MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Fabrication MURI Low-Level Control High-Level Control What strategies are used in insect locomotion and what are their implications? Insect locomotion studies (Berkeley Bio) New measurement capabilities (Stanford) What motor control adaptation strategies do people use and how can they be applied to robots? Learning and Compliance Strategies for Unstructured Environments (Harvard & Johns Hopkins) Implications for biomimetic robots (Harvard, Johns Hopkins, Stanford) Guiding questions

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Biological Motor Control Mechanical Higher Centers Environment aero-, hydro-, terra-dynamic Feedforward Controller (CPG) Adaptive Controller Sensors Closed-loop Open-loop System (Actuators, limbs) Feedback Controller Sensors Behavior Preflexes

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Human Arm Model Simplifies experiments –Excellent adaptability –Instructable subjects –Simple apparatus Manipulation application –Role of impedance less understood

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, 2000 Learning Impedance Strategies in Unstructured Environments Robert D. Howe and Yoky Matsuoka Division of Engineering and Applied Sciences Harvard University

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Contribution to Control Feedforward Intrinsic musculo-skeletal properties Preflex Motor program acting through moment arms Passive Dynamic Self-stabilization Mechanical System PredictiveRapid acting Neural System Reflex Active Stabilization Neural feedback loops Slow acting

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Impedance “preflex” can produce robust behavior (Full) Preflexes are tailored to specific tasks and environments Goal: Understand relationship between impedance value and task/environment Approach: Measure impedances and adaptation strategies in realistic settings Understanding Impedance Change over Time

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Develop a system that identifies impedance during execution of various tasks. –Virtual Environment Characterize impedance change over time –“Instantaneous” identification technique Investigate impedance adaptation characteristics –How do humans adapt to a required impedance for the task? –What is the initial strategy for a novel task? –What does the initial strategy depend on? Experimental Technique

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Assumptions for System Identification Work with the end-point impedance Represent hand as a linear, second- order system. k b m F Parameter identification is easy for time- invariant systems - Assume constant m,b, and k - Apply perturbation, repeat, and average.

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Previous System Identification Technique for Time Varying Systems Time varying system –Requires multiple perturbations for each data point. –PRBS (Bennett et al. 1992, Lacquaniti et al. 1993) –Repetition hides learning –Single task

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Creating a Task-Based Environment Use a PHANToM haptic interface (3 DOF) to apply task-based force feedback Permits software control of task parameters Use force and acceleration sensors near the hand. Handle & accelerometer Force sensor Robot

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Data Acquisition System (10kHz) Processor (servo loop 1kHz) Human Subject Controller Interactions Computer Monitor (30Hz) Handle & accelerometer Force sensor Robot Motors and Encoders Virtual Environment Dynamic Simulation

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Using Impulse-Based “Instantaneous” System Identification Use task-based force feedback if task interaction is impulse like Use added impulse force perturbation otherwise Identify within 40 msec, prior to CNS involvement: - prefelexes only Assume passive impedance is constant during 40 msec identification window

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Example Task: Bouncing a ball to a target height Handle corresponds to the paddle on the monitor Before During After VIEW ON MONITOR: Ball drops too quickly for visual reaction: bounce height set by hand impedance

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Contact Task Model: Three Stages in Bouncing a Ball Stage 1: Ball falling (before contact) –given Stage 2: During contact Stage 3: Ball rising (after contact) –given ball

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Task-Based Impulsive Force Time (msec) Force (N) (measured) Accel. (m/s^2) (measured) Velocity (m/s) Position (mm) Using the virtual environment contact task force

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Confirmation of the Technique F m*a k*x b*v Time (msec) Least Square Fit r = (mean) Accuracy –mass+/- 8.1 % –spring +/- 2.5% Force (N)

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Contact Task Experimental Design 1 Used wrist movement to bounce the ball up to a target height Impedance too high bounces too high Impedance too low bounces too low Visual feedback of success/ failure each trial Six sets of 40 bounces Group1: low, high, …, low, high; Group2: high, low, …, high, low

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Group2 (high, low) Group1 (low, high) Typical Stiffness Learning Curve (n=1) K (N/m) trials Low Target High Target

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Group2 (high, low) Group1 (low, high) Typical Damping Learning Curve (n=1) B (N.s/m) trials High Target Low Target

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, First Exposure Second Exposure First versus Second Exposure to the Task (n=5) K (N/m) High Target Low Target trials A= A= - 0.3

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Stiffness Adaptation Characteristics for Contact Task Experiment Initial stiffness: same (~200 N/m) regardless of target impedance Final stiffness: tuned to target (range N/m) Learning follows exponential curve Adaptation is faster for the second exposure (for high target impedance) - First exposure: A= Second exposure: A= - 0.3

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Precision Limitations K(N/m) B(Ns/m) K(N/m) B(Ns/m) NARROWING TARGET STIFFNESS NARROWING TARGET DAMPING

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Average Stiffness % Success rate with narrowing window Precision limitations Average Damping NARROWING TARGET WINDOW

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Position Task Experimental Design Handle corresponds to the paddle on the computer screen Used wrist movement to track a moving ball (const. velocity). Game over if ball dropped. 5 continuous minutes of recording with added perturbations. Group1: Large paddle; Group2: Small paddle

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Stiffness Adaptation in Position Task (n=5) Group 1: Large paddle Group 2: Small paddle K(N/m) time (1/10 min)

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Stiffness Adaptation Characteristics for Position Task Experiment Initial stiffness: same (~740 N/m) regardless of paddle size. Amplitude of initial stiffness different for position and contact tasks. Final stiffness: for the easier task, stiffness dropped lower and faster. Group1: Large paddle Group2: Small paddle

MURI High-Level Control Biomimetic Robots - ONR Site Visit - August 9, Summary Developed new experimental technique - “instantaneous” impedance measurement permits examination of learning and adaptation - virtual environment allows easy examination of a wide range of tasks Initial strategy depends on the overall task Final strategy depends on the environmental parameters Damping cannot be independently controlled