Jethro's Braindump

Robot Grasping

Humans are able to perform dexterous manipulation of everyday, unseen objects with their limbs. Yet, it remains a challenge for robots to perform grasping and manipulation of objects.

A typical robot grasp detection system can consist of 3 components.

Object localization
determining the target object’s location
Object Pose Estimation
obtaining a target object’s 6D pose in the scene
Grasp Estimation
Estimating the grasp pose in the camera’s coordinate frame


  1. The robot needs to adapt to changes in the environment.
  2. The robot is susceptible to noise and errors in the environment, in control, in perception, and perturbations to the robot itself.

Grasping Approaches

Dexterous manipulation
manipulation with fingers
Enveloping grasps
formed by wrapping both fingers and palm around object

Enveloping grasps have superior stability. Grasping Simulators such as (GraspIt!) have been made available for theoretical research.

Closed-loop Grasping

Execution time is the primary reason why grasping systems remain open-loop. However, closed-loop control via visual feedback is desirable because they are able to adapt to dynamic environments, and require less precise position control or camera calibration. (NO_ITEM_DATA:morrisonClosingLoopRobotic2018) uses neural network to synthesize a grasp quality image in real-time to achieve closed-loop grasping.