3D Vision & Language as Middleware

Andy Zeng

3D Scene Understanding, CVPR '23 Workshop

Manipulation

From structure to symmetries, etc.

Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras

Akinola et al., ICRA 2020

Non-linearities can be simpler to model in higher dims

Semi-Algebraic Approximation using Christoffel-Darboux Kernel

Marx et al., Springer 2021

From 3D vision to robotics

3D Vision

3D Vision for Robotics

Foundation Models for Robotics

6D Pose Est. (Amazon Picking)

Matterport3D

3DMatch

Pushing & Grasping

TossingBot

Transporter Nets

PaLM SayCan

Socratic Models

PaLM-E: Embodied Multimodal Language Model

From 3D vision to robotics

Foundation Models for Robotics

Transporter Nets

PaLM SayCan

Socratic Models

PaLM-E: Embodied Multimodal Language Model

Project Reach: scale up human teleop

Remote teleop robots to do useful work (amortize cost of data collection)

then automate with robot learning

+ sample efficient robot learning

  Project Reach arm farm '21

Transporter Nets

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Precise placing depends on the grasp 

Dense visual affordances for placing?

Sample efficient pick & place

Transporter Nets

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

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Picking

Pick-conditioned placing

Cross-correlation of sliding window around the pick

Dense value function for placing

Transporter Nets

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

Expert label

Treat entire affordance map as a single probability distribution

Behavior cloning

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

Softmax cross-entropy loss is great for behavior cloning

Implicit Behavior Cloning

CoRL 2021

Transporter Nets

Transporter Networks: Rearranging the Visual World for Robotic Manipulation

CoRL 2020

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

Softmax cross-entropy loss is great for behavior cloning

Transporter Nets Limitations

MIRA: Mental Imagery for Robotic Affordances

Lin Yen-Chen, Pete Florence, Andy Zeng, Jonathan T. Barron, Yilun Du, Wei-Chiu Ma, Anthony Simeonov, Alberto Rodriguez Garcia, Phillip Isola, CoRL 2022

• Only explored shape priors in 2D... can we go 3D?
• Only applied for pick & place... what about other things?
  • Shape matching, scene generation, 3D reconstruction, 3D affordances, etc.
• Requires a 3D sensor... can we get away with only 2D images? (see MIRA)
• Naive multi-task did not yield positive transfer over single tasks

On the quest for shared priors

Interact with the physical world to learn bottom-up commonsense

i.e. "shared knowledge"

# Tasks

Data

Expectation

  MARS Reach arm farm '21

with machine learning

On the quest for shared priors

Interact with the physical world to learn bottom-up commonsense

i.e. "shared knowledge"

# Tasks

Data

Expectation

Reality

Complexity in environment, embodiment, contact, etc.

  MARS Reach arm farm '21

with machine learning

Does multi-task learning result in positive transfer of representations?

Past couple years of research suggest: its complicated

Recent work in multi-task learning...

Multi-task learning grounded in language

Multi-task learning grounded in language seems more likely to lead to positive transfer

Multi-task learning grounded in language

Multi-task learning grounded in language seems more likely to lead to positive transfer

Language may provide an inductive bias for compositional generalization  in multi-task robot learning

Language may provide an inductive bias for compositional generalization  in multi-task robot learning

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin et al., "PaLM", 2022

Advent of Large Language Models

Language as Middleware

2 ways to use it as intermediate representation for robot learning

Implicit (finetune with lots of data)

Explicit (not as much data)

PaLM SayCan

Socratic Models

PaLM-E: Embodied Multimodal Language Model

Language as Middleware

2 ways to use it as intermediate representation for robot learning

Explicit (not as much data)

PaLM SayCan

Socratic Models

Socratic Models & PaLM-SayCan

One way to use Foundation Models with "language as middleware"

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io

Socratic Models & PaLM-SayCan

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io

Visual Language Model

CLIP, ALIGN, LiT,

SimVLM, ViLD, MDETR

Human input (task)

One way to use Foundation Models with "language as middleware"

Socratic Models & PaLM-SayCan

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io

Visual Language Model

CLIP, ALIGN, LiT,

SimVLM, ViLD, MDETR

Human input (task)

Large Language Models for
High-Level Planning

Language-conditioned Policies

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances say-can.github.io

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents wenlong.page/language-planner

One way to use Foundation Models with "language as middleware"

Socratic Models: Robot Pick-and-Place Demo

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io

For each step, predict pick & place:

Socratic Models & PaLM-SayCan

Open research problem, but here's one way to do it

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

https://socraticmodels.github.io

Visual Language Model

CLIP, ALIGN, LiT,

SimVLM, ViLD, MDETR

Human input (task)

Large Language Models for
High-Level Planning

Language-conditioned Policies

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances say-can.github.io

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents wenlong.page/language-planner

Inner Monologue

Inner Monologue: Embodied Reasoning through Planning with Language Models

https://innermonologue.github.io

Limits of language as information bottleneck?

