Hi, I am Adwait, an Electronics engineer by profession with a knack for learning to program intelligent machines. A roboticist by choice and guitarist in leisure time, I like to learn new languages(I already can speak 7) and do speed cubing when I am not punching keys on my computer. Previously, I worked at HTIC, IIT-Madras with the Spine Robotics team on an amazing project based on Image Guided Surgery (IGS) for the UR5 robotic arm. Previous to that, I was a summer fellow at CVIT, IIIT-H. In my sophomore year, I worked as robotics intern at KUKA, India along with the team of few engineers from ISRO on developing motion planning algorithms for robotic arms. Currently, I am waiting to join my master's at University of Bristol in Robotics.
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AFRAME.registerComponent('pressable', { | |
schema: { | |
pressDistance: { | |
default: 0.06 | |
} | |
}, | |
init: function() { | |
this.worldPosition = new THREE.Vector3(); | |
this.handEls = document.querySelectorAll('[hand-tracking-controls]'); |
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AFRAME.registerComponent('camera-logger', { | |
schema: { | |
timestamp: { | |
type: 'int' | |
}, | |
seconds: { | |
type: 'int' | |
} | |
}, | |
init: function() { |
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## Follow this link for further updates to Github Desktop for Ubuntu https://github.com/shiftkey/desktop/releases/latest | |
# UPDATE (2021-10-18): Thanks to Amin Yahyaabadi's message, the updated code is as follows | |
sudo wget https://github.com/shiftkey/desktop/releases/download/release-2.9.3-linux3/GitHubDesktop-linux-2.9.3-linux3.deb | |
### Uncomment below line if you have not installed gdebi-core before | |
# sudo apt-get install gdebi-core | |
sudo gdebi GitHubDesktop-linux-2.9.3-linux3.deb | |
# UPDATE (2021-03-05): Thanks to PaoloRanzi81's comment, the updated code is as follows https://gist.github.com/PaoloRanzi81 |
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#!/usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
""" | |
Created on Sat Dec 5 00:24:39 2021 | |
@author: adwaitnaik | |
""" | |
import pandas as pd | |
import numpy as np |
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def get_agent_states(self): | |
start_state = None | |
target_state = None | |
start_state = list(map(lambda x:x[0] if len(x) > 0 else None, np.where(insert_grid_map==4))) | |
target_state = list(map(lambda x:x[0] if len(x) > 0 else None, np.where(insert_grid_map==3))) | |
if (start_state == [None, None] or target_state == [None, None]): | |
sys.exit('Start or Target state not specified') |
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#fetching agent states | |
def get_agent_states(self): | |
start_state = None | |
target_state = None | |
start_state = list(map(lambda x:x[0] if len(x) > 0 else None, np.where(insert_grid_map==4))) | |
target_state = list(map(lambda x:x[0] if len(x) > 0 else None, np.where(insert_grid_map==3))) | |
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#constructing grid and fetching observations | |
#reading the data from the grid | |
def read_grid_map(self, insert_grid_map): | |
with open(insert_grid_map, 'r') as f: | |
grid_map = f.readlines() | |
grids = np.array(list(map(lambda x: | |
list(map(lambda y: int(y), | |
x.split(''))), grid_map))) |
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#next step | |
def step(self, action): | |
action = self.actions | |
reward_dict = {'up':10, 'down':10, 'left':10, 'right':10, 'begin':0, 'obstacle':-5} | |
next_state = (self.agent_state[0]+self.actions_pos_dict[actions][0], | |
self.agent_state[1]+self.actions_pos_dict[actions][1] ) | |
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#creating Grid Environment | |
COLORS = {0:[0.0,0.0,0.0], 1:[0.5,0.5,0.5], | |
2:[0.0,0.0,1.0], 3:[0.0,1.0,0.0], | |
4:[1.0,0.0,0.0], 6:[1.0,0.0,1.0], | |
7:[1.0,1.0,0.0]} | |
class GridEnv(gym.Env): | |
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