analysis-of-gan-approaches-on-mnist-using-tf2.0

The Generative Adversarial Networks with Python would serve as our primary reference throughout the project. The models would be trained on the MNIST dataset. The official TensorFlow framework and documentation will be used to implement the different architectures on Python. These papers would be used to implement various evaluation met

conq_python

Python library and example scripts for Conq, our Boston Dynamics Spot

deep-learning-for-robotics-project

Contact-grasp net + Affordance Novelty

gl_2022

gpd

Detect 6-DOF grasp poses in point clouds

introduction-to-manipulation

Python scripts of the assignments in ROB 599 Introduction to Manipulation (Fall 2023)

jemuelstanley47

Profile Description

jemuelstanley47.github.io

learning-latent-space-dynamics

Implementation of a Variational Autoencoder for learning latent space dynamics for pushing

learning-shape-representations-deepsdf-

A PyTorch implementation for learning SDF for performing 3D reconstruction. The modified autodecoder, as replicated from DeepSDF is trained from scratch to reconstruct few object classes sourced from the ShapeNet dataset.

motion-planning-and-trajectory-generation

Implementation of RRT-connect, MDP and Geometrical IK

neural-ode-robot-learning

Cartpole dynamics prediction using neural ode solver with Gym space.

posecnn

PyTorch implementation of PoseCNN

two-stage-object-detector-faster-r-cnn

Object Detector Faster RCNN with Feature Pyramid Network and RegNetX-400MF as the backbone.