activity
20202025
most citedGemini Robotics: Bringing AI into the Physical World

6 citations · 9 across the 5 of their papers we have counts for

collaborators

11 papers

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.LG2025

Self-Improving Embodied Foundation Models

Seyed Kamyar Seyed Ghasemipour, Ayzaan Wahid, Jonathan Tompson +2

Foundation models trained on web-scale data have revolutionized robotics, but their application to low-level control remains largely limited to behavioral cloning. Drawing inspirat…

cs.RO2025

Robo-DM: Data Management For Large Robot Datasets

Kaiyuan Chen, Letian Fu, David Huang +9

Recent results suggest that very large datasets of teleoperated robot demonstrations can be used to train transformer-based models that have the potential to generalize to new scen…

cs.RO2025

Towards Embodiment Scaling Laws in Robot Locomotion

Bo Ai, Liu Dai, Nico Bohlinger +7

Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…

cs.RO2025

SAS-Prompt: Large Language Models as Numerical Optimizers for Robot Self-Improvement

Heni Ben Amor, Laura Graesser, Atil Iscen +7

We demonstrate the ability of large language models (LLMs) to perform iterative self-improvement of robot policies. An important insight of this paper is that LLMs have a built-in…

cs.RO20256 cited

Gemini Robotics: Bringing AI into the Physical World

Gemini Robotics Team, Saminda Abeyruwan, Joshua Ainslie +115

Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as…