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Anima Anandkumar INFUSING PHYSICS + STRUCTURE INTO MACHINE LEARNING

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Page 1: INFUSING PHYSICS + STRUCTURE INTO MACHINE LEARNING...PHYSICS-INFUSED LEARNING FOR ROBOTICS AND CONTROL Data Priors Learning = Computational Reasoning over Data & Priors Learning =

Anima Anandkumar

INFUSING PHYSICS + STRUCTURE INTO MACHINE LEARNING

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TRINITY OF AI/ML

DATACOMPUTE

ALGORITHMS

Page 3: INFUSING PHYSICS + STRUCTURE INTO MACHINE LEARNING...PHYSICS-INFUSED LEARNING FOR ROBOTICS AND CONTROL Data Priors Learning = Computational Reasoning over Data & Priors Learning =

PHYSICS-INFUSED LEARNING

Data Priors

Learning = Computational Reasoning over Data & Priors

+Learning =

How to use Physics as a (new) form of Prior?• Learnability

• Generalization

Page 4: INFUSING PHYSICS + STRUCTURE INTO MACHINE LEARNING...PHYSICS-INFUSED LEARNING FOR ROBOTICS AND CONTROL Data Priors Learning = Computational Reasoning over Data & Priors Learning =

4

What are some of the hardest

challenges for AI?

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“ Mens sana in corpore sano.”

Juvenal in Satire X.

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ExplorersPlanetary, Underwater, and Space Explorers

TransformersSwarms of Robots Transforming Shapes and Functions

GuardiansDynamic Event Monitors and First Responders

TransportersRobotic Flying Ambulances and Delivery Drones

PartnersRobots Helping and Entertaining People

ROBOTICSMOONSHOTS

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MIND & BODYNEXT-GENERATION AI

Fine-grained reactive Control

Instinctive:Making and adapting plans

Deliberative:

Sense and react to human

Behavioral:Acting for the greater good

Multi-Agent:

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PHYSICS-INFUSED LEARNING FOR ROBOTICS AND CONTROL

Data Priors

Learning = Computational Reasoning over Data & Priors

+Learning =

How to use Physics as a (new) form of Prior?• Learnability

• Generalization

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9

BASELINE: MODEL-BASED CONTROL (NO LEARNING)

𝑠𝑡+1 = 𝐹 𝑠𝑡 , 𝑢𝑡 + 𝜖

New State

Current State

Current Action (aka control input)

Unmodeled Disturbance / Error

Robust Control (fancy contraction mappings)

• Stability guarantees (e.g., Lyapunov)

• Precision/optimality depends on error

(Value Iteration is also contraction mapping)

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LEARNING RESIDUAL DYNAMICS FOR DRONE LANDING

𝑠𝑡+1 = 𝑓 𝑠𝑡 , 𝑎𝑡 + ሚ𝑓 𝑠𝑡 , 𝑎𝑡 + 𝜖

New State

Current State

Current Action (aka control input)

Unmodeled Disturbance

𝑓 = nominal dynamicsሚ𝑓 = learned dynamics

Use existing control methods to generate actions

• Provably robust (even using deep learning)

• Requires ሚ𝑓 Lipschitz & bounded error

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CONTROL SYSTEM FORMULATION

• Dynamics:

• Control:

• Unknown forces & moments:

Learn the Residual(function of state and control input)

Learn the Residual

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12

DATA COLLECTION (MANUAL EXPLORATION)

• Learn ground effect:

• (s,u): height, velocity, attitude and four control inputs

෨𝐹 𝑠, 𝑢 →

Spectral-Normalized4-Layer Feed-Forward

Spectral Normalization: Ensures ෩𝑭 is Lipshitz[Bartlett et al., NeurIPS 2017][Miyato et al., ICLR 2018]

Ongoing Research:

Safe Exploration

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13

PREDICTION RESULTS

Height (m)

Gro

un

d E

ffe

ct (N

)

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GENERALIZATION PERFORMANCE ON DRONE

Our Model Baseline

Heig

ht

Vertical Velocity

Spectral Regularized NN Conventional NN

Neural Lander: Stable Drone Landing Control using Learned Dynamics, ICRA 2019

Guanya

ShiXichen

ShiMichael

O’Connell

Kamyar

Azizzadenesheli

Rose

YuSoon-Jo

Chung

Yisong

Yue

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CONTROLLER DESIGN (SIMPLIFIED)

Nonlinear Feedback Linearization:

Cancel out ground effect ෨𝐹(𝑠, 𝑢𝑜𝑙𝑑):

𝑢𝑛𝑜𝑚𝑖𝑛𝑎𝑙 = 𝐾𝑠𝜂

Feedback Linearization (PD control)

𝜂 =𝑝 − 𝑝∗

𝑣 − 𝑣∗Desired Trajectory(tracking error)

𝑢 = 𝑢𝑛𝑜𝑚𝑖𝑛𝑎𝑙 + 𝑢𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙Requires Lipschitz & small time delay

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STABILITY GUARANTEES

Assumptions:

Desired states along position trajectory bounded

Control updates faster than state dynamics

Learning error bounded (new): Bounded Lipschitz (through spectral normalization of layers

Stability Guarantee:(simplified)

𝜂(t) ≤ 𝜂(0) exp𝜆 − ෨𝐿𝜌

𝐶𝑡 +

𝜖

𝜆 − ෨𝐿𝜌

⟹ 𝜂(t) →𝜖

𝜆𝑚𝑖𝑛 𝐾 − ෨𝐿𝜌

Exponentially fast

Unmodeled disturbance

Lipschitz of NN

Time delaycontrol gain

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CAST @ CALTECHLEARNING TO LAND

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TESTING TRAJECTORY TRACKING

Move around a circle super close to the ground

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CAST @ CALTECHDRONE WIND TESTING LAB

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TAKEAWAYS

Control methods => analytic guarantees

Blend w/ learning => improve precision/flexibility

Preserve side guarantees

Sometimes interpret as functional regularization

(side guarantees)

(possibly relaxed)

(speeds up learning)

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Blending Data Driven Learning

with Symbolic Reasoning

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AGE-OLD DEBATE IN AI

Symbolic reasoning:

• Humans have impressive ability at symbolic reasoning

• Compositional: can draw complex inferences from simple axioms.

