Changes between Version 20 and Version 21 of Other/Summer/2023/RobotTestbed

Timestamp:

Jul 20, 2023, 5:40:56 PM (12 months ago)

Author:

katrinacelario

Comment:

—

Other/Summer/2023/RobotTestbed

@@ -9 +9 @@
 The main purpose of the project is to focus on the Internet of Things (IoT) and its transformative potential when intertwined with Machine Learning (ML). To explore this subject, the group continues the work of the '''''!SenseScape Testbed''''', an IoT experimentation platform for indoor environments containing a variety of sensors, location-tracking nodes, and robots. This testbed enables IoT applications, such as but not limited to, human activity and speech recognition and indoor mobility tracking. In addition, this project advocates for energy efficiency, occupant comfort, and context representation. The ''!SenseScape Testbed'' provides an adaptable environment for labelling and testing advanced ML algorithms centered around IoT.
 
+=== Hardware ===
+
+This project is centered on a specific piece of hardware referred to as a '''''MAESTRO''''', a custom multi-modal sensor designed by the previous group. The MAESTRO is capable of perceiving different types of data from its environment. 
+
+
 == Project Goals ==
-'''Previous Groups Work''': **https://dl.acm.org/doi/abs/10.1145/3583120.3589838**
+'''Previous Group's Work''': **https://dl.acm.org/doi/abs/10.1145/3583120.3589838**
 
 Based on the future research section, there are two main goals the group wants to accomplish. 
@@ -20 +25 @@
 
 === The Projects's Three Phases ===
-'''Scenario One'''
 
-'''Scenario Two'''
+The progression of this project relies on three milestones, each with unique and specific goals. Moving forward, each phase is more advanced than the last.
 
-'''Scenario Three'''
+'''Phase One:'''
+
+For the first phase, the group is looking for the MAESTROs to recognize a predetermined set of activities in an office environment, in this case WINLAB. The plan is to set the MAESTROs in a grid like coordinate system, considering both the location of outlets and the "predetermined activities" that will be conducted. In addition to the MAESTROs, there will be multiple cameras in place capturing continuous video data of human activity. This video data will be used for the automatic labeling. Phase one is the foundation for the rest of the milestones moving forward.
+
+'''Phase Two:'''
+
+For the second phase, the group is looking for the MAESTROs to communicate with each other about what is happening in their immediate space using '''zero-shot''' or '''few-shot''' recognition. 
+
+'''zero-shot:''' 
+      ability of a large language model to perform a task or    generate responses for which it has not been explicitly trained.
+
+'''few-shot:'''
+      ability of a large language model to recognize or classify new objects or categories which only a. few labeled examples or shots.
+
+
+'''Phase Three'''
+
+For the third and final phase, the group is looking for the MAESTROs to communicate with each other to create a narrative of the activity in the given space. The model could be queried about the "memory" of the space and will give ranging descriptions based on the desired scope of the answer (1 hour vs 1 year). Seen in this phase, the large language model is the core of the project; however, the MAESTROs must be deployed first.
 
 == Progress Overview ==