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Thou Shalt Stand Accountable to Another Person for Thy Trades Harvey taught Harry those laws I invented Harry and Harvey and Roddy and Winnipeg George, and all the others, in the hopes that you would gain something from reading about their experiences You ve already noticed that Harvey didn t teach Harry how to draw support and resistance He didn t even stick around to help Harry learn how to test! Harry had to do all of the hard work by himself But the magic of it all lies in the fact that once Harry committed to obey these three laws, he never had to worry again Even if his system of support and resistance trading broke down, he would be able to fall back on the three laws and create a brand new system It is possible to support yourself from trading You can trade for a living.

DRegister #(32, 5, 0, 0) PC4 ( clock, fromStage3_PC, intoStage4_PC, srcProcessorReset, 32 b0); DRegister #(32, 5, 0, 0) IR4 ( clock, fromStage3_IR, intoStage4_IR, srcProcessorReset, 32 hF0000000); DRegister #(32, 5, 0, 0) DecodedIR4 ( clock, fromStage3_DecodedIR, intoStage4_DecodedIR, srcProcessorReset, 32 h40000000); DRegister #(32, 5, 0, 0) Z4 ( clock, fromStage3_Z, intoStage4_Z, srcProcessorReset, 32 b0);

For a tiny fraction of time, your body will continue moving in the direction of your prior motion This residual motion will be absorbed by the soft tissue of your hand, and so the collision will cause no serious harm to your body In fact, the speed you have chosen for moving in the dark was calculated by an algorithm that has been re ned by many bumps and pain in your childhood: Experience teaches us how slowly we should move under the guidance of tactile sensors in order to prevent a serious harm from possible collision Today s robot arm manipulators have no similar soft tissue to absorb forces Their bodies are made of steel or aluminum, sometimes of hard plastic If the robot body were to move under the guidance of tactile sensors, bumping into a suddenly discovered obstacle would spell a disaster.

Once the arm collided with an obstacle, it would be too late to carry out an avoiding maneuver: In nite accelerations would develop, and an accident would ensue A theoretical.

Appendix B DRegister #(32, 5, 0, 0) MD4 ( clock, fromStage3_MD, intoStage4_MD, srcProcessorReset, 32 b0);

But you have to commit to obey the laws If you were attracted to trading for a living because of the freedoms that it would offer, I applaud you We all want financial freedom, and the opportunity to plan our own days.

alternative move so slowly that those forces would be contained is simply not realistic. Then, why not use vision instead The short answer is that since an arm manipulator operates in a workspace that is comparable in size to the arm itself, vision will be less effective for motion planning than proximity sensing that covers the whole arm. By and large, humans and animals use whole-body (tactile) sensing rather than vision for motion planning at small distances to sit down comfortably in a chair, to delicately avoid an overactive next-chair neighbor on an aircraft ight, and so on. See more on this below. This discussion suggests that except for some speci c tasks that require a physical contact of the robot with other objects such as the robot assembly, where the contact occurs in the robot wrist tactile sensing is not a good sensing media for robot motion planning. Proximity sensing is a better sensing candidate for the robot sensitive skin.

DRegister #(1, 5, 0, 0) IsRegisterWrite4 ( clock, fromStage3_IsRegisterWrite, intoStage4_IsRegisterWrite, srcProcessorReset, 1 b0); DRegister #(5, 5, 0, 0) Ra4 ( clock, fromStage3_Ra, intoStage4_Ra, srcProcessorReset, 5 b0);

Ability to Measure Distances. When the robot s proximal sensor detects an obstacle that has to be dealt with to avoid collision, it is useful to know not only which point(s) of the robot body is in danger, but also how far from that spot the obstacle is. In Figure 8.4, if in addition to learning from sensor P about a nearby obstacle the arm would also know the obstacle s distance from it for example, that the obstacle is in position O and not O its collision-avoiding maneuver could be much more precise. Similar to a higher sensor resolution, an ability to measure distances to obstacles can improve the dexterity of robot motion. In mobile robots this property is common, with stereo vision and laser ranger sensors being popular choices. For robot arms, given the full coverage requirement, realizing this ability is much harder. For example, at the robot-to-obstacle distances that we are interested in, 5 to 20 cm, the time-of- ight techniques used in mobile robot sensors are hardly practical for infrared sensors: The light s time of ight is too short to detect

Figure 8.4 Knowing the distance between the robot and a potential obstacle translates into better dexterity of the arm s motion.

and spend more time with the people we love. But to obtain that freedom, you have to be willing to sacrifice. The sacrifice is worth it. I know you can, too. I hope you stay in touch and let me know how you re doing. P.S. In the Appendixes that follow, you ll find more examples of trades that Harry took in the two years after he joined Roddy and Winnipeg George s hedge fund. For even more examples, and information on the interest rates and carry trades, point your Web browser to http://www.HarryBanes.com.

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