Representation learning:

• Data driven: Do not need to know the base concepts

• Black box and not compositional: cannot easily combine and create more complex systems.

Symbols vs. Representations

Combining Symbolic Expressions & Black-box Function Evaluations in Neural Programs, ICLR 2018

ForoughArabshahi

SameerSingh

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SYMBOLIC + NUMERICAL INPUT

Goal: Learn a domain of functions (sin, cos, log…)

Training on numerical input-output does not generalize.

Data Augmentation with Symbolic Expressions

Efficiently encode relationships between functions.

Solution:

Design networks to use both

symbolic + numeric

Leverage the observed structure of the data

Hierarchical expressions

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TASKS CONSIDERED

◎Mathematical equation verification○ sin2 𝜃 + cos2 𝜃 = 1 ???

◎Mathematical question answering ○ sin2 𝜃 +

2= 1

◎Solving differential equations

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EXPLOITING HIERARCHICAL REPRESENTATIONS

Symbolic expression Function Evaluation Data Point Number Encoding Data Point

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REPRESENTING MATHEMATICAL EQUATIONS

◎Grammar rules

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DOMAIN

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TREE-LSTM FOR CAPTURING HIERARCHIES

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DATASET GENERATION

◎Random local changes

Replace Node Shrink Node Expand Node

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DATASET GENERATION

◎Sub-tree matching

Dictionary key-value pairChoose Node Replace with value’s pattern

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Majority Class Sympy LSTM : sym TreeLSTM : sym TreeLSTM:sym+num

Generalization 50.24% 81.74% 81.71% 95.18% 97.20%

Extrapolation 44.78% 71.93% 76.40% 93.27% 96.17%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%A

CC

UR

AC

Y

EQUATION VERIFICATION

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EQUATION COMPLETION

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EQUATION COMPLETION

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TAKE-AWAYS

Vastly Improved numerical evaluation: 90% over function-fitting baseline.

Generalization to verifying symbolic equations of higher depth

Combining symbolic + numerical data helps in better generalization for both tasks: symbolic and numerical evaluation.

LSTM: Symbolic TreeLSTM: Symbolic TreeLSTM: symbolic + numeric

76.40 % 93.27 % 96.17 %

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TENSORS PLAY A CENTRAL ROLE

DATACOMPUTE

ALGORITHMS

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TENSOR : EXTENSION OF MATRIX

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TENSORS FOR DATAENCODE MULTI-DIMENSIONALITY

Image: 3 dimensionsWidth * Height * Channels

Video: 4 dimensionsWidth * Height * Channels * Time

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TENSORS FOR MODELS

STANDARD CNN USE LINEAR ALGEBRA

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Jean Kossaifi, Zack Chase Lipton, Aran Khanna, Tommaso Furlanello, A

Jupyters notebook: https://github.com/JeanKossaifi/tensorly-notebooks

TENSORS FOR MODELS

TENSORIZED NEURAL NETWORKS

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SPACE SAVING IN DEEP TENSORIZED NETWORKS

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T E N S O R L Y : H I G H - L E V E L A P I F O R T E N S O R A L G E B R A

• Python programming

• User-friendly API

• Multiple backends: flexible + scalable

• Example notebooks

Jean Kossaifi

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42© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

TENSORLY WITH PYTORCH BACKEND

import tensorly as tl

from tensorly.random import tucker_tensor

tl.set_backend(‘pytorch’)

core, factors = tucker_tensor((5, 5, 5),

rank=(3, 3, 3))

core = Variable(core, requires_grad=True)

factors = [Variable(f, requires_grad=True) for f in factors]

optimiser = torch.optim.Adam([core]+factors, lr=lr)

for i in range(1, n_iter):

optimiser.zero_grad()

rec = tucker_to_tensor(core, factors)

loss = (rec - tensor).pow(2).sum()

for f in factors:

loss = loss + 0.01*f.pow(2).sum()

loss.backward()

optimiser.step()

Set Pytorch backend

Attach gradients

Set optimizer

Tucker Tensor form

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AIREVOLUTIONIZING MANUFACTURING AND LOGISTICS

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NVIDIA ISAAC —WHERE ROBOTS GO TO LEARN

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Cars Pedestrians Path

Lanes Signs Lights

Cars Pedestrians Path

Lanes Signs Lights

1. COLLECT & PROCESS DATA 2. TRAIN MODELS

3. SIMULATE 4. DRIVE

NVIDIA DRIVEFROM TRAINING TO SAFETY

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TAKEAWAYS

End-to-end learning from scratch is impossible in most settings

Blend DL w/ prior knowledge => improve data efficiency, generalization, model size

Obtain side guarantees like stability + safety,

Outstanding challenge (application dependent):

what is right blend of prior knowledge vs data?

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Thank you


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