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