12 citations · 18 across the 3 of their papers we have counts for
3 papers
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…
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…
i-Sim2Real: Reinforcement Learning of Robotic Policies in Tight Human-Robot Interaction Loops
Saminda Abeyruwan, Laura Graesser, David B. D'Ambrosio +6
Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to train policies in simulation enables safe exploration and large-scale data collection…