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Michael Ahn

7 accepted papers

2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration

ICRA 2024

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man

Cited by 910SourcecodeScholar
2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0

ICRA 2024poster

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for man…

Cited by 259SourcecodeScholar
2023

Robotic Table Tennis: A Case Study into a High Speed Learning System

RSS 2023poster

We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a…

2022

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

CoRL 2022oral

Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real…

Cited by 1747SourcecodeScholar
2020

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

RSS 2020poster

Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort. However, most reinforcement learning algorithms assume that a well-designed reward function is provided, and learn a single behavior for that single reward function. Such reward funct…

Cited by 55SourcePDFScholar
2019

Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real

CoRL 2019

Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Our method hinges on the use of hierarchical sim2rea

Cited by 0SourcePDFScholar
2019

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

CoRL 2019

ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D’Claw is a three-fingered hand robot that facilitates learning dexterous

Cited by 0SourcePDFScholar