Three Laws of Robotics | EPFL Graph Search

The Three Laws of Robotics (often shortened to The Three Laws or Asimov's Laws) are a set of rules devised by science fiction author Isaac Asimov, which were to be followed by robots in several of his stories. The rules were introduced in his 1942 short story "Runaround" (included in the 1950 collection I, Robot), although similar restrictions had been implied in earlier stories. The laws The Three Laws, presented to be from the fictional "Handbook of Robotics, 56th Edition, 2058 A.D.", are: First Law A robot may not injure a human being or, through inaction, allow a human being to come to harm. Second Law A robot must obey the orders by human beings except where such orders would conflict with the First Law. Third Law A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Use in fiction These form an organizing principle and unifying theme for Asimov's robot-based fiction, a

Improving the accuracy of parallel robots

The main objective of this thesis is the accuracy improvement of parallel robots. Accuracy can be improved either by precise manufacturing and assembly or by calibration of each individual robot using a kinematic model which takes geometric deviations into account. The latter has the advantage of leading to low cost solutions but requires sophisticated modeling of the robot's structure which is usually considerably more complex than the derivation of its nominal model. To substantiate the theoretical tools proposed in this thesis two examples of parallel structures are chosen. One of them is the Delta robot with three translational degrees of freedom whereas the second example is a novel structure called Argos having three rotational degrees of freedom. For experimental verification a mock-up was built for each of the two structures. Four calibration steps, modeling, measurement, identification, and implementation are investigated. Investigations were restricted to static errors due to geometric deviations assuming rigid bodies. First a formula is proposed which allows to calculate the number of independent kinematic parameters required for a complete model of a parallel structure. Then a systematic parameterization is introduced and applied to derive four calibration models, two for each example. Two measurement devices are described which were built to determine the position and orientation (pose) of the end-effectors of the two robots. For the Delta robot two additional set-ups using no external (additional) measurement device are proposed. For parameter identification different methods were tested by simulation. Calibration based on the implicit model is proposed as a standard method to calibrate parallel robots. Another calibration method is introduced, referred to as semiparametric calibration, which leads to low computational effort. Fast solutions of the direct and inverse problems had to be found. For the first time al1 the solutions of the direct problem for the Delta robot were found by means of an algorithm introduced by Husty. In addition a fast numeric algorithm for the Delta's direct problem is proposed. The main contribution of this thesis is the experimental verification of calibration methods to improve the accuracy of parallel robots. Using these calibration methods for the two robots, ARGOS and DELTA, between a three- to a twelve-fold improvement of accuracy was achieved and experimentally verified.

EPFL1996

How to compete with robots by assessing job automation risks and resilient alternatives

Dario Floreano, Rafael Lalive, Nicola Nosengo, Antonio Paolillo, Fabrizio Schiano, William John Stewart, Davide Zambrano

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers’ automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.

2022

Piezoelectric Grippers for Mobile Micromanipulation

Tristan Abondance, Kaushik Jayaram

The ability to efficiently and precisely manipulate objects in inaccessible environments is becoming an essential requirement for many applications of mobile robots, particularly at small sizes. Here, we propose and implement a mobile micromanipulation solution using a piezoelectric microgripper integrated into a dexterous robot, HAMR (the Harvard Ambulatory MicroRobot), that has a size of approximately 4.5 cm by 4 cm by 2.3 cm and a maximum payload of approximately 3 g. Our 100 mg miniature gripper is composed of recurve piezoelectric actuators that produce parallel jaw motions (stroke of 236 +/- 32 mu m at 200 V) while providing high gripping forces (blocked force of 0.575 N at 200 V), making it effective for micromanipulation applications with tiny objects. Using this gripper, we successfully demonstrated a grasping and lifting task with an object of 1.3 g and thickness of 250 mu m at an operating voltage of 100 V. Finally, by taking advantage of the locomotion capabilities ofHAMR, wedemonstrate mobile manipulation by changing the position and orientation of small objects weighing up to 2.8 g controlled by the movement of the robot. We expect that the addition of this novel manipulation capability will increase the effectiveness of such miniature robots for accomplishing real-world tasks.

2020

Improving the accuracy of parallel robots

EPFL1996