activity
20172025
most citedLeveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

496 citations · 1k across the 25 of their papers we have counts for

collaborators

26 papers

cs.RO2025★ 1 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.RO2025

ExoStart: Efficient learning for dexterous manipulation with sensorized exoskeleton demonstrations

Zilin Si, Jose Enrique Chen, M. Emre Karagozler +15

Recent advancements in teleoperation systems have enabled high-quality data collection for robotic manipulators, showing impressive results in learning manipulation at scale. This…

cs.RO2025★ 6 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…

cs.LG2024

Learning from negative feedback, or positive feedback or both

Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari +9

Existing preference optimization methods often assume scenarios where paired preference feedback (preferred/positive vs. dis-preferred/negative examples) is available. This require…

cs.AI2024

Game On: Towards Language Models as RL Experimenters

Jingwei Zhang, Thomas Lampe, Abbas Abdolmaleki +2

We propose an agent architecture that automates parts of the common reinforcement learning experiment workflow, to enable automated mastery of control domains for embodied agents.…

cs.RO2024

Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning

Mohak Bhardwaj, Thomas Lampe, Michael Neunert +6

Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamic…