add word clouds

README.md

@@ -44,9 +44,7 @@
 <details>
 <summary><b>2023</b></summary>
 
-<center>
-<img src="./assets/2023.png" width="50%" />
-</center>
+<img src="./assets/2023.png" width="70%" class="center"/>
 
 - [AMP in the wild: Learning robust, agile, natural legged locomotion skills](https://arxiv.org/abs/2304.10888)
 - [ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments](https://arxiv.org/abs/2303.15900)
@@ -98,9 +96,7 @@
 <details>
 <summary><b>2022</b></summary>
 
-<center>
-<img src="./assets/2022.png" width="50%" />
-</center>
+<img src="./assets/2022.png" width="70%" class="center"/>
 
 - [A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation](https://arxiv.org/abs/2202.12385v1)
 - [A Linearization of Centroidal Dynamics for the Model-Predictive Control of Quadruped Robots](https://ieeexplore.ieee.org/document/9812433)
@@ -191,9 +187,7 @@
 <details>
 <summary><b>2021</b></summary>
 
-<center>
-<img src="./assets/2021.png" width="50%" />
-</center>
+<img src="./assets/2021.png" width="70%" class="center"/>
 
 - [A Review of Physics Simulators for Robotic Applications](https://ieeexplore.ieee.org/document/9386154/)
 - [A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation](https://arxiv.org/abs/2103.00946)
@@ -240,9 +234,7 @@
 <details>
 <summary><b>2020</b></summary>
 
-<center>
-<img src="./assets/2020.png" width="50%" />
-</center>
+<img src="./assets/2020.png" width="70%" class="center"/>
 
 - [An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research](https://ieeexplore.ieee.org/document/9015985)
 - [Dream to Control: Learning Behaviors by Latent Imagination](https://arxiv.org/abs/1912.01603)
@@ -270,9 +262,7 @@
 <details>
 <summary><b>2019</b></summary>
 
-<center>
-<img src="./assets/2019.png" width="50%" />
-</center>
+<img src="./assets/2019.png" width="70%" class="center"/>
 
 - [DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning](https://doi.org/10.48550/arXiv.1909.08399)
 - [Design a Fall Recovery Strategy for a Wheel-Legged Quadruped Robot Using Stability Feature Space](https://doi.org/10.1109/ROBIO49542.2019.8961722)
@@ -294,9 +284,7 @@
 <details>
 <summary><b>2018</b></summary>
 
-<center>
-<img src="./assets/2018.png" width="50%" />
-</center>
+<img src="./assets/2018.png" width="70%" class="center"/>
 
 - [Contact Model Fusion for Event-Based Locomotion in Unstructured Terrains](https://ieeexplore.ieee.org/document/8460904)
 - [Dynamic locomotion in the MIT Cheetah 3 through convex model-predictive control](https://ieeexplore.ieee.org/document/8594448)
@@ -316,9 +304,7 @@
 <details>
 <summary><b>~ 2017</b></summary>
 
-<center>
-<img src="./assets/2017.png" width="50%" />
-</center>
+<img src="./assets/2017.png" width="70%" class="center"/>
 
 - [ANYmal - a highly mobile and dynamic quadrupedal robot](https://doi.org/10.1109/IROS.2016.7758092)
 - [Design of HyQ – a hydraulically and electrically actuated quadruped robot](https://doi.org/10.1177/0959651811402275)

