Antonin Raffin

EDBT 2026 Demo / reviewers in the wild / expert

dblp:225/7772 · DBLP profile ↗

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6ranked-venue papers

1as first author

5since 2021 · last 2024

0000-0001-6036-6950ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 1 first-author · 4 since 2021Systems, architecture and hardware · 4 · 3 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Year	Publication	Venue	Position
2024	Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration abstract Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io. Abigail O'Neill, Abhiram Maddukuri, Abhishek Gupta 0004, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew E. Wang, Anikait Singh, Animesh Garg, Aniruddha Kembhavi, Annie Xie, Anthony Brohan, Antonin Raffin, Archit Sharma, Arefeh Yavary, Arhan Jain, Ashwin Balakrishna, Ayzaan Wahid, Ben Burgess-Limerick, Bernhard Schölkopf, Blake Wulfe, Brian Ichter, Cewu Lu, Charles Xu 0003, Charlotte Le, Chelsea Finn, Chen Wang 0053, Chenfeng Xu, Cheng Chi 0001, Chenguang Huang, Christine Chan, Christopher Agia, Chuer Pan, Chuyuan Fu, Coline Devin, Danfei Xu, Daniel Morton, Danny Drieß, Daphne Chen, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dinesh Jayaraman, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Ethan Paul Foster, Fangchen Liu, Federico Ceola, Fei Xia 0002, Feiyu Zhao, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Gilbert Feng, Giulio Schiavi, Glen Berseth, Gregory Kahn, Guanzhi Wang, Hao Su 0001, Haoshu Fang, Henghui Bao, Heni Ben Amor, Henrik I. Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Huy Ha, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim 0001, Jaimyn Drake, Jan Peters 0001, Jan Schneider 0007, Jasmine Hsu, Jeannette Bohg, Jeffrey T. Bingham, Jensen Gao, Jiaheng Hu, Jiajun Wu 0001, Jiankai Sun, Jianlan Luo, Jiayuan Gu, Jie Tan 0001, Jihoon Oh, Jimmy Wu, Jingpei Lu, Jitendra Malik, João Silvério, Joey Hejna, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Jordi Salvador, Joseph J. Lim, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Kenneth Y. Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Black, Kevin Zhang 0002, Kiana Ehsani, Kiran Lekkala, Kirsty Ellis, Krishan Rana, Krishnan Srinivasan, Kuan Fang, Kunal Pratap Singh, Kuo-Hao Zeng, Kyle Hatch, Kyle Hsu, Laurent Itti, Yunliang Chen 0001, Lerrel Pinto, Li Fei-Fei 0001, Liam Tan, Linxi Fan, Lionel Ott, Lisa Lee, Luca Weihs, Magnum Chen, Marion Lepert, Marius Memmel, Masayoshi Tomizuka, Masha Itkina, Mateo Guaman Castro, Max Spero, Maximilian Du, Michael Ahn, Michael C. Yip, Mingtong Zhang 0003, Mingyu Ding, Minho Heo, Mohan Kumar Srirama, Mohit Sharma 0001, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen 0001, Nicolas Heess, Nikhil J. Joshi, Niko Sünderhauf, Norman Di Palo, Nur Muhammad Shafiullah, Oier Mees, Oliver Kroemer, Osbert Bastani, Pannag R. Sanketi, Patrick Tree Miller, Patrick Yin, Paul Wohlhart, Peng Xu 0010, Peter David Fagan, Peter Mitrano, Pierre Sermanet, Pieter Abbeel, Priya Sundaresan, Qiuyu Chen, Rafael Rafailov, Ria Doshi, Roberto Martin Martin, Rohan Baijal, Rosario Scalise, Rose Hendrix, Roy Lin, Runjia Qian, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante-Gomez, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham D. Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Siddharth Karamcheti, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Subramanian Ramamoorthy, Sudeep Dasari, Suneel Belkhale, Sungjae Park, Suraj Nair 0003, Suvir Mirchandani, Takayuki Osa, Tanmay Gupta, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Thomas Kollar, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Trinity Chung, Vidhi Jain, Vincent Vanhoucke, Wolfram Burgard, Xiaolong Wang 0004, Xinghao Zhu, Xinyang Geng, Liangwei Xu, Yecheng Jason Ma 0001, Yejin Kim 0003, Yevgen Chebotar, Yilin Wu 0003, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunchu Zhang, Yunfan Jiang 0001, Yunshuang Li, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zehan Ma, Zichen Jeff Cui, Zichen Zhang 0016, Zipeng Lin	ICRA	24
2024	Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds abstract Controlled execution of dynamic motions in quadrupedal robots, especially those with articulated soft bodies, presents a unique set of challenges that traditional methods struggle to address efficiently. In this study, we tackle these issues by relying on a simple yet effective two-stage learning framework to generate dynamic motions for quadrupedal robots. First, a gradient-free evolution strategy is employed to discover simply represented control policies, eliminating the need for a predefined reference motion. Then, we refine these policies using deep reinforcement learning. Our approach enables the acquisition of complex motions like pronking and back-flipping, effectively from scratch. Additionally, our method simplifies the traditionally labour-intensive task of reward shaping, boosting the efficiency of the learning process. Importantly, our framework proves particularly effective for articulated soft quadrupeds, whose inherent compliance and adaptability make them ideal for dynamic tasks but also introduce unique control challenges. Francesco Vezzi, Jiatao Ding, Antonin Raffin, Jens Kober, Cosimo Della Santina	ICRA	3
