MIT Artificial Intelligence Laboratory — Research Directions

Legged Robots

Gill Pratt

MIT Artificial Intelligence Laboratory — Research Directions

Why Do Robot Systems Emphasize Stiff Trajectories Instead of Forces?

• Trajectories are more easily seen than forces

• Most Industrial Robot Tasks (to date) are Trajectory tasks– Painting

– Welding

• Humans, even when trying to be stiff, are soft when walking or doing other tasks

• Our Thesis: Most Natural Robotic Tasks require “low (mechanical) impedance” thinking

MIT Artificial Intelligence Laboratory — Research Directions

Can Legged Robots Work This Way?

• A: Yes! But requires that forces, as well as trajectories be considered

• First, this requires actuators that have:– Low minimum (mechanical) impedance (i.e. can be soft)

– High force fidelity + dynamic range

– Robustness to Shock

– Energy Storage

MIT Artificial Intelligence Laboratory — Research Directions

But We’re Stuck with Electric-Magnetic Actuators

• Electric actuators have decent power if run at high speed, but force/torque is low– Direct drive is too heavy for autonomous robots.

– Gears are necessary to multiply force/torque and allow the actuator to run at high speed.

– But gears introduce a number of terrible disadvantages …

MIT Artificial Intelligence Laboratory — Research Directions

Disadvantages of Gear Reduction

• N2 increase in apparent inertia for N:1 speed reduction

» Low output impedances are impossible to achieve

• Backlash / Friction » Output force control has low resolution

» Can be improved with novel gears (e.g. Artisan) but not inexpensively

• Economical Gear Reductions are intolerant to shock– Output teeth break due to single tooth contacts

– Can be improved (e.g. harmonic drive) but not inexpensively

• Poor regeneration (back-drive) efficiency

MIT Artificial Intelligence Laboratory — Research Directions

• Spring in series with motor output

• Spring converts motor position into output force

• Measure spring deflection to control output force

• Series elasticity intentionally used to obtain good force control

Motor andGearbox

Spring

Bearing

Actuatoroutput

Our Solution: Series-Elastic Actuators

MIT Artificial Intelligence Laboratory — Research Directions

Series-Elastic Actuators(Tendon Elastic)

MIT Artificial Intelligence Laboratory — Research Directions

Series-Elastic Actuators

Torsion spring

Revolute/Stiffening

Linear

Compact DC brushless

MIT Artificial Intelligence Laboratory — Research Directions

Spring Flamingo — Low Impedance

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MIT Artificial Intelligence Laboratory — Research Directions

Walking Algorithms: Motivations for the Virtual Model Control language

• (almost) all of us have good physical intuition

• (very) few of us have formal control intuition

• Passive walkers work using physical components

• Q: Can active walker algorithms be expressed using physical metaphors?

– A: Yes, and they perform surprisingly well

• Key Idea: Add Control in Parallel with natural dynamics of mechanism

MIT Artificial Intelligence Laboratory — Research Directions

Virtual Model ControlPeg-Leg 2-D Walking

• Body Height / Posture maintained via a virtual wheeled “walker”, regardless of # of legs on ground

MIT Artificial Intelligence Laboratory — Research Directions

Virtual Model ControlPeg-Leg 2-D Walking

• Speed is Controlled by “food placement” of a virtual dog-track rabbit instead of “foot placement”.

• Double-support speed control is possible only because we have good force control on each leg.

MIT Artificial Intelligence Laboratory — Research Directions

Simulated Pole-Balancing Hexapod under VMC

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MIT Artificial Intelligence Laboratory — Research Directions

“Spring Turkey” under VMC

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MIT Artificial Intelligence Laboratory — Research Directions

“Spring Flamingo” Walking Over Rough Terrain Blindly

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MIT Artificial Intelligence Laboratory — Research Directions

Informal Robustness(see papers for real numbers)

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MIT Artificial Intelligence Laboratory — Research Directions

Recent News:M2 – A Human Sized Biped Robot

12 Degrees of Freedom28 kg(62 lbs)0.97 m(38 in) hip height

MIT Artificial Intelligence Laboratory — Research Directions

M2 Hardware First Steps

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