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

Biomechatronics facility Emerging sensors Analysis of Human Gait (a) (b) (c) (d) Human-Centered Robotics Laboratory

Human Centered Robotics Today

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)

Contact Center of Pressures (CoPs) C  Balance of moments on support links

Dependency CoP’s – ZMP

Dependency CoP’s – ZMP (Coplanar Stance Only)  Relationship CoP’s - ZMP  Dependencies

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

Lateral Walk: CoM and CoP Trajectoriy Generation

Specifications

Multiple steps: forward trajectories

Multiple steps: lateral trajectories

Results: lateral steps

Dynamic Walk

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

Cognitive architecture

Demos Asimo  Upper body compliant behaviors  Honda’s balance controller  Torque to position transformer

Manipulation tests

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