2024	Tele-Running: Trajectory Generation for Monopod Robots by Teleoperation abstract Energy-efficient legged locomotion in robots depends on exploiting passive dynamics, particularly with integrated mechanical compliance. Traditional model-based control strategies that leverage these dynamics often encounter challenges due to inherent model uncertainties. We propose a novel model-free method for generating motor trajectories in compliantly actuated monopods using teleoperation with force feedback. This approach allows operators to detect ground reaction forces and excite the robot's natural frequency, achieving highly efficient hopping. The method results in a mechanical Cost of Transport (CoT) of 0.25 at 0.63 m S−1on an articulated hopper. To further enhance energy efficiency and adjust for hardware variations, these trajectories are refined using Black-Box Optimization (BBO) directly on the hardware. Experimental results confirm that these optimized trajectories closely match the efficiency of those initiated by humans, demonstrating the effectiveness of this method in exciting the robot's natural dynamics. David Wandinger, Annika Schmidt, Antonin Raffin, Alin Albu-Schäffer, Manuel Keppler	SMC	3
2023	Guiding Reinforcement Learning with Shared Control Templates abstract Purposeful interaction with objects usually requires certain constraints to be respected, e.g. keeping a bottle upright to avoid spilling. In reinforcement learning, such constraints are typically encoded in the reward function. As a consequence, constraints can only be learned by violating them. This often precludes learning on the physical robot, as it may take many trials to learn the constraints, and the necessity to violate them during the trial-and-error learning may be unsafe. We have serendipitously discovered that constraint representations for shared control - in particular Shared Control Templates (SCTs) - are ideally suited for safely guiding RL. Representing constraints explicitly, rather than implicitly in the reward function, also simplifies the design of the reward function. The main advantage of the approach is safer, faster learning without constraint violations (even with sparse reward functions). We demonstrate this in a pouring task in simulation and on a real robot, where learning the task requires only 65 episodes in 16 minutes. Abhishek Padalkar, Gabriel Quere, Franz Steinmetz, Antonin Raffin, Matthias Nieuwenhuisen, João Silvério, Freek Stulp	ICRA	4
2021	Stable-Baselines3: Reliable Reinforcement Learning Implementations abstract Stable-Baselines3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python. The implementations have been benchmarked against reference codebases, and automated unit tests cover 95% of the code. The algorithms follow a consistent interface and are accompanied by extensive documentation, making it simple to train and compare different RL algorithms. Our documentation, examples, and source-code are available at https://github.com/DLR-RM/stable-baselines3. Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann	J. Mach. Learn. Res.	1
2019	Decoupled Control of Position and / or Force of Tendon Driven Fingers abstract In contrast to underactuated robotic hands the DLR AWIWI II hand of the David robot is fully controllable because each finger with 4 joints is actuated by 6 or 8 tendons respectively. For such fingers all joint angles (generalized positions) or joint torques (generalized forces) can be controlled independently. Usually, the specifications in joint space are converted to desired tendon forces or motor torques, which are regulated by an inner loop impedance controller. However, this conversion typically exhibits couplings between the components of the joint angle vector or the joint torque vector respectively, which arise when using the well known equations. Therefore the usual force control and position control schemes are reviewed and a generic computation of the desired tendon forces is presented. This is also done for the control of the Cartesian position and force at the finger endpoint. Thus the main contribution of the paper is the inhibition of couplings in joint space or at the Cartesian endpoint. This is demonstrated in simulations of the index finger of the DLR David hand. Friedrich Lange, Gabriel Quere, Antonin Raffin	ICRA	3