Retrospectives on Scaling Robot Learning

Andy Zeng

MIT CSL Seminar

"Dirty Laundry"

Symptoms of a larger problem

"Dirty Laundry"

Symptoms of a larger problem

The not-so-secret recipe to making a rockstar behavior cloning demo on real robots

"Dirty Laundry"

Symptoms of a larger problem

The not-so-secret recipe to making a rockstar behavior cloning demo on real robots

Step 1. collect your own "expert" data and don't trust anyone else to make it perfect

"Dirty Laundry"

Symptoms of a larger problem

The not-so-secret recipe to making a rockstar behavior cloning demo on real robots

Step 1. collect your own "expert" data and don't trust anyone else to make it perfect

Step 2. avoid "no action" data so your policy doesn’t just sit there

"Dirty Laundry"

Symptoms of a larger problem

The not-so-secret recipe to making a rockstar behavior cloning demo on real robots

Step 1. collect your own "expert" data and don't trust anyone else to make it perfect

Step 2. avoid "no action" data so your policy doesn’t just sit there

Step 3. It’s not working? Collect more data until "extrapolation" becomes "interpolation"

"Dirty Laundry"

Symptoms of a larger problem

The not-so-secret recipe to making a rockstar behavior cloning demo on real robots

Step 1. collect your own "expert" data and don't trust anyone else to make it perfect

Step 2. avoid "no action" data so your policy doesn’t just sit there

Step 3. It’s not working? Collect more data until "extrapolation" becomes "interpolation"

Step 4. Train and test on the same day because your setup might change tomorrow

"Dirty Laundry"

Symptoms of a larger problem

The not-so-secret recipe to making a rockstar behavior cloning demo on real robots

Step 1. collect your own "expert" data and don't trust anyone else to make it perfect

Step 2. avoid "no action" data so your policy doesn’t just sit there

Step 3. It’s not working? Collect more data until "extrapolation" becomes "interpolation"

Step 4. Train and test on the same day because your setup might change tomorrow

Mostly because we don't have a lot of data

Scaling Physical Robot Learning

Scaling robots ↔ cloud ↔ operator

Scaling Physical Robot Learning

Scaling robots ↔ cloud ↔ operator

+ Couple thousand episodes on a good week

- Still bottlenecked on rate of data collection

Scaling Physical Robot Learning

Scaling robots ↔ cloud ↔ operator

+ Couple thousand episodes on a good week

- Still bottlenecked on rate of data collection

10 robots: learn end-to-end pick and place

with objects the size of your palm

Scaling Physical Robot Learning

Scaling robots ↔ cloud ↔ operator

+ Couple thousand episodes on a good week

- Still bottlenecked on rate of data collection

10 robots: learn end-to-end pick and place

with objects the size of your palm

10x more robots = 10x dollars, manpower, space

VP: "but can you solve cooking though?"

Roboticist

Computer Vision

NLP

Internet-scale data

How do other fields scale up data collection?

Roboticist

Computer Vision

NLP

Internet-scale data

How do other fields scale up data collection?

Scripts that crawl the Internet

+ Autonomous

+ Easier to scale

– Maybe noisier

Many paths forward

Scaling Robot Learning

For practical applications

  • Inductive biases of deep networks
  • Prior structure (e.g. physics)

In pursuit of embodied AI

  • Learning from the Internet

Make the most out of the data we have

How do we learn from the Internet?

MT-Opt, Kalashnikov, Varley, Hausman et al

XYZ Robotics

Many paths forward

For practical applications

  • Inductive biases of deep networks
  • Prior structure (e.g. physics)

In pursuit of embodied AI

  • Learning from the Internet

Make the most out of the data we have

How do we learn from the Internet?

-> Highlight: "Socratic Models" (new!)

Many paths forward

For practical applications

  • Inductive biases of deep networks
  • Prior structure (e.g. physics)

In pursuit of embodied AI

  • Learning from the Internet

Make the most out of the data we have

How do we learn from the Internet?

