Control of Humanoid Robots
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Transcript of Control of Humanoid Robots
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12 November 2009, UT Austin, CS Department
Control of Humanoid Robots
Luis Sentis, Ph.D.
Personal robotics Guidance of gait
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Assessment of Disruptive Technologies by 2025 (Global Trends)
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Human on the loop:
Personal / Assitive robotics (health) Unmanned surveillance systems (defense / IT) Modeling and guidance of human movement (health)
Human-Centered Robotics
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Current Projects: Compliant Control of Humanoid Robots
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Recent Project:Guidance of Gait Using Functional Electrical Stimulation
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CONTROL OF HUMANOID ROBOTS
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General Control Challenges
Dexterity: How can we create and execute advanced skills that coordinate motion, force, and compliant multi-contact behaviors
Interaction: How can we model and respond to the constrained physical interactions associated with human environments?
Autonomy: How can we create action primitives that encapsulate advance skills and interface them with high level planners
PARKOUR
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The Problem (Interactions)
Operate efficiently under arbitrary multi-contact constraints
Respond compliantly to dynamic changes of the environment
Plan multi-contact maneuvers
Coordination of complex skills using compliant multi-contact interactions
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Key Challenges (Interactions)
Find representations of the robot internal contact state
Express contact dependencies with respect to frictional properties of contact surfaces
Develop controllers that can generate compliant whole-body skills
Plan feasible multi-contact behaviors
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Approach (8 years of development)
1. Models of multi-contact and CoM interactions
2. Methodology for whole-body compliant control
3. Planners of optimal maneuvers under friction
4. Embedded control architecture
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Humanoids as Underactuated Systems in Contact
Non-holonomic Constraints(Underactuated DOFs)
External forces
Model-based approach: Euler-Lagrange
Torque commands
Whole-bodyAccelerations
External Forces
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Model of multi-contact constraints
Accelerations are spanned by the contact null-space multiplied by the underactuated model:
Assigning stiff model:
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Model of Task Kinematics Under Multi-Contact Constraints
x
q legs
Reduced contact-consistent Jacobian
x base
q arms Differential kinematics
Operational point (task to joints)
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Modeling of Internal Forces and Moments
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Variables representing the contact state
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Aid using the virtual linkage model (predict what robot can do)
CC
C
C
Grasp / Contact Matrix
Center of pressure pointsInternal tensions
Center of Mass
Normal moments
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Properties Grasp/Contact Matrix
1. Models simultaneously the internal contact state and Center of Mass inter-dependencies
2. Provides a medium to analyze feasible Center of Mass behavior
3. Emerges as an operator to plan dynamic maneuvers in 3d surfaces
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Example on human motion analysis(is the runner doing his best?)
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More Details of the Grasp / Contact Matrix
Balance of forces and moments:
Underdetermined relationship between reaction forces and CoM behavior:
Optimal solution wrt friction forces
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Example on analysis of stability regions (planning locomotion / climbing)
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Approach
1. Models of multi-contact and CoM interactions
2. Methodology for whole-body compliant control
3. Planners of optimal maneuvers under friction
4. Embedded control architecture
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Linear Control
Stanford robotics / AI lab
Torque control: unified force and motion control(compliant control)
Control of the task forces (pple virtual work)
Control of the task motion
Potential Fields
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Inverse kinematics vs. torque control
duality
Pros:
Trajectory based
Cons:
Ignores dynamicsForces don’t appear
Pros:
Forces appearCompliant because of dynamics
Cons:
Requires torque control
Inverse kinematics: Torque control:
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Highly Redundant Systems Under Constraints
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Prioritized Whole-Body Torque Control
Prioritization (Constraints first):
Gradient descent is in the manifold of the constraint
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Constrained-consistent gradient descent
x task
Optimal gradient descent:
Constrained kinematics:
x un-constrained
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Constrained Multi-Objective Torque Control
Lightweight optimization
Decends optimally in constrained-consistent space
Resolves conflicts between competing tasks
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Torque control of humanoids under contact
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Control of Advanced Skills
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Example: Interactive Manipulation
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Manifold of closed loops
Control of internal forces
Unified motion / force / contact control
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Compliant Control of Internal Forces
Using previous torque control structure, estimation of contact forces, and the virtual linkage model:
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Simulation results
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Approach
1. Models of multi-contact and CoM interactions
2. Methodology for whole-body compliant control
3. Planners of optimal maneuvers under friction
4. Embedded control architecture
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Contact Requisites: Avoid Rotations and Friction Slides
C Rotational Contact Constraints: Need to maintain CoP in support area
Frictional Contact Constraints: Need to control tensions and CoM behavior to remain in friction cones
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Automatic control of CoP’s and internal forces
Motion control
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CoM control
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Example: CoM Oscillations
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Specifications
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Multiple steps: forward trajectories
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Results: lateral steps
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Approach
1. Models of multi-contact and CoM interactions
2. Methodology for whole-body compliant control
3. Planners of optimal maneuvers under friction
4. Embedded control architecture
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Demos Asimo
Upper body compliant behaviors
Honda’s balance controller
Torque to position transformer
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Summary
Grasp Matrix
1. Models of multi-contact and CoM interactions
2. Methodology for whole-body compliant control
3. Planners of optimal maneuvers under friction
4. Embedded control architecture
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PRESENTATION’S END
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