Канал: RAIL

SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning

FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning

FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning

Making Real-World Reinforcement Learning Practical

Making Real-World Reinforcement Learning Practical

RLIF: Interactive Imitation Learning as Reinforcement Learning

RLIF: Interactive Imitation Learning as Reinforcement Learning

CS 285: Guest Lecture: Dorsa Sadigh

CS 285: Guest Lecture: Dorsa Sadigh

CS 285: Guest Lecture: Aviral Kumar

CS 285: Guest Lecture: Aviral Kumar

CS 285: Lecture 23, Part 2: Challenges & Open Problems

CS 285: Lecture 23, Part 2: Challenges & Open Problems

CS 285: Lecture 23, Part 1: Challenges & Open Problems

CS 285: Lecture 23, Part 1: Challenges & Open Problems

Large-Scale Data-Driven Robotic Learning

Large-Scale Data-Driven Robotic Learning

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 3

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 3

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 2

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 2

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 1

CS 285: Lecture 21, RL with Sequence Models & Language Models, Part 1

CS 285: Andrea Zanette: Towards a Statistical Foundation for Reinforcement Learning

CS 285: Andrea Zanette: Towards a Statistical Foundation for Reinforcement Learning

CS 285: Eric Mitchell: Reinforcement Learning from Human Feedback: Algorithms & Applications

CS 285: Eric Mitchell: Reinforcement Learning from Human Feedback: Algorithms & Applications

LangRob Workshop @ CoRL 2023 (Part 3)

LangRob Workshop @ CoRL 2023 (Part 3)

LangRob Workshop @ CoRL 2023 (Part 2)

LangRob Workshop @ CoRL 2023 (Part 2)

LangRob Workshop @ CoRL 2023 (Part 1)

LangRob Workshop @ CoRL 2023 (Part 1)

Reinforcement Learning with Large Datasets: Robotics, Image Generation, and LLMs

Reinforcement Learning with Large Datasets: Robotics, Image Generation, and LLMs

CS 285: Lecture 18, Variational Inference, Part 4

CS 285: Lecture 18, Variational Inference, Part 4

Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning (Summary)

Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning (Summary)

NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration (Summary Video)

NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration (Summary Video)

CS 285: Lecture 2, Imitation Learning. Part 3

CS 285: Lecture 2, Imitation Learning. Part 3

CS 285: Lecture 2, Imitation Learning. Part 1

CS 285: Lecture 2, Imitation Learning. Part 1

CS 285: Lecture 2, Imitation Learning. Part 5

CS 285: Lecture 2, Imitation Learning. Part 5

CS 285: Lecture 2, Imitation Learning. Part 2

CS 285: Lecture 2, Imitation Learning. Part 2

CS 285: Lecture 2, Imitation Learning. Part 4

CS 285: Lecture 2, Imitation Learning. Part 4

CS 285: Lecture 1, Introduction. Part 3

CS 285: Lecture 1, Introduction. Part 3

CS 285: Lecture 1, Introduction. Part 1

CS 285: Lecture 1, Introduction. Part 1

CS 285: Lecture 1, Introduction. Part 2

CS 285: Lecture 1, Introduction. Part 2

Multi-Stage Cable Routing Through Hierarchical Imitation Learning

Multi-Stage Cable Routing Through Hierarchical Imitation Learning

Data-Driven Reinforcement Learning for Robotic Manipulation

Data-Driven Reinforcement Learning for Robotic Manipulation

Reinforcement Learning with Large Datasets: a Path to Resourceful Autonomous Agents

Reinforcement Learning with Large Datasets: a Path to Resourceful Autonomous Agents

ViNT: A Foundation Model for Visual Navigation (Summary Video)

ViNT: A Foundation Model for Visual Navigation (Summary Video)

A General-Purpose Robotic Navigation Model

A General-Purpose Robotic Navigation Model

Robotic Learning with Large Datasets: Towards General-Purpose Models for All Robots

Robotic Learning with Large Datasets: Towards General-Purpose Models for All Robots

Reinforcement Learning Pretraining for Reinforcement Learning Finetuning

Reinforcement Learning Pretraining for Reinforcement Learning Finetuning

FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing (Summary)

FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing (Summary)