1

2

-> Highlight: "Socratic Models" (new!)

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Dense visual affordances are great for sample efficient grasping

Takeaway #1

Fully Conv Net

Orthographic

Projection

Grasping

Affordances

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Dense visual affordances are great for sample efficient grasping

Takeaway #1

Fully Conv Net

Orthographic

Projection

Grasping

Affordances

f_g({\color{Green}s},{\color{Red}a})

Grasping value function

receptive field

grasp action

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Dense visual affordances are great for sample efficient grasping

Takeaway #1

Fully Conv Net

Orthographic

Projection

Grasping

Affordances

f_g({\color{Green}s},{\color{Red}a})

Grasping value function

receptive field

grasp action

spatial action predictions anchored on visual features

  • translation & rotation equivariance

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Residual physics is great for sample efficient throwing

Takeaway #2

a={\color{Green}\pi}(s)

Physics-based controller

a={\color{Blue}f}(s)
a={\color{Green}\pi}(s)+{\color{Blue}f}(s,{\color{Green}\pi}(s))

Visuomotor policy

Residual physics

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Residual physics is great for sample efficient throwing

Takeaway #2

a={\color{Green}\pi}(s)

Physics-based controller

Learns "residual" corrections

a={\color{Blue}f}(s)
a={\color{Green}\pi}(s)+{\color{Blue}f}(s,{\color{Green}\pi}(s))

Visuomotor policy

Residual physics

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Interaction as a means to learn perception

Takeaway #3

TossingBot

TossingBot: Learning to Throw Arbitrary Objects with Residual Physics

RSS 2019, T-RO 2020

Interaction as a means to learn perception

Takeaway #3

From learning manipulation affordances...

  • Clusters form around visual features of similar objects
  • Emerging perceptual representations from interaction

grounded on physics

Transporter Nets

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Transporter Nets

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Precise placing depends on the grasp 

Transporter Nets

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Precise placing depends on the grasp 

Dense visual affordances for placing?

Transporter Nets

Geometric biases can be achieved by "modernizing" sliding window approach

Takeaway #1

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

f_\textrm{pick}(\mathbf{o}_t)\rightarrow \mathcal{T}_\textrm{pick}
f_\textrm{place}(\mathbf{o}_t,\mathcal{T}_\textrm{pick})\rightarrow \mathcal{T}_\textrm{place}
\mathcal{Q}_\textrm{place}(\tau|\mathbf{o}_t,\mathcal{T}_\textrm{pick}) = \psi(\mathbf{o}_t[\mathcal{T}_\textrm{pick}])*\phi(\mathbf{o}_t)[\tau]
\mathcal{T}_\textrm{place} = \argmax_{\{\tau_i\}} \mathcal{Q}_\textrm{place}(\tau_i|\mathbf{o}_t,\mathcal{T}_\textrm{pick})

Picking

Pick-conditioned placing

Cross-correlation of sliding window around the pick

Dense value function for placing

Transporter Nets

Softmax cross-entropy loss is great for behavior cloning

Takeaway #2

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Expert label

Treat entire affordance map as a single probability distribution

Transporter Nets

Softmax cross-entropy loss is great for behavior cloning

Takeaway #2

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Expert label

Behavior cloning

  • Only one action label per timestep
  • But true distribution is likely multimodal

Treat entire affordance map as a single probability distribution

Transporter Nets

Softmax cross-entropy loss is great for behavior cloning

Takeaway #2

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

  • Surprisingly capable of capturing multimodal distributions with only one-hot labels
  • Visual features provide a strong inductive bias for learning these modes

Implicit Behavioral Cloning

Softmax cross-entropy loss... actually in general...

Energy-based models are great for behavior cloning

Takeaway #1

Implicit Behavioral Cloning

Pete Florence et al., CoRL 2021

If you squint...

