Stanford Interactive Perception and Robot Learning Lab
We seek to understand the underlying principles of robust sensorimotor coordination by implementing them on robots. This is the place for our Open Source code.
Stanford
Pinned Repositories
ao-grasp
Concept2Robot
simulations used in "Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations"
franka-panda-iprl
IPRL's Franka Panda Driver and Operational Space Controller
GRAC
implementation of our self-guided and self-regularized actor-critic algorithm
multimodal_representation
nerf_shared
A object-oriented, minimalistic PyTorch implementation of NeRF (Neural Radiance Fields).
ScaffoldLearning
Simulation Environments in "Learning to Scaffold the Development of Robotic Manipulation Skills"
sceneflownet
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
torchfilter
Bayesian filters in PyTorch
UniGrasp
Implementation of " UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands"
Stanford Interactive Perception and Robot Learning Lab's Repositories
stanford-iprl-lab/torchfilter
Bayesian filters in PyTorch
stanford-iprl-lab/multimodal_representation
stanford-iprl-lab/UniGrasp
Implementation of " UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands"
stanford-iprl-lab/GRAC
implementation of our self-guided and self-regularized actor-critic algorithm
stanford-iprl-lab/sceneflownet
Motion-based Object Segmentation based on Dense RGB-D Scene Flow
stanford-iprl-lab/Concept2Robot
simulations used in "Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations"
stanford-iprl-lab/nerf_shared
A object-oriented, minimalistic PyTorch implementation of NeRF (Neural Radiance Fields).
stanford-iprl-lab/ao-grasp
stanford-iprl-lab/franka-panda-iprl
IPRL's Franka Panda Driver and Operational Space Controller
stanford-iprl-lab/ScaffoldLearning
Simulation Environments in "Learning to Scaffold the Development of Robotic Manipulation Skills"
stanford-iprl-lab/coding-practices-tutorial
Tips for writing readable and maintainable code
stanford-iprl-lab/baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
stanford-iprl-lab/contact_graspnet
Efficient 6-DoF Grasp Generation in Cluttered Scenes
stanford-iprl-lab/FrankaPanda
model description and driver for the Panda arm
stanford-iprl-lab/rrc_simulation
Simulation for the Real Robot Challenge (https://real-robot-challenge.com)
stanford-iprl-lab/zeddy
stanford-iprl-lab/ctrl-utils
stanford-iprl-lab/dbot
Depth-Based Bayesian Object Tracking Library
stanford-iprl-lab/fl
A Lightweight Bayesian Filtering Library with real-time support
stanford-iprl-lab/mujoco-py
MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
stanford-iprl-lab/rrc_package
Example package for the Real Robot Challenge Submission System