
      <image:title>Sander Tonkens - Make it stand out</image:title>
      <image:caption>Contingency planning: the policy planner (blue AV) implicitly nudges a non-controlled AV (red) to change lanes upon detecting the blocked lane (scenario A), which can be viewed as the cooperative strategy. However, the decision tree retains the ability to yield at a later time once the red agent is identified as being non-cooperative (scenario C)</image:caption>
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  <url>
    <loc>http://sylviaherbert.com/chong-he</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2024-07-08</lastmod>
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      <image:title>Chong He</image:title>
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  <url>
    <loc>http://sylviaherbert.com/clvf</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2022-12-19</lastmod>
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  <url>
    <loc>http://sylviaherbert.com/zheng-gong</loc>
    <changefreq>daily</changefreq>
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    <lastmod>2025-03-12</lastmod>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/5e767005-5812-4229-8e5e-a16bbc400a88/zheng.jpeg</image:loc>
      <image:title>Zheng Gong</image:title>
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    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/df067509-34db-4393-8e5e-78ef7e8e0589/Screenshot+2025-03-12+at+11.10.13%E2%80%AFAM.png</image:loc>
      <image:title>Zheng Gong - Make it stand out</image:title>
      <image:caption>Control Lyapunov Value Functions: A constructive and general method for producing control Lyapunov-like functions that stabilize to an equilibrium (or control invariant set) at a user-defined rate</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/0628b9ca-7c5e-4420-a4a5-5d7c8dead7fe/Screenshot+2025-03-12+at+11.09.33%E2%80%AFAM.png</image:loc>
      <image:title>Zheng Gong - Make it stand out</image:title>
      <image:caption>Decomposition (+ Composition) of Value Functions: Decomposing coupled dynamic models to generate smaller value functions in each subspace that can be composed to find the backward reachable set (BRS) for a goal or an obstacle</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/9c758eb0-52e1-407b-9fb3-207eef6e182e/Screenshot+2025-03-12+at+11.09.38%E2%80%AFAM.png</image:loc>
      <image:title>Zheng Gong - Make it stand out</image:title>
      <image:caption>Safety in Reinforcement Learning: Improving the optimization procedure for reinforcement learning with hard and soft safety constraints using reachability estimation</image:caption>
    </image:image>
  </url>
  <url>
    <loc>http://sylviaherbert.com/boyang</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2025-03-12</lastmod>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1665440843778-74MTCMB64WGC044WA4JH/boyang.jpeg</image:loc>
      <image:title>Boyang Li</image:title>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/93c7ef19-b327-4720-a97f-fb59656cbfc9/Screenshot+2025-03-12+at+11.17.08%E2%80%AFAM.png</image:loc>
      <image:title>Boyang Li - Make it stand out</image:title>
      <image:caption>Value Functions for Robust Control Invariance: Analysis between methods from Hamilton-Jacobi reachability analysis and control barrier functions to generate value functions that enforce control invariance</image:caption>
    </image:image>
    <image:image>
      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/7094b0f5-f4c0-4396-9548-1d4115f6ad57/Screenshot+2025-03-12+at+11.17.22%E2%80%AFAM.png</image:loc>
      <image:title>Boyang Li - Make it stand out</image:title>
      <image:caption>Safe &amp; stabilizing online control: A framework wherein a nonlinear robotic system tracks a simplified planning model via a control Lyapunov-like function that ensures stabilization to the planned path despite sudden real or artificial disturbances</image:caption>
    </image:image>
  </url>
  <url>
    <loc>http://sylviaherbert.com/alex-toofanian</loc>
    <changefreq>daily</changefreq>
    <priority>0.75</priority>
    <lastmod>2023-07-11</lastmod>
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      <image:loc>https://images.squarespace-cdn.com/content/v1/5164717ee4b09befa7e93fbf/1665442740109-5OWDYYJJ8F96S3JD45E2/Alex.jpg</image:loc>
      <image:title>Alex Toofanian</image:title>
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  <url>
    <loc>http://sylviaherbert.com/matthew-kim</loc>
    <changefreq>daily</changefreq>
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      <image:caption>High-Dimensional Physics-Informed Machine Learning: Advancing methods for semi-supervised learning the solution to differential games using physics-informed neural networks (PINNs). On the left is a 50D nonlinear problem (top row is the baseline, bottom rows are our method). On the right is a quadrotor experiment with 99.9% probabilistic safety guarantees.</image:caption>
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      <image:caption>Efficient Linear Reachability with Guarantees for Nonlinear Dynamics: A method to linearize a nonlinear control system wherein the linearization error is treated as an adversarial disturbance in Hopf reachability analysis to provide guaranteed conservative (green) sets to reach a goal or avoid an obstacle</image:caption>
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      <image:caption>Event-triggered learning for safe control: This framework uses event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model.</image:caption>
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      <image:caption>Safe reinforcement learning with reach-avoid safety filters: Using reach-avoid sets to minimally override the RL process while ensuring the system (a) maintains safety and (b) returns to its base configuration at the end of the horizon.</image:caption>
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      <image:caption>Reduced order modeling for differential games: Decomposing “fast” and “slow” dynamics for scalable computation of high-dimensional nonlinear systems, e.g. large-scale biological systems</image:caption>
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      <image:caption>Non-anticipative strategies in differential games: An analysis of how different strategies affect guarantees in Hamilton-Jacobi reachability analysis</image:caption>
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      <image:caption>An animation of how we produce a reachable set. First the goal (or unsafe set) is defined, shown here as the blue circle. We then create a level set function that is a signed distance function: negative inside the set and positive outside. We propagate this function using HJ reachability for the desired time horizon. Finally, we take the zero slice of this function to produce the reachable set</image:caption>
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    <priority>0.75</priority>
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    <changefreq>daily</changefreq>
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    <changefreq>daily</changefreq>
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    <loc>http://sylviaherbert.com/robustness-and-dynamic-games-1</loc>
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    <lastmod>2026-03-25</lastmod>
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