\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a})

BC policy learning as:                 instead of:

\hat{\textbf{a}} = F_{\theta}(\textbf{o})

Pretty sure that's an EBM

Implicit Behavioral Cloning

Softmax cross-entropy loss... actually in general...

Energy-based models are great for behavior cloning

Takeaway #1

Implicit Behavioral Cloning

Pete Florence et al., CoRL 2021

Learn a probability distribution over actions:

If you squint...

Pretty sure that's an EBM

\mathcal{L}_{\text{InfoNCE}} = \sum_{i=1}^N -\log \big( \tilde{p}_{\theta}( {\color{black} \mathbf{y}_i} | \ \mathbf{x}, \ {\color{red}\{\tilde{\mathbf{y}}^j_i\}_{j=1}^{N_{\text{neg.}}} } ) \big)
\tilde{p}_{\theta}( {\color{black} \mathbf{y}_i} | \ \mathbf{x}, \ {\color{red}\{\tilde{\mathbf{y}}^j_i\}_{j=1}^{N_{\text{neg.}}} } ) = \frac{e^{-E_{\theta}(\mathbf{x}_i, {\color{black} \mathbf{y}_i} )}} {e^{-E_{\theta}( \mathbf{x}_i, {\color{black} \mathbf{y}_i})} + {\color{red} \sum_{j=1}^{N_{\text{neg}}}} e^{-E_{\theta}(\mathbf{x}_i, {\color{red} \tilde{\mathbf{y}}^j_i} )} }
\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a})

BC policy learning as:                 instead of:

\hat{\textbf{a}} = F_{\theta}(\textbf{o})

Implicit Behavioral Cloning

Softmax cross-entropy loss... actually in general...

Energy-based models are great for behavior cloning

Takeaway #1

Implicit Behavioral Cloning

Pete Florence et al., CoRL 2021

Learn a probability distribution over actions:

If you squint...

\mathcal{L}_{\text{InfoNCE}} = \sum_{i=1}^N -\log \big( \tilde{p}_{\theta}( {\color{black} \mathbf{y}_i} | \ \mathbf{x}, \ {\color{red}\{\tilde{\mathbf{y}}^j_i\}_{j=1}^{N_{\text{neg.}}} } ) \big)
\tilde{p}_{\theta}( {\color{black} \mathbf{y}_i} | \ \mathbf{x}, \ {\color{red}\{\tilde{\mathbf{y}}^j_i\}_{j=1}^{N_{\text{neg.}}} } ) = \frac{e^{-E_{\theta}(\mathbf{x}_i, {\color{black} \mathbf{y}_i} )}} {e^{-E_{\theta}( \mathbf{x}_i, {\color{black} \mathbf{y}_i})} + {\color{red} \sum_{j=1}^{N_{\text{neg}}}} e^{-E_{\theta}(\mathbf{x}_i, {\color{red} \tilde{\mathbf{y}}^j_i} )} }
  • Conditioned on observation (raw images)
  • Uniformly sampled negatives
\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a})

BC policy learning as:                 instead of:

\hat{\textbf{a}} = F_{\theta}(\textbf{o})

Pretty sure that's an EBM

Implicit Behavioral Cloning

Energy-based models are great for behavior cloning

Takeaway #1

Implicit Behavioral Cloning

Pete Florence et al., CoRL 2021

+ Can represent multi-modal actions

 

\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a})

BC policy learning as:                 instead of:

\hat{\textbf{a}} = F_{\theta}(\textbf{o})

Fly left or right around the tree?

Implicit Behavioral Cloning

Energy-based models are great for behavior cloning

Takeaway #1

Implicit Behavioral Cloning

Pete Florence et al., CoRL 2021

+ Can represent multi-modal actions

+ More sample efficiently learn discontinuous trajectories

\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a})

BC policy learning as:                 instead of:

\hat{\textbf{a}} = F_{\theta}(\textbf{o})

Fly left or right around the tree?

