Control of Humanoid Robots

ugliestmysticAI and Robotics

Nov 14, 2013 (3 years and 8 months ago)

66 views

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
-
body

Accelerations

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)

C

C

C

C

Grasp / Contact Matrix

Center of pressure points

Internal 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 dynamics

Forces don’t appear

Pros:


Forces appear

Compliant 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