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20182026
most citedVisual Reinforcement Learning with Imagined Goals

307 citations · 509 across the 20 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.RO2023★ 3 cited

PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play

Lili Chen, Shikhar Bahl, Deepak Pathak

Learning from unstructured and uncurated data has become the dominant paradigm for generative approaches in language and vision. Such unstructured and unguided behavior data, commo…

cs.RO2023★ 2 cited

DEFT: Dexterous Fine-Tuning for Real-World Hand Policies

Aditya Kannan, Kenneth Shaw, Shikhar Bahl +2

Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper,…

cs.RO2023★ 103 cited

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

Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…

cs.LG2023

Efficient RL via Disentangled Environment and Agent Representations

Kevin Gmelin, Shikhar Bahl, Russell Mendonca +1

Agents that are aware of the separation between themselves and their environments can leverage this understanding to form effective representations of visual input. We propose an a…

cs.RO2023

Structured World Models from Human Videos

Russell Mendonca, Shikhar Bahl, Deepak Pathak

We tackle the problem of learning complex, general behaviors directly in the real world. We propose an approach for robots to efficiently learn manipulation skills using only a han…

cs.RO2023★ 2 cited

Affordances from Human Videos as a Versatile Representation for Robotics

Shikhar Bahl, Russell Mendonca, Lili Chen +2

Building a robot that can understand and learn to interact by watching humans has inspired several vision problems. However, despite some successful results on static datasets, it…

cs.RO2023

ALAN: Autonomously Exploring Robotic Agents in the Real World

Russell Mendonca, Shikhar Bahl, Deepak Pathak

Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While i…