The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies

Ronen Basri et al., NeurIPS 2019

subtle but decisive maneuvers

Implicit Kinematic Policies

Implicit policies give rise to better action representations for continuous control

Takeaway #2

Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning

Aditya Ganapathi et al., ICRA 2022

Adi Ganapathi

Implicit Kinematic Policies

Implicit policies give rise to better action representations for continuous control

Takeaway #2

Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning

Aditya Ganapathi et al., ICRA 2022

Adi Ganapathi

RSS Workshop on Action Representations for Learning in Continuous Control, 2020

Implicit Kinematic Policies

Implicit policies give rise to better action representations for continuous control

Takeaway #2

Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning

Aditya Ganapathi et al., ICRA 2022

Adi Ganapathi

Which action space?

  • Let the model decide:
\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a}_\textrm{joints},\textbf{a}_\textrm{cart})

Implicit Kinematic Policies

Implicit policies give rise to better action representations for continuous control

Takeaway #2

Implicit Kinematic Policies: Unifying Joint and Cartesian Action Spaces in End-to-End Robot Learning

Aditya Ganapathi et al., ICRA 2022

Adi Ganapathi

Which action space?

  • Let the model decide:
\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a}_\textrm{joints},\textbf{a}_\textrm{cart})

Can also use (differentiable) forward kinematics to "fix" joint encoder errors:

\hat{\textbf{a}} = \underset{\textbf{a} \in \mathcal{A}}{\arg\min} \ \ E_{\theta}(\textbf{o},\textbf{a}_\textrm{joints},{\color{Blue}\textrm{FK}}(f(\textbf{a}_\textrm{joints})))

How much data do we need?

50 expert demos

How much data do we need?

500 expert demos

50 expert demos

How much data do we need?

500 expert demos

5000 expert demos

50 expert demos

How much data do we need?

500 expert demos

5000 expert demos

50 expert demos

Many paths forward

For practical applications

  • Inductive biases of deep networks
  • Prior structure (e.g. physics)

In pursuit of embodied AI

  • Learning from the Internet

Make the most out of the data we have

How do we learn from the Internet?

Learning to See before Learning to Act

Pre-training on large-scale offline datasets (e.g., ImageNet, COCO) can help affordance models

Takeaway #1

Learning to See before Learning to Act: Visual Pre-training for Manipulation

Lin Yen-Chen et al., ICRA 2020

Lin Yen-Chen

Learning to See before Learning to Act

Pre-training on large-scale offline datasets (e.g., ImageNet, COCO) can help affordance models

Takeaway #1

Learning to See before Learning to Act: Visual Pre-training for Manipulation

Lin Yen-Chen et al., ICRA 2020

Lin Yen-Chen

XIRL

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

Schmeckpeper et al., CoRL '20

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

Schmeckpeper et al., CoRL '20

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

Schmeckpeper et al., CoRL '20

Task: "put all pens in the cup, transfer to another"

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

Schmeckpeper et al., CoRL '20

Task: "put all pens in the cup, transfer to another"

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

Learn generic visual reward function

  • Temporal Cycle Consistency
    • Self-supervised embedding
\widetilde{V_{ij}^t} = \sum_k^{L_j} \alpha_k V_j^k
\alpha_k = \frac{e^{-\Vert V_i^t-V_j^k\Vert^2}}{\sum_k^{L_j} e^{-\Vert V_i^t-V_j^k \Vert^2}}
\beta_{ijt}^{k} = \frac{e^{-\Vert\widetilde{V_{ij}^t}-V_i^k\Vert^2}}{\sum_k^{L_i} e^{-\Vert\widetilde{V_{ij}^t} - V_i^k\Vert^2}}

XIRL

Learning from Internet-scale videos (e.g., YouTube) may require embodiment-agnostic methods

