Vignesh Prasad (souljaboy764)

souljaboy764

Geek Repo

Company:TU Darmstadt

Location:Darmstadt, Germany

Home Page:https://sites.google.com/view/vignesh-prasad/

Twitter:@FatAndFurious42

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Vignesh Prasad's repositories

intprim

Interaction Primitives library

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cogail

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

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dynamicslearner

Deep Learning based Contact Observer for Pepper robot

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franka_ros

ROS integration for Franka research robots

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GraphDiffusionImitate

Master's thesis exploring diffusion-based graph generative models for imitation learning in robotics tasks 🧠🤖

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grasp_diffusion

Pytorch implementation of diffusion models on Lie Groups for 6D grasp pose generation https://sites.google.com/view/se3dif/home

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hvgh-pytorch

PyTorch implementation of Nagano et al. "HVGH: Unsupervised Segmentation for High-Dimensional Time Series Using Deep Neural Compression and Statistical Generative Model", 2019, Frontiers in Robotics and AI.

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ikpy

An Inverse Kinematics library aiming performance and modularity

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mc_naoqi

Communication module between mc_rtc and SoftBank robots running NAOqi operating system

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mc_naoqi_dcm

DCM module for SoftBankRobotics robots designed to be used with mc_naoqi interface

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MNIST-for-Numpy

A simple, easy to use MNIST loader written in Python 3

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naoqi_dcm_driver

Hardware interface allowing control Nao, Romeo, and Pepper robots via ROS

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nuisi_dataset

NuiSI: Nuitrack Skeleton Interaction Dataset of Physical Human-Human Interactions

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pepper_compliance_controllers

Collection of Controllers for Testing Position based Compliance on Pepper

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pepper_controller_server

Custom Controller Server Implementations using the ROS control interface

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phd_utils

A repository of helper functions for the various projects done during my PhD

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realsense-ros

Intel(R) RealSense(TM) ROS Wrapper for D400 series, SR300 Camera and T265 Tracking Module

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rmdn_hri_ros

ROS package for rmdn_hri

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skorch-vae

Tutorial repo for running Gridsearch for VAEs using skorch

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tcn

An pytorch implementation of time-contrastive networks as presented in the paper "Time-Contrastive Networks: Self-Supervised Learning from Multi-View Observation".

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