added Moji's IROS paper

_data/publications.json

@@ -47,6 +47,36 @@
         ],
         "abstract": "Mobile manipulators are susceptible to situations in which the precomputed grasp pose is not reachable as the result of conflicts between collision avoidance behaviour and the manipulation task. In this work, we address this issue by combining real-time grasp planning with geometric motion planning for decentralized multi-agent systems, referred to as Reactive Grasp Fabrics (RGF). We optimize the precomputed grasp pose candidate to account for obstacles and the robot's kinematics. By leveraging a reactive geometric motion planner, specifically geometric fabrics, the grasp optimization problem can be simplified, resulting in a fast, adaptive framework that can resolve deadlock situations in pick-and-place tasks. We demonstrate the robustness of this approach by controlling a mobile manipulator in both simulation and real-world experiments in dynamic environments."
     },
+    {
+        "title": "Evaluating Dynamic Environment Difficulty for Obstacle Avoidance Benchmarking",
+        "authors": [
+            "Moji Shi",
+            "Gang Chen",
+            "Alvaro Serra-Gomez",
+            "Siyuan Wu",
+            "Javier Alonso-Mora"
+        ],
+        "date": "2024-10-15",
+        "type": "conference",
+        "venue": "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",
+        "links": [
+            {
+                "pdf": "/assets/files/publications/24_moji_iros.pdf",
+                "code": "https://github.com/smoggy-P/gym-Drone2D-ActivePerception",
+                "web": "https://smoggy-p.github.io/Evaluating_Dynamic_Difficulty/"
+            }
+        ],
+        "image": "/assets/images/papers/dynamic_env_metric/dynamic_environment_difficulty.png",
+        "belongs_to_projects": [
+        	"interact"
+        ],
+        "topics":[
+            "evlaution",
+            "benchmarking",
+            "motion planning"    
+        ],
+        "abstract": "Dynamic obstacle avoidance is a popular research topic for autonomous systems, such as micro aerial vehicles and service robots. Accurately evaluating the performance of dynamic obstacle avoidance methods necessitates the establishment of a metric to quantify the environment's difficulty, a crucial aspect that remains unexplored. In this paper, we propose four metrics to measure the difficulty of dynamic environments. These metrics aim to comprehensively capture the influence of obstacles' number, size, velocity, and other factors on the difficulty. We compare the proposed metrics with existing static environment difficulty metrics and validate them through over 1.5 million trials in a customized simulator. This simulator excludes the effects of perception and control errors and supports different motion and gaze planners for obstacle avoidance. The results indicate that the survivability metric outperforms and establishes a monotonic relationship between the success rate, with a Spearman's Rank Correlation Coefficient (SRCC) of over 0.9. Specifically, for every planner, lower survivability leads to a higher success rate. This metric not only facilitates fair and comprehensive benchmarking but also provides insights for refining collision avoidance methods, thereby furthering the evolution of autonomous systems in dynamic environments."
+    },
     {
         "title": "Current-Based Impedance Control for Interacting with Mobile Manipulators",
         "authors": [

_people/clarence-chen.md

@@ -2,7 +2,7 @@
 name: Gang (Clarence) Chen
 title: Post-Doctoral Researcher # The title that shows below your name.
 type: post-doc # post-doc, phd-visitor, professor, engineer
-joined_date: 2023-06-01
+joined_date: 2023-02-15
 # left_date: 2023-09-30 # Optional, if you add a left date the member will be moved to the past members section
 image: /assets/images/people/Clarence_Chen.jpeg # link or path in /assets/...
 links: # Optional, links to other stuff