Takeaway #1

XIRL: Cross-embodiment Inverse Reinforcement Learning

Kevin Zakka et al., CoRL 2021

Kevin Zakka

Learn generic visual reward function

  • Temporal Cycle Consistency
    • Self-supervised embedding
\widetilde{V_{ij}^t} = \sum_k^{L_j} \alpha_k V_j^k
\alpha_k = \frac{e^{-\Vert V_i^t-V_j^k\Vert^2}}{\sum_k^{L_j} e^{-\Vert V_i^t-V_j^k \Vert^2}}
\beta_{ijt}^{k} = \frac{e^{-\Vert\widetilde{V_{ij}^t}-V_i^k\Vert^2}}{\sum_k^{L_i} e^{-\Vert\widetilde{V_{ij}^t} - V_i^k\Vert^2}}

Improving Robot Learning with Internet-Scale Data

Existing offline datasets

Videos

Improving Robot Learning with Internet-Scale Data

Foundation Models

Existing offline datasets

Videos

Socratic Models

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

New multimodal capabilities via guided "Socratic dialogue" between zero-shot foundation models

Takeaway #1

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

New multimodal capabilities via guided "Socratic dialogue" between zero-shot foundation models

Takeaway #1

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

Example output from CLIP + GPT-3: "This image shows an inviting dining space with plenty of natural light."

Example

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

New multimodal capabilities via guided "Socratic dialogue" between zero-shot foundation models

Egocentric perception!

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

Video Question Answering as a Reading Comprehension problem

Takeaway #2

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

Video Question Answering as a Reading Comprehension problem

Takeaway #2

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Demo?

Video Question Answering as a Reading Comprehension problem

Takeaway #2

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Socratic Models

Build reconstructions of the visual world with language for our robot/AR assistants to use

Takeaway #3

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Reconstruction Representations

  • 3D meshes
  • 3D point clouds
  • Volumetric grids
  • 2D Height-maps
  • Learned latent space
  • Language?

Socratic Models

Build reconstructions of the visual world with language for our robot/AR assistants to use

Takeaway #3

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io  (new!)

Reconstruction Representations

  • 3D meshes
  • 3D point clouds
  • Volumetric grids
  • 2D Height-maps
  • Learned latent space
  • Language?

Allow robots to interface with models trained on Internet-scale data that store "commonsense knowledge"

In pursuit of embodied AI

Zero-shot Perception

Zero-shot Planning

Recipe for robot intelligence?

SayCan

Socratic Models

wenlong.page/language-planner

In pursuit of embodied AI

Zero-shot Perception

Zero-shot Planning

Recipe for robot intelligence?

wenlong.page/language-planner

Toddlers

Commonsense

SayCan

Socratic Models

In pursuit of embodied AI

Zero-shot Perception

Zero-shot Planning

Recipe for robot intelligence?

wenlong.page/language-planner

Toddlers

Commonsense

Internet-scale data

SayCan

Socratic Models

In pursuit of embodied AI

What's next? Your work!

Zero-shot Perception

Zero-shot Planning

Recipe for robot intelligence?

wenlong.page/language-planner

Toddlers

Commonsense

Internet-scale data

SayCan

Socratic Models

Thank you!

Pete Florence

Tom Funkhouser

Adrian Wong

Kaylee Burns

Jake Varley

Erwin Coumans

Alberto Rodriguez

Johnny Lee

Vikas Sindhwani

Ken Goldberg

Stefan Welker

Corey Lynch

Laura Downs

Jonathan Tompson

Shuran Song

Vincent Vanhoucke

Kevin Zakka

Michael Ryoo

Travis Armstrong

Maria Attarian

Jonathan Chien

Dan Duong

Krzysztof Choromanski

Phillip Isola

Tsung-Yi Lin

Ayzaan Wahid

Igor Mordatch

Oscar Ramirez

Federico Tombari

Daniel Seita

Lin Yen-Chen

Adi Ganapathi