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Control of Humanoid Robots. Personal robotics. Guidance of gait. 12 November 2009, UT Austin, CS Department. Luis Sentis, Ph.D. Assessment of Disruptive Technologies by 2025 (Global Trends). Human-Centered Robotics. Human on the loop: Personal / Assitive robotics (health) - PowerPoint PPT Presentation
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12 November 2009, UT Austin, CS Department
Control of Humanoid Robots
Luis Sentis, Ph.D.
Personal robotics Guidance of gait
Assessment of Disruptive Technologies by 2025 (Global Trends)
Human on the loop:
Personal / Assitive robotics (health) Unmanned surveillance systems (defense / IT) Modeling and guidance of human movement (health)
Human-Centered Robotics
Current Projects: Compliant Control of Humanoid Robots
Recent Project:Guidance of Gait Using Functional Electrical Stimulation
CONTROL OF HUMANOID ROBOTS
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
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
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
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
Humanoids as Underactuated Systems in Contact
Non-holonomic Constraints(Underactuated DOFs)
External forces
Model-based approach: Euler-Lagrange
Torque commands
Whole-bodyAccelerations
External Forces
Model of multi-contact constraints
Accelerations are spanned by the contact null-space multiplied by the underactuated model:
Assigning stiff model:
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)
Modeling of Internal Forces and Moments
Variables representing the contact state
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
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
Example on human motion analysis(is the runner doing his best?)
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
Example on analysis of stability regions (planning locomotion / climbing)
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
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
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:
Highly Redundant Systems Under Constraints
Prioritized Whole-Body Torque Control
Prioritization (Constraints first):
Gradient descent is in the manifold of the constraint
Constrained-consistent gradient descent
x task
Optimal gradient descent:
Constrained kinematics:
x un-constrained
Constrained Multi-Objective Torque Control
Lightweight optimization
Decends optimally in constrained-consistent space
Resolves conflicts between competing tasks
Torque control of humanoids under contact
Control of Advanced Skills
Example: Interactive Manipulation
Manifold of closed loops
Control of internal forces
Unified motion / force / contact control
Compliant Control of Internal Forces
Using previous torque control structure, estimation of contact forces, and the virtual linkage model:
Simulation results
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
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
Automatic control of CoP’s and internal forces
Motion control
CoM control
Example: CoM Oscillations
Specifications
Multiple steps: forward trajectories
Results: lateral steps
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
Demos Asimo
Upper body compliant behaviors
Honda’s balance controller
Torque to position transformer
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
PRESENTATION’S END