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20172025
most citedAutomatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation

54 citations · 201 across the 13 of their papers we have counts for

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10 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.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…

cs.RO202220 cited

Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors

Steven Bohez, Saran Tunyasuvunakool, Philemon Brakel +18

We investigate the use of prior knowledge of human and animal movement to learn reusable locomotion skills for real legged robots. Our approach builds upon previous work on imitati…

cs.RO202116 cited

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

Alex X. Lee, Coline Devin, Yuxiang Zhou +18

We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategie…

cs.RO20201 cited

"What, not how": Solving an under-actuated insertion task from scratch

Giulia Vezzani, Michael Neunert, Markus Wulfmeier +7

Robot manipulation requires a complex set of skills that need to be carefully combined and coordinated to solve a task. Yet, most ReinforcementLearning (RL) approaches in robotics…

cs.RO202010 cited

Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion

Roland Hafner, Tim Hertweck, Philipp Klöppner +6

Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that t…