This page shows the most relevant public items for Robotics, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Mar 4, 2026 • arXiv • Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
Reward learning from human preferences often suffers from spurious correlations, leading agents to develop brittle and misaligned behaviors. The authors present a framework that integrates causal i...
Paper • Aug 14, 2025 • arXiv • Sergey Levine, Chelsea Finn, Ankur Handa, Dieter Fox
While Agentic AI has mastered digital environments through API integrations, transferring this autonomous orchestration to the physical world remains a significant challenge. This paper presents a ...
Paper • Mar 20, 2017 • arXiv • Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel
Bridging the 'reality gap' between simulated environments and the physical world is a major challenge in robotics. We introduce domain randomization, a simple yet powerful technique for training ne...
Paper • Jul 5, 2017 • arXiv • Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, Wojciech Zaremba
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay (HER) which allows sample-efficient lear...
Paper • Jan 14, 2021 • arXiv • OpenAI Robotics Team
Training robots to solve a wide variety of manipulation tasks typically requires massive amounts of human-engineered reward functions and goal specifications. We introduce a method for automatic go...
Paper • Jul 30, 2018 • arXiv • Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Jozefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, Wojciech Zaremba
We demonstrate that reinforcement learning algorithms can be used to learn highly dexterous, in-hand manipulation policies that successfully transfer to the real world. We train a policy to control...
Paper • Oct 15, 2019 • arXiv • Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Peter Welinder, Lilian Weng, Wojciech Zaremba, Lei Zhang
We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. We use reinforcement learning to train a policy to sol...
Paper • Oct 13, 2023 • arXiv • Open X-Embodiment Collaboration (Google DeepMind & Academic Partners)
Large, diverse datasets have catalyzed breakthroughs in natural language and computer vision, yet robotics has struggled to build generalist models due to the fragmented nature of hardware platform...
Paper • Dec 13, 2022 • arXiv • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Google DeepMind
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets. We i...
Paper • Jul 28, 2023 • arXiv • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Irpan, Google DeepMind
We introduce Robotic Transformer 2 (RT-2), a novel Vision-Language-Action (VLA) model that learns from both vast web datasets and specialized robotics data. We show that high-capacity vision-langua...
Paper • Jun 20, 2023 • arXiv • Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, DeepMind Robotics Team
Creating general-purpose robots requires models that can rapidly adapt to new tasks and new physical embodiments. We present RoboCat, a self-improving foundation agent for robotic manipulation. Rob...
Paper • Feb 11, 2026 • arXiv • Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Jinjie Gu
Vision-language-action (VLA) models often suffer from the state gap issue when inferring open and long-horizon tasks in scientific scenarios. We propose an LLM-based agentic inference mechanism tha...
Paper • Feb 19, 2026 • arXiv • Sergey Levine, Chelsea Finn, Marcus Sterling
While digital agents have mastered software-based orchestration, integrating these cognitive loops with physical robotics remains a challenge. We present a unified framework that closes the 'Agenti...
Paper • Feb 10, 2026 • arXiv • Kewei Hu, Michael Zhang, Hanwen Kang
We introduce Grounded Scene-graph Reasoning (GSR), a structured reasoning paradigm that explicitly models world-state evolution as transitions over semantically grounded scene graphs. By reasoning ...
Paper • Feb 12, 2026 • arXiv • Wancai Zheng, Hao Chen, Xinyi Yu
3D Gaussian Splatting (3DGS) is a photorealistic rendering method but lacks semantics and physical executability for Visual-Language Navigation (VLN). We propose SAGE-3D, a paradigm that upgrades 3...
Paper • Feb 8, 2026 • arXiv • Eva Mungai, Zach Nobles, Scott Kuindersma, Yeuhi Abe
We introduce ZEST (Zero-shot Embodied Skill Transfer), a motion-imitation framework that trains policies via RL from diverse sources—mocap, noisy monocular video, and animation—and deploys them to ...
Paper • Feb 13, 2026 • arXiv • Zhihong Liu, Yang Li, Rengming Huang, Cewu Lu
Open world language conditioned task planning is crucial for robots in large-scale households. A key challenge remains scalability; performance degrades with increasing environment size and plan le...
Paper • Feb 12, 2026 • arXiv • Xiang Liu, Guocai Yao, Min Zhang, Changshui Zhang
The goal of this paper is to improve VLA performance through iterative online interaction. Since collecting real-world rollouts is expensive, we investigate whether a learned simulator—an action-co...
Paper • Feb 13, 2026 • arXiv • Jacky Kwok, Xilun Zhang, Marco Pavone, Chelsea Finn
We investigate test-time verification as a means to shrink the 'intention-action gap' in embodied AI. We characterize the test-time scaling law for embodied instruction following and demonstrate th...
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