The Bitterest of Lessons: The Role of Data and Optimization in Emergence

The Bitterest of Lessons: The Role of Data and Optimization in Emergence

Neural Software Abstractions: Michael Chang Dissertation Talk

Neural Software Abstractions: Michael Chang Dissertation Talk

RAIL Live Stream

RAIL Live Stream

CS 285: Guest Lecture: Alexandre Bayen

CS 285: Guest Lecture: Alexandre Bayen

CS 285: Guest Lecture: Bo Dai

CS 285: Guest Lecture: Bo Dai

CS 285: Lecture 23: Open Problems

CS 285: Lecture 23: Open Problems

CS 285: Chelsea Finn (Stanford)

CS 285: Chelsea Finn (Stanford)

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement

Object Representations as Fixed Points

Object Representations as Fixed Points

Deep Reinforcement Learning with Real-World Data

Deep Reinforcement Learning with Real-World Data

CS 285: Lecture 22, Part 1: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 1: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 5: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 5: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 4: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 4: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 3: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 3: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 2: Transfer Learning & Meta-Learning

CS 285: Lecture 22, Part 2: Transfer Learning & Meta-Learning

What do we require to pretrain robots?

What do we require to pretrain robots?

Pre-training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials

Pre-training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials

CS 285: Lecture 12, Part 4: Model-Based RL with Policies

CS 285: Lecture 12, Part 4: Model-Based RL with Policies

CS 285: Lecture 12, Part 3: Model-Based RL with Policies

CS 285: Lecture 12, Part 3: Model-Based RL with Policies

CS 285: Lecture 12, Part 2: Model-Based RL with Policies

CS 285: Lecture 12, Part 2: Model-Based RL with Policies

CS 285: Lecture 12, Part 1: Model-Based RL with Policies

CS 285: Lecture 12, Part 1: Model-Based RL with Policies

How to Leverage Unlabeled Data in Offline Reinforcement Learning

How to Leverage Unlabeled Data in Offline Reinforcement Learning

Ensuring Safety in Online Reinforcement Learning by Leveraging Offline Data

Ensuring Safety in Online Reinforcement Learning by Leveraging Offline Data

Robotic Learning

Robotic Learning

Planning with Reinforcement Learning

Planning with Reinforcement Learning

Hybrid Imitative Planning with Predictive and Geometric Costs in Offroad Environments

Hybrid Imitative Planning with Predictive and Geometric Costs in Offroad Environments

Understanding the World Through Action

Understanding the World Through Action

Imitation learning vs. offline reinforcement learning

Imitation learning vs. offline reinforcement learning

The Case for Real-World Reinforcement Learning

The Case for Real-World Reinforcement Learning

Data-Driven Offline Optimization for Architecting Hardware Accelerators

Data-Driven Offline Optimization for Architecting Hardware Accelerators

Offline Reinforcement Learning: BayLearn 2021 Keynote Talk

Offline Reinforcement Learning: BayLearn 2021 Keynote Talk

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability

CS 285: Lecture 17, Part 1: RL Theory

CS 285: Lecture 17, Part 1: RL Theory

CS 285: Lecture 17, Part 2: RL Theory

CS 285: Lecture 17, Part 2: RL Theory

CS 285: Lecture 16, Part 4: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 4: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 1: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 1: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 3: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 3: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 2: Offline Reinforcement Learning 2

CS 285: Lecture 16, Part 2: Offline Reinforcement Learning 2

CS 285: Lecture 15, Part 3: Offline Reinforcement Learning

CS 285: Lecture 15, Part 3: Offline Reinforcement Learning

CS 285: Lecture 15, Part 2: Offline Reinforcement Learning

CS 285: Lecture 15, Part 2: Offline Reinforcement Learning

CS 285: Lecture 15, Part 1: Offline Reinforcement Learning

CS 285: Lecture 15, Part 1: Offline Reinforcement Learning

CS 285: Lecture 6, Part 3

CS 285: Lecture 6, Part 3

CS 285: Lecture 3, Part 5

CS 285: Lecture 3, Part 5

CS 285: Lecture 3, Part 4

CS 285: Lecture 3, Part 4

CS 285: Lecture 3, Part 3

CS 285: Lecture 3, Part 3

CS 285: Lecture 3, Part 2

CS 285: Lecture 3, Part 2

CS 285: Lecture 3, Part 1

CS 285: Lecture 3, Part 1

Understanding the World Through Action

Understanding the World Through Action

DR3: Value-Based Deep RL Requires Explicit Regularization

DR3: Value-Based Deep RL Requires Explicit Regularization

Conservative Objective Models (ICML 2021)

