Control of Humanoid Robots

Luis Sentis, Ph.D.

Personal robotics Guidance of gait

12 November 2009, UT Austin, CS Department

Assessment of Disruptive Technologies by 2025 (Global Trends)

Human-Centered Robotics 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

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

interactions associated with human environments?

physical 

Autonomy: skills

How can we create

action primitives

that encapsulate advance and interface them with high level planners

PARKOUR

The Problem (Interactions)

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)

 Find

representations

of the robot internal contact state  Express contact dependencies with respect to

frictional

of contact surfaces properties  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

 Model-based approach: Euler-Lagrange Non-holonomic Constraints (Underactuated DOFs) Whole-body Accelerations External Forces Torque commands External forces

Model of multi-contact constraints

Assigning stiff model:  Accelerations are spanned by the contact null-space multiplied by the underactuated model:

Model of Task Kinematics Under Multi-Contact Constraints

 Operational point (task to joints)

x

base

q

arms

x q

legs  Differential kinematics  Reduced contact-consistent Jacobian

Modeling of Internal Forces and Moments

Variables representing the contact state

Aid using the virtual linkage model (predict what robot can do) Internal tensions Center of pressure points Center of Mass C C C C Grasp / Contact Matrix 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

Torque control: unified force and motion control (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: Torque control: duality Pros: Trajectory based Cons: Ignores dynamics Forces don’t appear Pros: Forces appear Compliant because of dynamics Cons: Requires 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

un-constrained

x

task  Constrained kinematics:  Optimal gradient descent:

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

Control of internal forces

 Manifold of closed loops  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

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

Grasp Matrix

PRESENTATION’S END