Iretiayo Akinola

This work provides a framework for a workspace aware online grasp planner. Expand

We present Multi-Fingered Adaptive Tactile Grasping, or MAT, a tactile closed-loop method capable of realizing grasps provided by a coarse initial positioning of the hand above an object. Expand

We introduce a multi-fingered robotic grasping policy in simulation that operates in the pixel space of the input: a single depth image. Expand

This paper describes a novel hierarchical system for shared control of a humanoid robot. Expand

Generative Attention Learning (GenerAL) is a framework for high-DOF multi-fingered grasping that is not only robust to dense clutter and novel objects but also effective with a variety of different parallel-jaw and multi-fingerered robot hands. Expand

We present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature. Expand

We propose a method that uses evaluative feedback obtained from human brain signals measured via scalp EEG to accelerate RL for robotic agents in sparse reward settings. Expand

We present an approach that uses active learning to smartly choose queries for the human supervisor based on the uncertainty of the robot and effectively reduces the amount of feedback needed to learn a given task. Expand