Conservative Objective Models (ICML 2021)

Learning to Reach Goals via Iterated Supervised Learning

Learning to Reach Goals via Iterated Supervised Learning

How Robots Can Learn End-to-End From Data

How Robots Can Learn End-to-End From Data

CS 182: Lecture 19: Part 2: GANs

CS 182: Lecture 19: Part 2: GANs

CS 182: Lecture 21: Part 1: Meta-Learning

CS 182: Lecture 21: Part 1: Meta-Learning

CS 182: Lecture 19: Part 1: GANs

CS 182: Lecture 19: Part 1: GANs

CS 182: Lecture 20: Part 1: Adversarial Examples

CS 182: Lecture 20: Part 1: Adversarial Examples

CS 182: Lecture 19: Part 3: GANs

CS 182: Lecture 19: Part 3: GANs

CS 182: Lecture 21: Part 2: Meta-Learning

CS 182: Lecture 21: Part 2: Meta-Learning

CS 182: Lecture 21: Part 3: Meta-Learning

CS 182: Lecture 21: Part 3: Meta-Learning

CS 182: Lecture 20: Part 3: Adversarial Examples

CS 182: Lecture 20: Part 3: Adversarial Examples

CS 182: Lecture 20: Part 2: Adversarial Examples

CS 182: Lecture 20: Part 2: Adversarial Examples

CS 182: Lecture 17: Part 3: Generative Models

CS 182: Lecture 17: Part 3: Generative Models

CS 182: Lecture 18: Part 4: Latent Variable Models

CS 182: Lecture 18: Part 4: Latent Variable Models

CS 182: Lecture 15: Part 3: Policy Gradients

CS 182: Lecture 15: Part 3: Policy Gradients

CS 182: Lecture 16: Part 2: Actor-Critic & Q-Learning

CS 182: Lecture 16: Part 2: Actor-Critic & Q-Learning

CS 182: Lecture 15: Part 1: Policy Gradients

CS 182: Lecture 15: Part 1: Policy Gradients

CS 182: Lecture 16: Part 3: Actor-Critic & Q-Learning

CS 182: Lecture 16: Part 3: Actor-Critic & Q-Learning

CS 182: Lecture 17: Part 1: Generative Models

CS 182: Lecture 17: Part 1: Generative Models

CS 182: Lecture 15: Part 2: Policy Gradients

CS 182: Lecture 15: Part 2: Policy Gradients

CS 182: Lecture 16: Part 1: Actor-Critic & Q-Learning

CS 182: Lecture 16: Part 1: Actor-Critic & Q-Learning

CS 182: Lecture 17: Part 2: Generative Models

CS 182: Lecture 17: Part 2: Generative Models

CS 182: Lecture 18: Part 1: Latent Variable Models

CS 182: Lecture 18: Part 1: Latent Variable Models

CS 182: Lecture 18: Part 3: Latent Variable Models

CS 182: Lecture 18: Part 3: Latent Variable Models

CS 182: Lecture 18: Part 2: Latent Variable Models

CS 182: Lecture 18: Part 2: Latent Variable Models

CS 182: Lecture 2, Part 3: Machine Learning Basics

CS 182: Lecture 2, Part 3: Machine Learning Basics

CS 182: Lecture 9: Part 2: Visualization and Style Transfer

CS 182: Lecture 9: Part 2: Visualization and Style Transfer

CS 182: Lecture 6: Part 3: Convolutional Networks

CS 182: Lecture 6: Part 3: Convolutional Networks

CS 182: Lecture 8: Part 4: Computer Vision

CS 182: Lecture 8: Part 4: Computer Vision

CS 182: Lecture 4: Part 1: Optimization

CS 182: Lecture 4: Part 1: Optimization

CS 182: Lecture 12: Part 1: Transformers

CS 182: Lecture 12: Part 1: Transformers

CS 182: Lecture 9: Part 1: Visualization and Style Transfer

CS 182: Lecture 9: Part 1: Visualization and Style Transfer

CS 182: Lecture 2, Part 1: Machine Learning Basics

CS 182: Lecture 2, Part 1: Machine Learning Basics

CS 182: Lecture 13: Part 2: NLP

CS 182: Lecture 13: Part 2: NLP

CS 182: Lecture 8: Part 3: Computer Vision

CS 182: Lecture 8: Part 3: Computer Vision

CS 182: Lecture 5: Part 2: Backpropagation

CS 182: Lecture 5: Part 2: Backpropagation

CS 182: Lecture 7: Part 2: Initialization, Batch Normalization

