Multiform Adaptive Robot Skill Learning from Humans

Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC), Tysons Corner, VA, October 11-1

Construction and Calibration of a Low-Cost 3D Laser Scanner with 360◦ Field of View for Mobile Robots

Navigation of many mobile robots relies on environmental information obtained from three-dimensional (3D) laser scanners. This paper presents a new 360◦ field-of-view 3D laser scanner for mobile robots that avoids the high cost of commercial devices. The 3D scanner is based on spinning a Hokuyo UTM- 30LX-EX two-dimensional (2D) rangefinder around its optical center. The proposed design profits from lessons learned with the development of a previous 3D scanner with pitching motion. Intrinsic calibration of the new device has been performed to obtain both temporal and geometric parameters. The paper also shows the integration of the 3D device in the outdoor mobile robot Andabata.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Robotic Baseball Throwing Arm

ME450 Capstone Design and Manufacturing Experience: Fall 2020We wanted to build a robotic arm that can help study the ways we measure speeds, accelerations and torques of a human arm through a baseball pitchDr. Stephen Cain, Mechanical Engineering Researchhttp://deepblue.lib.umich.edu/bitstream/2027.42/164450/1/Robotic_Baseball_Throwing_Arm.pd

Design and prototype of a hovering ornithopter based on dragonfly flight

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.Includes bibliographical references (leaf 31).Hovering is normally achieved using a horizontal wing path to create lift; bees, wasps and helicopters use this technique. Dragonflies hover using a unique method, by flapping along an inclined stroke plane. This seems to create a higher efficiency than is possible for normal hovering. The aim of this project is to build a mechanical model to mimic the aerodynamic properties and hovering motion of dragonflies. Through the design and evaluation of this model, we can evaluate the mechanical feasibility of reproducing the wing path using single motor control and establish whether the difference in stroke plane is advantageous for the dragonfly. By adjusting the initial angle of attack of the ornithopter's wings, we can artificially recreate varying stroke planes. A comparison of the resultant lift generated from different stroke planes showed that greater lift forces were generated with non-zero stroke planes as demonstrated in normal hovering.by Theresa Guo.S.B

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

We investigate whether a robot arm can learn to pick and throw arbitrary objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring reliable pre-throw conditions (e.g. initial pose of object in manipulator) to handling varying object-centric properties (e.g. mass distribution, friction, shape) and dynamics (e.g. aerodynamics). In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot, is able to grasp and throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations. Videos are available at https://tossingbot.cs.princeton.eduComment: Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage: https://tossingbot.cs.princeton.ed

Structural dynamics branch research and accomplishments to FY 1992

This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications

Experiment, simulation and analysis on coupling hydrodynamic forces under key parameters for a spherical underwater exploration robot

As a novel underwater exploration robot, BYSQ-2 spherical robot uses the heavy pendulum to change the attitudes with the characteristics of small steering resistance and high compressive strength. However, the greater water resistance in the process of moving forward obstructs the rapid movement, because the robot has a spherical shell and only one propeller. The maximum speed was obtained only 0.6 m/s according to experimental tests and theoretical calculations. In order to improve the movement speed, the robot’s virtual assembly model was built to study the coupling hydrodynamic forces between the spherical shell and the propeller by CFD method. The coupling hydrodynamic forces were analyzed and summarized under different key structural parameters that include the pipe diameter and the shell diameter. Furthermore, in the conditions of different rotational speed, propeller thrust and water resistance of robot were simulated and calculated. According to the simulation results of the model with the appropriate structural parameters, it was demonstrated that the speed of the robot was improved obviously in the process of moving forward