SCM.256 - Spring 2024

Guest Lecture:

(Some) Recent ML trends/applications

Shen Shen

May 13, 2024

Notable trends lately

(in CV and NLP)

Self-supervision
Scaling up
Multi-modality
Transformer-based architecture stack
Diffusion-based generative algorithms

Self-supervision (masking)

It seems

Multi-modality

Diffusion/score-based

[image credit: Lilian Weng]

Dall-E 2 (UnCLIP): CLIP + GLIDE

[https://arxiv.org/pdf/2204.06125.pdf]

Outline Today

Part 1: Some echoing trends in Robotics

Part 3: Some more domain-specific applications

Engineering
Natural sciences (e.g. life sciences, health care)
Math, algorithms
Social sciences (e.g. political, ethical impact)

Part 2: Some future directions in CV/NLP/Robotics

Part 1: Robotics

Lots of slides adapted from

Russ Tedrake

Boyuan Chen

2004 - Uses vanilla policy gradient (actor-critic)

uses first-principle (modeling, control, optimization stack)

For the next challenge:

Good control when we don't have useful models?

For the next challenge:

Good control when we don't have useful models?

Rules out:
- (Multibody) Simulation
- Simulation-based reinforcement learning (RL)
- State estimation / model-based control
Some top choices:
- Learn a dynamics model
- Behavior cloning (imitation learning)

Levine*, Finn*, Darrel, Abbeel, JMLR 2016

Visuomotor policies

perception network

(often pre-trained)

policy network

other robot sensors

learned state representation

actions

x history

manipulation (and general control) is hard because?

partially because the data is scarce

Denoising diffusion models

(for actions)

Image source: Ho et al. 2020

Denoiser can be conditioned on additional inputs, $u$: $p_\theta(x_{t-1} | x_t, u) $

Image backbone: ResNet-18 (pretrained on ImageNet)
Total: 110M-150M Parameters
Training Time: 3-6 GPU Days ($150-$300)

Why (Denoising) Diffusion Models?

High capacity + great performance
Small number of demonstrations (typically ~50)
Multi-modal (non-expert) demonstrations
Training stability and consistency
- no hyper-parameter tuning
Generates high-dimension continuous outputs
- vs categorical distributions (e.g. RT-1, RT-2)
- Action-chunking transformers (ACT)
Solid mathematical foundations (score functions)
Reduces nicely to the simple cases (e.g. LQG / Youla)

Scaling Up

We've discussed training one skill
Wanted: few shot generalization to new skills
- multitask, language-conditioned policies
- connects beautifully to internet-scale data

Big Questions:
- How do we feed the data flywheel?
- What are the scaling laws?

Discussion

What if we did have a good model? (and well-specified objective)

Core challenges:
- Control from pixels
- Control through contact
- Optimizing rich robustness objective
The most effective approach today:
- RL on privileged information + teacher-student

Deep RL + Teacher-Student

Lee et al., Learning quadrupedal locomotion over challenging terrain, Science Robotics, 2020

Deep RL + Teacher-student

Magic of Modality

Modality = image, video, 3d mesh, text etc.
One recipe (motivated by data):
- Use discriminative model on data-rich domain to guide training in data-scarce domains
- Use generative model on data-rich domains to synthesize data for data-scarce domains

Task Planning with LLM

Connect unstructured world with structured algorithms

What humans would want:

Task: clean up the spilled coke

Set the coke can into an upright position
Find some napkins
Pick up napkins
Wipe the spilled coke with napkins
Wipe the coke can
Throw away the used napkins

Humans: Language as tasks

Language as plans!

Can we use human priors & knowledges

It turns out humans activities on the internet produces a massive amount of knowledge in the form of text that are really useful!

Highlight

Given a fixed list of options, can evaluate likelihood with LM
Given all vocabularies, can sample with likelihood to generate

Ingredient 1

Bind each executable skill to some text options
Have a list of text options for LM to choose from
Given instruction, choose the most likely one

Ingredient 2

Prompt LLM to output in a more structured way
Parse the structure output

Few-shot prompting of Large Language Models

LLMs can copy the logic and extrapolate it!

Prompt Large Language Models to do structured planning

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances, Ahn et al. , 2022

LLMs for robotics

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances, Ahn et al. , 2022

What task-based affordances reminds us of in MDP/RL?

Value functions!

[Value Function Spaces, Shah, Xu, Lu, Xiao, Toshev, Levine, Ichter, ICLR 2022]

Robotic affordances

Combine LLM and Affordance

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances, Ahn et al. , 2022

LLM x Affordance

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances, Ahn et al. , 2022

Language Models as Zero-Shot Planners:
Extracting Actionable Knowledge for Embodied Agents
Inner Monologue: Embodied Reasoning through Planning with Language Models
PaLM-E: An Embodied Multimodal Language Model
Chain-of-thought prompting elicits reasoning in large language models
Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Extended readings in

LLM + Planning

Part 2: Current/future directions

NLP -- credits: Andrej Karpathy (nov 2023)
CV -- credits: Kaiming He (oct 23)
Robotics -- credits: CoRL debate (nov 23)

Interpretability

Why video

Video is how human perceive the world (physics, 3D)
Video is widely available on internet
Internet videos contain human actions and tutorials

Pre-train on entire youtube, first image + text -> video
Finetune on some robot video
Inference time, given observation image + text prompt -> video of robot doing the task -> back out actions