We present DARRT, a sampling-based algorithm for planning with multiple types of manipulation. Given a robot, a set of movable objects, and a set of actions for manipulating the objects, DARRT returns a sequence of manipulation actions that move the robot and objects from an initial configuration to a final configuration. The manipulation actions may be non-prehensile, meaning that the object i...

This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires sequence of highly dynamic coordinated movements, which is usually difficult learn for chef, let alone transfer robots. In this letter, we define canonical movement, and then propose decoupled framework learning deformable object manipulation from human demonstratio...

Object manipulation techniques in robotics can be categorized in two major groups including manipulation with and without grasp. The aim of this paper is to develop an object manipulation method where in addition to being grasp-less, the manipulation task is done in a passive approach. In this method, linear and angular positions of the object are changed and its manipulation path is controlled...

Many manipulation systems using air flow have been proposed for object handling in a non-prehensile way and without solid-to-solid contact. Potential applications include high-speed transport of fragile and clean products and high-resolution positioning of wafers. This paper discusses a comprehensive survey of state-of-the art pneumatic manipulation from the macro scale to the micro scale. The ...

This paper describes a system in which multiple robots cooperate to move multiple objects such as groups of boxes using a constrained prehensile manipulation mode, by wrapping ropes around them. The system consists of three manipulation skills: tieing ropes around objects, affecting rotations using a flossing manipulation gait, and affecting translations using a ratcheting manipulation gait. We...

We explore learning-based approaches for feedback control of a dexterous five-finger hand performing non-prehensile manipulation. First we learn local controllers that are able to perform the task starting at a predefined initial state. These controllers are constructed using trajectory optimization with respect to locally-linear time-varying models learned directly from sensor data. In some ca...

Robotic manipulation in cluttered environments is one of the challenges roboticists are currently facing. When objects to handle delicate fresh fruits, grasping even more challenging. Detecting and localizing fruits with accuracy necessary grasp them very difficult due large variability aspect dimensions each item. This paper proposes a solution that exploits state-of-the-art neural network nov...

In this paper, we propose a multi-task learning from demonstration method that works using raw images as input to autonomously accomplish a wide variety of tasks in the real world using a low-cost robotic arm. The controller is a single recurrent neural network that can generate robot arm trajectories to perform different manipulation tasks. In order to learn complex skills from relatively few ...