Robotics Posts

Productive Struggle: The Future of Human Learning in the Age of AI

Rose E. Wang and Megha Srivastava

What happens to human learning when superhuman intelligence is as accessible as a Google search?

MiniVLA: A Better VLA with a Smaller Footprint

Suneel Belkhale and Dorsa Sadigh

Reducing OpenVLA's parameters 7x, and improving the input and output representation space.

Stanford AI Lab Papers and Talks at RSS 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at RSS 2023, all in one place.

Self-Improving Robots: Embracing Autonomy in Robot Learning

Archit Sharma

Learning methods for robotics have a data problem. Autonomous robotic systems can deliver orders of magnitude more real-world embodied data than we have.

Stanford AI Lab Papers and Talks at CVPR 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at CVPR 2023, all in one place.

Learning to Imitate

Divyansh Garg

SOTA methods for Imitation Learning are heavily data-inefficient or rely on unstable Adversarial training. We present a novel framework that introduces simple, stable, and data-efficient learning from few experts that scales well to complex environments.

Reward Isn't Free: Supervising Robot Learning with Language and Video from the Web

Suraj Nair

Where do the rewards for robotic reinforcement learning come from? In this blog post we study how using crowdsourced language annotations and videos of humans, we can learn reward functions in a scalable way and enable them to generalize more broadly.

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

Ajay Mandlekar

We conducted an extensive study of six offline learning algorithms for robot manipulation on five simulated and three real-world multi-stage manipulation tasks of varying complexity, and with datasets of varying quality. Our study analyzes the most critical challenges when learning from offline h...

iGibson: A Simulation Environment to Train AI Agents in Large Realistic Scenes

Roberto Martín-Martín

Introducing iGibson, an open source, fully academically developed simulation environment for interactive tasks in large realistic scenes.

Learning to Influence Multi-Agent Interaction

Annie Xie

We introduce a framework for multi-agent interaction that represents the low-level policies of non-stationary agents with high-level latent strategies.