activity
20192025
most citedData-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks

29 citations · 43 across the 5 of their papers we have counts for

collaborators
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8 papers · 1 filter

cs.RO20251 cited

Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer

Gemini Robotics Team, Abbas Abdolmaleki, Saminda Abeyruwan +169

General-purpose robots need a deep understanding of the physical world, advanced reasoning, and general and dexterous control. This report introduces the latest generation of the G…

cs.RO20242 cited

Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs

Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu +19

An elusive goal in navigation research is to build an intelligent agent that can understand multimodal instructions including natural language and image, and perform useful navigat…

cs.RO2024

Learning to Learn Faster from Human Feedback with Language Model Predictive Control

Jacky Liang, Fei Xia, Wenhao Yu +47

Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot beha…

cs.RO2023

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.RO2023273 cited

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Anthony Brohan, Noah Brown, Justice Carbajal +51

We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic…

cs.RO2019

RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies

Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser +2

This paper addresses two challenges facing sampling-based kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationa…