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Open Loop Stable Control Strategies for Robot Juggling

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Open Loop Stable Control Strategies for Robot Juggling In: IEEE International Conference on Robotics and Automation, 3, pp.913-918, Georgia, Atlanta. Open Loop Stable Control Strategies for Robot Juggling Stefan Schaal and Christopher G. Atkeson The Artificial Intelligence Laboratory and the Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, NE43-771, 545 Technology Square, Cambridge, MA 02139 Abstract: In a series of case studies out of the field of dynamic manipulation (Mason, 1992), different principles for open loop 2 Case Studies of Stable Open Loop Control stable control are introduced and analyzed. This investigation may provide some insight into how open loop control can serve as 2.1 Paddle Juggling a useful foundation for closed loop control and, particularly, what to focus on in learning control. In paddle juggling, a ball (or multiple balls) is kept in the air by hitting the ball vertically with a horizontal paddle (a 1 Introduction behavior often exhibited by tennis players waiting for a court). Under visual guidance, this is a closed loop task This paper explores open loop stable control strategies for a which has been examined by (Aboaf 1988, Bühler 1990, variety of juggling tasks. By control strategy we mean the Rizzi 1992a&b, Ballard 1989, and Toshiba 1989). Without way a movement system structures itself to approach a task. information about the ball state, only open loop control is An open loop stable control strategy does not use active re- possible. This task received considerable attention in recent action to respond to perturbations. It uses the geometry of years, for the vibrating paddle version (high paddle oscilla- the mechanical device, the kinematics and dynamics of mo- tion frequency with small amplitude) can be shown to ex- tion, and the properties of materials to stabilize the task ex- hibit period bifurcations, strange attractors, and chaos-like ecution. It is distinguished from closed loop control strate- motion (Lichtenberg & Lieberman, 1982; Guckenheimer & gies by the absence of sensory input to the computing of ac- Holmes, 1983; Moon, 1987; Tufillaro et al., 1992). In the tuator commands for error compensation. Some open loop following, open loop stable control strategies for paddle controlled devices use no actuators at all (McGeer, 1990). juggling will be explored. The emphasis lies on achieving a As has been shown by McGeer’s (1990) passive dy- constant bouncing height and period; control of the horizon- namic walking machines, dynamic systems are likely to of- tal dimensions will be neglected for the moment. The pad- fer regions in state and control space which are inherently dle mass is assumed to be much larger than the ball mass. more advantageous to the execution of a task then others. Analogously, such favorable, although not open loop, con- time trol strategies may be found in sports where, for instance, x˙ the Fosbury Flop in high jumping lifted the entire discipline k x, x˙ to new heights. If strategy largely determines the perfor- u mance of a movement task and not the details of how the k x strategy is implemented, exploitation of open loop dynam- xP,x˙ P k ics may be a promising way to find good task strategies. wk Open loop control is interesting for several reasons. t t It is an important technique for automation and can offer k k+1 cheaper and faster control for some operations. Vibratory (a) (b) feeders and sorters, and the remote center compliance de- FIGURE 1 (a) sketch of paddle juggling and notation for vice for peg in hole insertions are good examples. Open continu ous case; (b) notation for discretized case loop stable strategies may be used as a core around which closed loop control is organized. This may make closed As can easily be verified, the discrete equations of loop control more robust by reducing the demands on the motion (using the notation of Figure 1) yield: feedback controller. Finally, understanding open loop sta- 2 xÇ =− ()()1+α w−αxÇ−2gu , ble strategies may aid in understanding the key features to k+1 k k k = + be learned by a robot practicing a task. xk+1 xk uk , (1) In section 2 several juggling tasks will be discussed = 1 ()+ α −α+ to illuminate issues of open loop control and their implica - tk+1 ()()1 wk xÇkxÇk+1. tions. We believe that searching for open loop stability, or g at least something which comes close to that, may help the ( xÇk , xk ) denote the velocity and the position, respectively, system to substantially reduce control effort, improve per- of the ball just before the moment of impact. The velocity formance and speed up learning. Discussions of the case of the paddle at this time is (wk ) . After the impact, the studies are largely deferred to section 3. Each case study paddle shifts its position by the distance (u ) where the has been explored by building an actual machine. k next impact (k+1) will take place. Energy loss during the (Figure 3a) demonstrate the feasibility of this open loop impact is modeled with a coefficient of restitution (α). control method (Figure 3b and 3c). A special trampoline- Is there an open loop control strategy which would like racket and a ping-pong ball as juggling object even al- achieve a stable, simple (one impact per cycle) juggling low open loop stability in the horizontal plane: this racket pattern? It turns out that a sinusoidal driving motion, exerts a restoring force toward the racket center if the ball = ω + θ xP Asin( t 0 ), as chosen in nearly all open loop lands off-center. Data was recorded with a vision system studies, suffices to obtain stability. Appropriately relating running at 60 Hz; parameters of the paddle movement were τ = = α (wk ,uk ) to the sine motion results in the following condi- 0.4sec, A 0.05m, =0.51. tion for stable fixed points of period (τ), where τ = 2π / ω : π g 1− α θ = arccos (2) 0 Aw2 1+ α θ This condition was formulated for the phase ( 0 ), assum- M ing the motion of the paddle is constant and the ball has to find the impact phase where a stable periodic motion exists; (a) impact velocity is solely determined by the ballistic flight of the ball of duration (τ). 90 85 x x 80 P B 75 70 65 0 60 0 12 55 Amplitude 50 45 40 35 0 (b) 30 10 11 12 13 0 12 Time [sec] 100 95 x x 90 P B 85 80 0 75 0 12 70 65 Amplitude 60 Paddle, Ball Vertical Pos. Time 55 50 FIGURE 2 Simulation of effect of hit trajectory on open loop 45 stabil ity: (top) positively accelerating hit trajectory; (middle) con- (c) 40 stant velocity hit trajectory; (bottom) negatively accelerating hit tra- 10 11 12 13 jectory Time [sec] FIGURE 3 (a) setup of juggling robot; (b) period-one juggling The essence of this open loop stability lies in what motion; (c) period-two juggling motion could be called a focusing hit trajectory (Schaal et al. 1992). As depicted in the simulations of Figure 2, only hit It is possible to estimate the size of the basin of at- trajectories which are negatively accelerating at impact trac tion of steady paddle juggling. The system has a trap- while the position is still increasing accomplish this stabil- ping region for all initial velocities of the ball yielding: ity. The middle row of Figure 2 shows the effect of a con- + + α ≤ 2 gA (1 ) Aw stant velocity hit trajectory on a set of ball trajectories with xÇk =0 (3) α −1 a range of initial velocities. Both the paddle and ball verti- cal positions are plotted against time. Due to the neutral sta- The derivation of this bound is similar to Tufillaro bility of the constant velocity hit trajectory the trajectories et al. (1992) and had to be omitted due to space limitations. diverge at a rate that is linear in time. The top row of Figure Knowing the trapping region, it is sufficient to investigate 2 shows the exponential divergence due to a positively ac- the basin of attraction only in this region, which is illus- celerating hit trajectory for a set of ball trajectories with a trated numerically in Figure 4 for the paddle motion param- tenth of the range of initial velocities used in the constant eters given above. The gray areas denote initial conditions velocity case. The bottom row of Figure 2 shows the focus- belonging to the basin of attraction for periodic juggling, ing effect of a negatively accelerating hit trajectory for a the white areas physically impossible initial conditions, and wide range of initial ball velocities. Interestingly, in all the the black areas initial conditions which did not lead to peri- literature on closed loop ball juggling this control strategy odic juggling (Tufillaro et al., 1992). has not been applied. The numerical calculation for Figure 4 assesses the Two runs of a simple one-joint robot using a panto- size of the basin of attraction as 0.257 of the trapping re- graph linkage to maintain a horizontal paddle orientation gion, corresponding to a probability P=0.257 that the ball 2 ends up in periodic juggling if it was initially dropped from Driving the wedge with a sinusoidal motion and try- a large enough height. In the real robot, random effects dur- ing to rely on the principle of self-focusing trajectories does ing bouncing as well as unmodeled parameters (like air re- not suffice to find an open loop control law. From the anal- sistance for the ping-pong ball) made the basin of attraction ysis of the conservative, non-oscillating ball-in-a-wedge, significantly larger.
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