-  Loses spatial (numerical) precision

-  Highly (distributional) multimodal

-  Not as information-rich as other modalities

Limits of language as information bottleneck?

-  Loses spatial (numerical) precision

-  Highly (distributional) multimodal

-  Not as information-rich as other modalities

-  Only for high level? what about low-level?

Perception

Planning

Control

Socratic Models

Inner Monologue

PaLM-SayCan

Wenlong Huang et al, 2022

Imitation? RL?

Engineered?

Intuition and commonsense is not just a high-level thing

Intuition and commonsense is not just a high-level thing

Applies to low-level reasoning too

• spatial: "move a little bit to the left"
• temporal: "move faster"
• functional: "balance yourself"

Behavioral commonsense is the "dark matter" of interactive intelligence:

Intuition and commonsense is not just a high-level thing

Can we extract these priors from language models?

Applies to low-level reasoning too

• spatial: "move a little bit to the left"
• temporal: "move faster"
• functional: "balance yourself"

Behavioral commonsense is the "dark matter" of interactive intelligence:

Language models can write code

Code as a medium to express low-level commonsense

Live Demo

In-context learning is supervised meta learning

Trained with autoregressive models via "packing"

In-context learning is supervised meta learning

Trained with autoregressive models via "packing"

Better with non-recurrent autoregressive sequence models

Transformers at certain scale can generalize to unseen      (i.e. tasks)

"Data Distributional Properties Drive Emergent In-Context Learning in Transformers"
Chan et al., NeurIPS '22

"General-Purpose In-Context Learning by Meta-Learning Transformers" Kirsch et al., NeurIPS '22

"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes" Garg et al., '22

Language models can write code

Code as a medium to express more complex plans

Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

code-as-policies.github.io

Code as Policies: Language Model Programs for Embodied Control

Live Demo
https:youtu.be/byavpcCRaYI

Outstanding Paper Award in Robot Learning Finalist, ICRA 2023

Language models can write code

Code as a medium to express more complex plans

Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

code-as-policies.github.io

Code as Policies: Language Model Programs for Embodied Control

Live Demo

Outstanding Paper Award in Robot Learning Finalist, ICRA 2023

Language models can write code

Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

code-as-policies.github.io

Code as Policies: Language Model Programs for Embodied Control

use NumPy,

SciPy code...

Outstanding Paper Award in Robot Learning Finalist, ICRA 2023

Language models can write code

Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

code-as-policies.github.io

Code as Policies: Language Model Programs for Embodied Control

• PD controllers
• impedance controllers

Outstanding Paper Award in Robot Learning Finalist, ICRA 2023

Language models can write code

Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

code-as-policies.github.io

Code as Policies: Language Model Programs for Embodied Control

Outstanding Paper Award in Robot Learning Finalist, ICRA 2023

Language models can write code

Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, Andy Zeng

code-as-policies.github.io

Code as Policies: Language Model Programs for Embodied Control

What is the foundation models for robotics?

Outstanding Paper Award in Robot Learning Finalist, ICRA 2023

VLMaps + Code as Policies for more spatial reasoning

1. Fuse visual-language features into robot maps

2. Use code as policies to do spatial reasoning for navigation

"Visual Language Maps" Chenguang Huang et al., ICRA 2023

"Audio Visual Language Maps" Chenguang Huang et al., 2023

Fuse all the modalities! (including audio)

(i.e. semantic memory with Foundation Models)

3D Vision Wishlist

• How 3D reasoning can enable language to inform low-level control?
• How to ground shapes/geometry in language?
• How can we use code-writing LLMs for 3D reasoning?

Thank you!

Pete Florence

Adrian Wong

Johnny Lee

Vikas Sindhwani

Stefan Welker

Vincent Vanhoucke

Kevin Zakka

Michael Ryoo

Maria Attarian

Brian Ichter

Krzysztof Choromanski

Federico Tombari

Jacky Liang

Aveek Purohit

Wenlong Huang

Fei Xia

Peng Xu

Karol Hausman

and many others!