CS223A - Introduction to Robotics-lecture13
Topics: Control - Overview, Joint Space Control, Resolved Motion Rate Control, Natural Systems, Dissipative Systems, Example, Passive System Stability Instructor (Oussama Khatib):Okay. Let’s get started. So today’s video is about dampening. This is from Vancouver Tech and it was presented as ISRR, International Symposium of Robotic Research in ’93. [Video playing] At the University of Michigan Robotics Laboratory, we’re interested in tasks involving dynamically dexterous interaction between robots and their environments. Computers currently play chess better that all but a few of the best human experts. But no machine has yet been built that can manipulate the physical pieces with anywhere near the skill and reliability of the youngest human chess novice. Our three degree of freedom direct drive robot is endowed with a juggling algorithm that transforms the positions and velocities of a falling ball into desired joint positions and velocities, which the robot is forced to track by use of a nonlinear inverse dynamics controller. Smooth position and velocity estimates are produced by a linear observer, which in turn, receives input from a real-time stereo vision system. The one juggle tasks requires the machine to bat a single ball into a stable periodic trajectory, passing through a user-specified apex. Adding a second ball with an independently specified apex point defines a two-juggle task. The juggling algorithm shown here employs an urgency measure to switch the machine’s interest between the reference command corresponding to the two independent one juggles. It’s worth emphasizing that there is no planning in the conventional sense taking place in this system. Rather, the robot’s impact decisions are induced by its continuous motions in the effort to track a carefully distorted version of the positions and velocities of the two balls. Machine juggling skills, in and of themselves, seem unlikely to play a direct role in the social and economic impact of advanced robotics. However, we are convinced that the problems of controlling contexts, focusing visual attention, and coordinating in real time the constituent behaviors of such skills provides an invaluable laboratory for understanding what is hard about dynamical dexterity. Without a phase regulation control term, the balls quickly wander in phase and eventually fall in simultaneously. In contrast, with phrase regulation again enabled, nearly simultaneous falling balls are successfully separated. In this experiment, we failed to prevent a spatial collision. We hope in the future to better understand the nature of these and other dynamical obstacles, or order to control around them more effectively. Of course, there will always be situations from which the machine cannot recover. Instructor (Oussama Khatib):Okay. So who’s interested in juggling? Well, those who are interested in juggling could try it next quarter in Experimental Robotics. In fact, a lot of the projects in Experimental Robotics involve dynamic skills, throwing a ball into a basket, playing ping-pong, or whatever. So juggling is quite challenging, actually. Well, juggling requires control and here we are. So this is a little bit of a concept that we are going to see over the discussions on control. And the concept is instead of really thinking about the robot as a programmable machine where you need to find all the join motions corresponding to your task. So you want to move to some location and you want to be able to reach that location with some orientation of your vector. Well, basically, what you have to do is you have to solve this inverse kinematic problem to find the joint angles that would allow you to be in that configuration. I’m not sure if a human can do that. Humans usually are really poor at computation, so finding the inverse kinematics, finding all the joint angles that will put you in that final configuration is really difficult. So what do you think humans do? Student:[Inaudible]. Instructor (Oussama Khatib):Feedback of what? Student:[Inaudible]. Instructor (Oussama Khatib):So you sort of like think – try to reach for something. Try to reach for the chair in front of you. How do you do it? So you’re looking at your hand and you look at the chair and you have this visual feedback. So it’s sort of like your hand is attracted by a force pulling you toward that goal position you describe. And this is the concept you see here. It’s sort of like potential energy, where the minimum of this potential energy is located at the goal position. And that is going to create a force pulling your hand toward the goal. Your hand is going to just move toward this goal without a priori imaging or knowing where your final configuration is going to be. The final configuration is going to emerge from your motion. We will come to this later. But this kind of idea is really what we call task oriented or operation space control. Th
Topics: Control - Overview, Joint Space Control, Resolved Motion Rate Control, Natural Systems, Dissipative Systems, Example, Passive System Stability Instructor (Oussama Khatib):Okay. Let’s get started. So today’s video is about dampening. This is from Vancouver Tech and it was presented as ISRR, International Symposium of Robotic Research in ’93. [Video playing] At the University of Michigan Robotics Laboratory, we’re interested in tasks involving dynamically dexterous interaction between robots and their environments. Computers currently play chess better that all but a few of the best human experts. But no machine has yet been built that can manipulate the physical pieces with anywhere near the skill and reliability of the youngest human chess novice. Our three degree of freedom direct drive robot is endowed with a juggling algorithm that transforms the positions and velocities of a falling ball into desired joint positions and velocities, which the robot is forced to track by use of a nonlinear inverse dynamics controller. Smooth position and velocity estimates are produced by a linear observer, which in turn, receives input from a real-time stereo vision system. The one juggle tasks requires the machine to bat a single ball into a stable periodic trajectory, passing through a user-specified apex. Adding a second ball with an independently specified apex point defines a two-juggle task. The juggling algorithm shown here employs an urgency measure to switch the machine’s interest between the reference command corresponding to the two independent one juggles. It’s worth emphasizing that there is no planning in the conventional sense taking place in this system. Rather, the robot’s impact decisions are induced by its continuous motions in the effort to track a carefully distorted version of the positions and velocities of the two balls. Machine juggling skills, in and of themselves, seem unlikely to play a direct role in the social and economic impact of advanced robotics. However, we are convinced that the problems of controlling contexts, focusing visual attention, and coordinating in real time the constituent behaviors of such skills provides an invaluable laboratory for understanding what is hard about dynamical dexterity. Without a phase regulation control term, the balls quickly wander in phase and eventually fall in simultaneously. In contrast, with phrase regulation again enabled, nearly simultaneous falling balls are successfully separated. In this experiment, we failed to prevent a spatial collision. We hope in the future to better understand the nature of these and other dynamical obstacles, or order to control around them more effectively. Of course, there will always be situations from which the machine cannot recover. Instructor (Oussama Khatib):Okay. So who’s interested in juggling? Well, those who are interested in juggling could try it next quarter in Experimental Robotics. In fact, a lot of the projects in Experimental Robotics involve dynamic skills, throwing a ball into a basket, playing ping-pong, or whatever. So juggling is quite challenging, actually. Well, juggling requires control and here we are. So this is a little bit of a concept that we are going to see over the discussions on control. And the concept is instead of really thinking about the robot as a programmable machine where you need to find all the join motions corresponding to your task. So you want to move to some location and you want to be able to reach that location with some orientation of your vector. Well, basically, what you have to do is you have to solve this inverse kinematic problem to find the joint angles that would allow you to be in that configuration. I’m not sure if a human can do that. Humans usually are really poor at computation, so finding the inverse kinematics, finding all the joint angles that will put you in that final configuration is really difficult. So what do you think humans do? Student:[Inaudible]. Instructor (Oussama Khatib):Feedback of what? Student:[Inaudible]. Instructor (Oussama Khatib):So you sort of like think – try to reach for something. Try to reach for the chair in front of you. How do you do it? So you’re looking at your hand and you look at the chair and you have this visual feedback. So it’s sort of like your hand is attracted by a force pulling you toward that goal position you describe. And this is the concept you see here. It’s sort of like potential energy, where the minimum of this potential energy is located at the goal position. And that is going to create a force pulling your hand toward the goal. Your hand is going to just move toward this goal without a priori imaging or knowing where your final configuration is going to be. The final configuration is going to emerge from your motion. We will come to this later. But this kind of idea is really what we call task oriented or operation space control. Th