Added twtlplan project

content/project/example/index.md

@@ -1,41 +0,0 @@
----
-title: Example Project
-summary: An example of using the in-built project page.
-tags:
-- Deep Learning
-date: "2016-04-27T00:00:00Z"
-
-# Optional external URL for project (replaces project detail page).
-external_link: ""
-
-image:
-  caption: Photo by rawpixel on Unsplash
-  focal_point: Smart
-
-links:
-- icon: twitter
-  icon_pack: fab
-  name: Follow
-  url: https://twitter.com/georgecushen
-url_code: ""
-url_pdf: ""
-url_slides: ""
-url_video: ""
-
-# Slides (optional).
-#   Associate this project with Markdown slides.
-#   Simply enter your slide deck's filename without extension.
-#   E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
-#   Otherwise, set `slides = ""`.
-slides: example
----
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content/project/twtlplan/index.md

@@ -0,0 +1,124 @@
+---
+title: Path Planning With TWTLPlan
+summary: Sampling-based path planning using temporal logic specifications
+tags: []
+date: "2021-02-03"
+
+# Optional external URL for project (replaces project detail page).
+external_link: ""
+
+image:
+  caption: ""
+  focal_point: Smart
+
+links:
+# - icon: twitter
+#   icon_pack: fab
+#   name: Follow
+#   url: https://twitter.com/georgecushen
+url_code: "https://github.com/fran-penedo/twtlplan"
+url_pdf: ""
+url_slides: ""
+url_video: ""
+
+# Slides (optional).
+#   Associate this project with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
+#   Otherwise, set `slides = ""`.
+slides: ""
+---
+
+Motion planning is a fundamental problem in robotics. The
+objective is to generate control policies for a robot to drive it from
+an initial state to a goal region in its state space under
+kino-dynamic constraints
+Even without considering dynamics, the problem becomes increasingly
+difficult in high dimensions, and it has been shown
+to be PSPACE-complete. A class of algorithms
+was developed relying on randomly sampling
+the configuration space of the robot and planning local 
+motions between these samples.
+Probabilistic Roadmaps and Rapidly Exploring
+Random Trees are among the most widely known
+examples, along with their asymptotically optimal variants,
+PRM$^\*$ and RRT$^\*$.
+
+Robots are increasingly required to perform complex tasks,
+where correctness guarantees, such as safety and liveness
+in human-robot teams and autonomous driving, are critical. 
+One approach is to encode the tasks as temporal logic specifications
+and leverage formal methods techniques to generate
+control policies that are correct by construction.
+As opposed to traditional methods restricted to reach-avoid setups,
+these frameworks are able to express more complex
+tasks such as sequencing (e.g., "Reach $A$, then $B$"),
+convergence ("Go to $A$ and stay there forever"),
+persistent surveillance ("Visit $A$, $B$, and $C$ infinitely often"),
+and more complex combinations of the above.
+Some applications additionally require time constraints.
+For example, consider the following task:
+"Visit $A$, $B$, and $C$ in this order. Perform action $a$ for 2 time units at
+$A$ within 10 time units. Then, perform $b$ for 3 time
+units at $B$ within 6 time units. Finally, in the time window $[3, 9]$ after $b$ is finished,
+perform $c$ for 1 time unit at $C$. All three actions must be finished
+within 15 time units."
+Temporal logics such as Linear Temporal Logic (LTL) or Computational Tree Logic
+(CTL) can be used to encode the former kind of tasks, while tasks with explicit time 
+constraints may be expressed using bounded linear temporal
+logic (BLTL), metric temporal logic (MTL),
+signal temporal logic (STL), and time-window temporal logic
+(TWTL).
+
+A natural approach to generate control strategies from
+rich task specifications for robots with large state spaces is to combine
+sampling-based techniques with automata-based synthesis methods.
+In this project, we proposed a language-guided sampling-based method to generate
+robot control policies satisfying tasks expressed as TWTL formulae.
+A TWTL formula
+formally captures the notion of performing tasks of a certain duration in regions of interest 
+in order. For example, a specification in natural language such as
+"perform task A of duration 1 within 2 time units; then, within the time
+interval [1, 8] perform tasks B and C of durations 3 and 2, respectively; furthermore, C
+must be finished within 4 time units from the start of B;" would correspond to the
+following TWTL formula:
+
+$$
+    f = [H^1 A]^{[0, 2]} * [H^3 B \wedge [H^2 C]^{[0, 4]}]^{[1, 8]}
+$$
+  
+A key feature of TWTL is that a formula may be relaxed, i.e., we can add a variable
+delay to each deadline. A relaxed formula with a positive delay is a weaker
+specification, while a negative delay indicates a stronger specification. A path
+satisfying a stronger specification satisfies a weaker one. TWTLPlan makes use of this
+feature by finding a path that satisfies a weaker specification first, then attempts to
+reduce the delay until the original specification is satified. These paths are computed
+using a sampling-based algorithm called RRT$^\*$.
+
+![Case Study 1 relaxed path found](/media/cs1_c.png) 
+![Case Study 1 satisfying path found](/media/cs1_d.png)
+
+
+This approach has two advantages:
+(a) the growth of the sampling graph is biased towards satisfaction of a
+relaxed version of the specification
+without initially taking into account deadlines, and
+(b) after an initial policy has been found, sampling can be focused on the parts
+of the plan that need to be improved.
+The two stages can be thought of as exploration-exploitation phases,
+where initially candidate solutions are found,
+and then focused local sampling is performed on the parts
+that need to be improved in order to satisfy the specification, i.e. time
+constraints.
+As a byproduct, a satisfying policy with respect to a minimally 
+relaxed version of the specification may be returned
+when the original problem does not have a solution.
+Such a policy may inform operators about timing problems in the specification
+or system (i.e., robot dynamics and/or environment).
+The algorithm uses annotated finite state automata to
+represent all possible temporal relaxations of a TWTL formulae.
+We were also able to prove that our solution is probabilistically complete.
+
+We implemented our path planning algorithm in [TWTLPlan](https://github.com/fran-penedo/twtlplan).
+Using this tool you can define 2D scenarios with rectangular obstacles and regions of
+interest and generate satisfying paths for specifications given in a subset of TWTL.

content/publication/wafr16/index.md

@@ -67,7 +67,7 @@ image:
 #   E.g. `internal-project` references `content/project/internal-project/index.md`.
 #   Otherwise, set `projects: []`.
 projects:
-- twtl
+- twtlplan
 
 # Slides (optional).
 #   Associate this publication with Markdown slides.