assets/word_cloud.py

@@ -20,22 +20,55 @@
 # 1. Get the paper list from the user
 # papers_input = input("Please provide the list of papers:\n")
 papers_input = """
-- [ANYmal - a highly mobile and dynamic quadrupedal robot](https://doi.org/10.1109/IROS.2016.7758092)
-- [Design of HyQ – a hydraulically and electrically actuated quadruped robot](https://doi.org/10.1177/0959651811402275)
-- [High-slope terrain locomotion for torque-controlled quadruped robots](https://link.springer.com/article/10.1007/s10514-016-9573-1)
-- [Meta Learning Shared Hierarchies](https://doi.org/10.48550/arXiv.1710.09767)
-- [Robot-Centric Elevation Mapping with Uncertainty Estimates](https://doi.org/10.1142/9789814623353_0051)
-- [Slip Detection and Recovery for Quadruped Robots](https://doi.org/10.1016/j.robot.2005.07.002)
-- [State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and IMU](https://doi.org/10.7551/mitpress/9816.001.0001)
-- [Survey of Numerical Methods for Trajectory Optimization](https://arc.aiaa.org/doi/10.2514/2.4231)
-- [Terrain-adaptive locomotion skills using deep reinforcement learning](https://dl.acm.org/doi/10.1145/2897824.2925881)
-- [Wholebody trajectory optimization for non-periodic dynamic motions on quadrupedal systems](https://ieeexplore.ieee.org/document/7989623)
-
+- [AMP in the wild: Learning robust, agile, natural legged locomotion skills](https://arxiv.org/abs/2304.10888)
+- [ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments](https://arxiv.org/abs/2303.15900)
+- [ArtPlanner: Robust Legged Robot Navigation in the Field](https://arxiv.org/abs/2303.01420)
+- [ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation](https://arxiv.org/abs/2304.00410)
+- [Autonomous Stair Ascending and Descending by Quadruped Wheelchairs](https://ieeexplore.ieee.org/abstract/document/10202377)
+- [Barkour: Benchmarking Animal-level Agility with Quadruped Robots](https://arxiv.org/abs/2305.14654)
+- [DOC: Differentiable Optimal Control for Retargeting Motions onto Legged Robots](https://la.disneyresearch.com/wp-content/uploads/DOC_paper.pdf)
+- [Dojo: A Differentiable Physics Engine for Robotics](https://arxiv.org/abs/2203.00806)
+- [DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning](https://arxiv.org/abs/2301.10602)
+- [DribbleBot: Dynamic Legged Manipulation in the Wild](https://gmargo11.github.io/dribblebot/rsc/dribblebot_paper.pdf)
+- [Drilling Task with a Quadruped Robot for Silage Face Measurements](https://www.researchgate.net/publication/370765569_Drilling_Task_with_a_Quadruped_Robot_for_Silage_Face_Measurements)
+- [Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface](https://arxiv.org/abs/2305.08962)
+- [Event-based Agile Object Catching with a Quadrupedal Robot](https://arxiv.org/abs/2303.17479)
+- [Fast Traversability Estimation for Wild Visual Navigation](https://arxiv.org/abs/2305.08510)
+- [Fast Traversability Estimation for Wild Visual Navigation](https://arxiv.org/abs/2305.08510)
+- [From Data-Fitting to Discovery: Interpreting the Neural Dynamics of Motor Control through Reinforcement Learning](https://arxiv.org/abs/2305.11107)
+- [iPlanner: Imperative Path Planning](https://arxiv.org/abs/2302.11434)
+- [Language to Rewards for Robotic Skill Synthesis](https://arxiv.org/abs/2306.08647)
+- [Learning a Single Policy for Diverse Behaviors on a Quadrupedal Robot using Scalable Motion Imitation](https://arxiv.org/abs/2303.15331)
+- [Learning and Adapting Agile Locomotion Skills by Transferring Experience](https://arxiv.org/abs/2304.09834)
+- [Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators](https://www.research-collection.ethz.ch/handle/20.500.11850/595246)
+- [Learning Complex Motor Skills for Legged Robot Fall Recovery](https://ieeexplore.ieee.org/document/10138662/)
+- [Learning Impulse-Reduced Gait for Quadruped Robot using CMA-ES](https://ieeexplore.ieee.org/abstract/document/10202519)
+- [Learning quadrupedal locomotion on deformable terrain](https://www.science.org/doi/full/10.1126/scirobotics.ade2256)
+- [Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic Environments](https://arxiv.org/abs/2209.09233)
+- [Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion](https://arxiv.org/abs/2303.11330)
+- [LSC: Language-guided Skill Coordination](https://languageguidedskillcoordination.github.io/)
+- [LSTP: Long Short-Term Motion Planning for Legged and Legged-Wheeled Systems](https://www.research-collection.ethz.ch/handle/20.500.11850/625515)
+- [Mastering Diverse Domains through World Models](https://arxiv.org/abs/2301.04104)
+- [Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotion](https://arxiv.org/abs/2308.12517)
+- [OPT-Mimic: Imitation of Optimized Trajectories for Dynamic Quadruped Behaviors](https://arxiv.org/abs/2210.01247)
+- [ORBIT: A Unified Simulation Framework for Interactive Robot Learning Environments](https://ieeexplore.ieee.org/abstract/document/10107764)
+- [Reinforcement Learning for Legged Robots: Motion Imitation from Model-Based Optimal Control](https://arxiv.org/abs/2305.10989)
+- [Reinforcement Learning from Multiple Sensors via Joint Representations](https://arxiv.org/abs/2302.05342)
+- [RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion](https://arxiv.org/abs/2305.17842)
+- [Robust Quadrupedal Locomotion via Risk-Averse Policy Learning](https://arxiv.org/abs/2308.09405)
+- [Robust Recovery Motion Control for Quadrupedal Robots via Learned Terrain Imagination](https://arxiv.org/abs/2306.12712)
+- [Roll-Drop: accounting for observation noise with a single parameter](https://arxiv.org/abs/2304.13150)
+- [SafeSteps: Learning Safer Footstep Planning Policies for Legged Robots via Model-Based Priors](https://arxiv.org/abs/2307.12664.pdf)
+- [SayTap: Language to Quadrupedal Locomotion](https://arxiv.org/abs/2306.07580)
+- [Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots](https://arxiv.org/abs/2307.10079)
+- [Solving Challenging Control Problems via Learning-based Motion Planning and Imitation](https://ieeexplore.ieee.org/abstract/document/10202250)
+- [Towards Legged Locomotion on Steep Planetary Terrain](https://www.research-collection.ethz.ch/handle/20.500.11850/625001)
+- [Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation](https://www.science.org/doi/10.1126/scirobotics.adg5014)
 """
 
 # 2. Ask the user for the title of the picture
 # picture_title = input("Please provide the title for the picture (without the .png extension): ")
-picture_title = "2017"
+picture_title = "2023"
 
 # 3. Extract the words from the titles (excluding the links)
 titles = re.findall(r"\[(.*?)\]\(.*?\)", papers_input)
@@ -51,8 +84,8 @@
 keyword_freq = Counter(keywords)
 
 # 4. Create the word cloud with landscape ratio and save it
-wordcloud = WordCloud(width=1200, height=600, background_color="white").generate_from_frequencies(keyword_freq)
-plt.figure(figsize=(12,6))
+wordcloud = WordCloud(width=1800, height=600, background_color="white").generate_from_frequencies(keyword_freq)
+plt.figure(figsize=(18,6))
 plt.imshow(wordcloud, interpolation="bilinear")
 plt.axis("off")
 plt.tight_layout()