CS 182: Lecture 7: Part 2: Initialization, Batch Normalization

CS 182: Lecture 2, Part 2: Machine Learning Basics

CS 182: Lecture 2, Part 2: Machine Learning Basics

CS 182: Lecture 1, Part 3: Introduction

CS 182: Lecture 1, Part 3: Introduction

CS 182: Lecture 11: Part 2: Sequence to Sequence

CS 182: Lecture 11: Part 2: Sequence to Sequence

CS 182 Lecture 3: Part 3: Error Analysis

CS 182 Lecture 3: Part 3: Error Analysis

CS 182: Lecture 12: Part 2: Transformers

CS 182: Lecture 12: Part 2: Transformers

CS 182: Lecture 10: Part 2: Recurrent Neural Networks

CS 182: Lecture 10: Part 2: Recurrent Neural Networks

CS 182: Lecture 1, Part 2: Introduction

CS 182: Lecture 1, Part 2: Introduction

CS 182: Lecture 5: Part 3: Backpropagation

CS 182: Lecture 5: Part 3: Backpropagation

CS 182: Lecture 14: Part 2: Imitation Learning

CS 182: Lecture 14: Part 2: Imitation Learning

CS 182: Lecture 7: Part 1: Initialization, Batch Normalization

CS 182: Lecture 7: Part 1: Initialization, Batch Normalization

CS 182: Lecture 11: Part 1: Sequence to Sequence

CS 182: Lecture 11: Part 1: Sequence to Sequence

CS 182: Lecture 11: Part 3: Sequence to Sequence

CS 182: Lecture 11: Part 3: Sequence to Sequence

CS 182: Lecture 4: Part 3: Optimization

CS 182: Lecture 4: Part 3: Optimization

CS 182: Lecture 12: Part 3: Transformers

CS 182: Lecture 12: Part 3: Transformers

CS 182: Lecture 10: Part 3: Recurrent Neural Networks

CS 182: Lecture 10: Part 3: Recurrent Neural Networks

CS 182: Lecture 8: Part 1: Computer Vision

CS 182: Lecture 8: Part 1: Computer Vision

CS 182: Lecture 7: Part 3: Initialization, Batch Normalization

CS 182: Lecture 7: Part 3: Initialization, Batch Normalization

CS 182: Lecture 13: Part 3: NLP

CS 182: Lecture 13: Part 3: NLP

CS 182 Lecture 3: Part 1: Error Analysis

CS 182 Lecture 3: Part 1: Error Analysis

CS 182: Lecture 10: Part 1: Recurrent Neural Networks

CS 182: Lecture 10: Part 1: Recurrent Neural Networks

CS 182 Lecture 3: Part 2: Error Analysis

CS 182 Lecture 3: Part 2: Error Analysis

CS 182: Lecture 8: Part 2: Computer Vision

CS 182: Lecture 8: Part 2: Computer Vision

CS 182: Lecture 13: Part 1: NLP

CS 182: Lecture 13: Part 1: NLP

CS 182: Lecture 14: Part 3: Imitation Learning

CS 182: Lecture 14: Part 3: Imitation Learning

CS 182: Lecture 4: Part 2: Optimization

CS 182: Lecture 4: Part 2: Optimization

CS 182: Lecture 6: Part 1: Convolutional Networks

CS 182: Lecture 6: Part 1: Convolutional Networks

CS 182: Lecture 14: Part 1: Imitation Learning

CS 182: Lecture 14: Part 1: Imitation Learning

CS 182: Lecture 5: Part 1: Backpropagation

CS 182: Lecture 5: Part 1: Backpropagation

CS 182: Lecture 1, Part 1: Introduction

CS 182: Lecture 1, Part 1: Introduction

CS 182: Lecture 2, Part 4: Machine Learning Basics

CS 182: Lecture 2, Part 4: Machine Learning Basics

CS 182: Lecture 9: Part 3: Visualization and Style Transfer

CS 182: Lecture 9: Part 3: Visualization and Style Transfer

CS 182: Lecture 6: Part 2: Convolutional Networks

CS 182: Lecture 6: Part 2: Convolutional Networks

A Gentle Introduction to Offline Reinforcement Learning

A Gentle Introduction to Offline Reinforcement Learning

Offline Reinforcement Learning: Incorporating Knowledge from Data into RL

Offline Reinforcement Learning: Incorporating Knowledge from Data into RL

CS 285: Lecture 23, Open Problems, Part 1

CS 285: Lecture 23, Open Problems, Part 1

