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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)

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Control of humanoid robots
Control of Humanoid Robots

Personal robotics

Guidance of gait

12 November 2009,

UT Austin, CS Department

Luis Sentis, Ph.D.



Human centered robotics
Human-Centered Robotics Trends)

  • Human on the loop:

  • Personal / Assitive robotics (health)

  • Unmanned surveillance systems (defense / IT)

  • Modeling and guidance of human movement (health)


Current projects compliant control of humanoid robots
Current Projects: Trends)Compliant Control of Humanoid Robots


Recent project guidance of gait using functional electrical stimulation
Recent Project: Trends)Guidance of Gait Using Functional Electrical Stimulation



General control challenges
General Control Challenges Trends)

  • 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
The Problem (Interactions) Trends)

Coordination of complex skills using compliant multi-contact interactions

  • Operate efficiently under arbitrary multi-contact constraints

  • Respond compliantly to dynamic changes of the environment

  • Plan multi-contact maneuvers


Key challenges interactions
Key Challenges (Interactions) Trends)

  • 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
Approach (8 years of development) Trends)

  • Models of multi-contact and CoM interactions

  • Methodology for whole-body compliant control

  • Planners of optimal maneuvers under friction

  • Embedded control architecture


Humanoids as underactuated systems in contact
Humanoids as Underactuated Systems in Contact Trends)

  • Model-based approach: Euler-Lagrange

Non-holonomic Constraints(Underactuated DOFs)

External Forces

Torque commands

Whole-bodyAccelerations

External forces


Model of multi contact constraints
Model of multi-contact constraints Trends)

Assigning stiff model:

  • Accelerations are spanned by the contact null-space multiplied by the underactuated model:


Model of task kinematics under multi contact constraints
Model of Task Kinematics Under Multi-Contact Constraints Trends)

  • Operational point (task to joints)

qarms

  • Differential kinematics

xbase

x

  • Reduced contact-consistent Jacobian

qlegs




Aid using the virtual linkage model predict what robot can do
Aid using the virtual linkage model Trends) (predict what robot can do)

C

C

C

C

Internal tensions

Center of Mass

Center of pressure points

Grasp / Contact Matrix

Normal moments


Properties grasp contact matrix
Properties Grasp/Contact Matrix Trends)

  • Models simultaneously the internal contact state and Center of Mass inter-dependencies

  • Provides a medium to analyze feasible Center of Mass behavior

  • Emerges as an operator to plan dynamic maneuvers in 3d surfaces


Example on human motion analysis is the runner doing his best
Example on human motion analysis Trends)(is the runner doing his best?)


More details of the grasp contact matrix
More Details of the Grasp / Contact Matrix Trends)

  • Balance of forces and moments:

  • Underdetermined relationship between reaction forces and CoM behavior:

Optimal solution wrt friction forces


Example on analysis of stability regions Trends)(planning locomotion / climbing)


Approach
Approach Trends)

  • Models of multi-contact and CoM interactions

  • Methodology for whole-body compliant control

  • Planners of optimal maneuvers under friction

  • Embedded control architecture


Torque control unified force and motion control compliant control
Torque control: unified force and motion control Trends)(compliant control)

Control of the task forces (pple virtual work)

Control of the task motion

Stanford robotics / AI lab

Linear Control

Potential Fields


Inverse kinematics vs torque control
Inverse kinematics vs. torque control Trends)

Torque control:

Inverse kinematics:

duality

Pros:

Forces appear

Compliant because of dynamics

Cons:

Requires torque control

Pros:

Trajectory based

Cons:

Ignores dynamics

Forces don’t appear



Prioritized whole body torque control
Prioritized Whole-Body Torque Control Trends)

  • Prioritization (Constraints first):

  • Gradient descent is in the manifold of the constraint


Constrained consistent gradient descent
Constrained-consistent gradient descent Trends)

x un-constrained

x task

  • Constrained kinematics:

  • Optimal gradient descent:


Constrained multi objective torque control
Constrained Multi-Objective Torque Control Trends)

  • Lightweight optimization

  • Decends optimally in constrained-consistent space

  • Resolves conflicts between competing tasks





Control of internal Trends)forces

  • Manifold of closed loops

  • Unified motion / force / contact control


Compliant control of internal forces
Compliant Control of Internal Forces Trends)

  • Using previous torque control structure, estimation of contact forces, and the virtual linkage model:



Approach1
Approach Trends)

  • Models of multi-contact and CoM interactions

  • Methodology for whole-body compliant control

  • Planners of optimal maneuvers under friction

  • Embedded control architecture


Contact requisites avoid rotations and friction slides
Contact Requisites: Avoid Rotations and Friction Slides Trends)

Rotational Contact Constraints:

Need to maintain CoP in support area

C

Frictional Contact Constraints:

Need to control tensions and CoM behavior to remain in friction cones



Com control
CoM control Trends)






Approach2
Approach Trends)

  • Models of multi-contact and CoM interactions

  • Methodology for whole-body compliant control

  • Planners of optimal maneuvers under friction

  • Embedded control architecture


Demos asimo
Demos Asimo Trends)

  • Upper body compliant behaviors

  • Honda’s balance controller

  • Torque to position transformer


Summary
Summary Trends)

  • Models of multi-contact and CoM interactions

  • Methodology for whole-body compliant control

  • Planners of optimal maneuvers under friction

  • Embedded control architecture

Grasp Matrix



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