Search Results for author:
Found 7 papers, 2 papers with code
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects.
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction.
Domain Randomization for Active Pose Estimation
In this work, we investigate how to improve the accuracy of domain randomization based pose estimation.
Residual Reinforcement Learning for Robot Control
In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL.
Learning Robotic Assembly from CAD
We propose to leverage this prior knowledge by guiding RL along a geometric motion plan, calculated using the CAD data.
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6. 7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1. 0 in randomized poses on a table.
Robotics