CS 285: Lecture 21, Transfer Learning, Part 2

CS 285: Lecture 21, Transfer Learning, Part 2

CS 285: Lecture 21, Transfer Learning, Part 3

CS 285: Lecture 21, Transfer Learning, Part 3

CS 285: Lecture 23, Open Problems, Part 3

CS 285: Lecture 23, Open Problems, Part 3

CS 285: Lecture 21, Transfer Learning, Part 1

CS 285: Lecture 21, Transfer Learning, Part 1

CS 285: Lecture 22, Meta-Learning, Part 5

CS 285: Lecture 22, Meta-Learning, Part 5

CS 285: Lecture 22, Meta-Learning, Part 3

CS 285: Lecture 22, Meta-Learning, Part 3

CS 285: Lecture 21, Transfer Learning, Part 4

CS 285: Lecture 21, Transfer Learning, Part 4

CS 285: Lecture 23, Open Problems, Part 2

CS 285: Lecture 23, Open Problems, Part 2

CS 285: Lecture 22, Meta-Learning, Part 1

CS 285: Lecture 22, Meta-Learning, Part 1

CS 285: Lecture 21, Transfer Learning, Part 5

CS 285: Lecture 21, Transfer Learning, Part 5

CS 285: Lecture 22, Meta-Learning, Part 2

CS 285: Lecture 22, Meta-Learning, Part 2

CS 285: Lecture 22, Meta-Learning, Part 4

CS 285: Lecture 22, Meta-Learning, Part 4

NeurIPS 2020 Tutorial on Offline RL: Part 1

NeurIPS 2020 Tutorial on Offline RL: Part 1

NeurIPS 2020 Tutorial on Offline RL: Part 2

NeurIPS 2020 Tutorial on Offline RL: Part 2

CS 285: Lecture 19, Control as Inference, Part 2

CS 285: Lecture 19, Control as Inference, Part 2

CS 285: Lecture 18, Variational Inference, Part 3

CS 285: Lecture 18, Variational Inference, Part 3

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 4

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 4

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 2

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 2

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 1

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 1

CS 285: Lecture 17, Part 1

CS 285: Lecture 17, Part 1

CS 285: Lecture 19, Control as Inference, Part 4

CS 285: Lecture 19, Control as Inference, Part 4

CS 285: Lecture 19, Control as Inference, Part 1

CS 285: Lecture 19, Control as Inference, Part 1

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 3

CS 285: Lecture 20, Inverse Reinforcement Learning, Part 3

CS 285: Lecture 19, Control as Inference, Part 3

CS 285: Lecture 19, Control as Inference, Part 3

CS 285: Lecture 19, Control as Inference, Part 5

CS 285: Lecture 19, Control as Inference, Part 5

CS 285: Lecture 17, Part 2

CS 285: Lecture 17, Part 2

CS 285: Lecture 18, Variational Inference, Part 1

CS 285: Lecture 18, Variational Inference, Part 1

CS 285: Lecture 18, Variational Inference, Part 2

CS 285: Lecture 18, Variational Inference, Part 2

CS 285: Lecture 18, Variational Inference, Part 4

CS 285: Lecture 18, Variational Inference, Part 4

CS 285: Lecture 17, Part 3

CS 285: Lecture 17, Part 3

CS 285: Lecture 14, Part 2

CS 285: Lecture 14, Part 2

CS 285: Lecture 16, Part 4

CS 285: Lecture 16, Part 4

CS 285: Lecture 15, Part 5a

CS 285: Lecture 15, Part 5a

CS 285: Lecture 16, Part 2

CS 285: Lecture 16, Part 2

CS 285: Lecture 14, Part 4

CS 285: Lecture 14, Part 4

CS 285: Lecture 15, Part 2

CS 285: Lecture 15, Part 2

CS 285: Lecture 15, Part 4

CS 285: Lecture 15, Part 4

CS 285: Lecture 16, Part 1

CS 285: Lecture 16, Part 1

CS 285: Lecture 14, Part 1

CS 285: Lecture 14, Part 1

CS 285: Lecture 14, Part 3

CS 285: Lecture 14, Part 3

CS 285: Lecture 16, Part 3

CS 285: Lecture 16, Part 3

CS 285: Lecture 15, Part 1

CS 285: Lecture 15, Part 1