Samuel Bustamante-Gomez

EDBT 2026 Demo / reviewers in the wild / expert

dblp:400/2707 · DBLP profile ↗

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7ranked-venue papers

3as first author

5since 2021 · last 2025

0000-0002-7923-8307ORCID · reported

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

Artificial intelligence and machine learning · 6 · 2 first-author · 4 since 2021Systems, architecture and hardware · 6 · 2 first-author · 4 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 first-author · 1 since 2021

Year	Publication	Venue	Position
2025	RACCOON: Grounding Embodied Question-Answering with State Summaries from Existing Robot Modules abstract Explainability is vital for establishing user trust, also in robotics. Recently, foundation models (e.g. vision-language models, VLMs) fostered a wave of embodied agents that answer arbitrary queries about their environment and their interactions with it. However, naively prompting VLMs to answer queries based on camera images does not take into account existing robot architectures which represent the robot's tasks, skills, and beliefs about the state of the world. To overcome this limitation, we propose RACCOON, a framework that combines foundation models' responses with a robot's internal knowledge. Inspired by Retrieval-Augmented Generation (RAG), RACCOON selects relevant context, retrieves information from the robot's state, and utilizes it to refine prompts for an LLM to answer questions accurately. This bridges the gap between the model's adaptability and the robot's domain expertise. Samuel Bustamante-Gomez, Markus Knauer, Jeremias Thun, Stefan Schneyer, Alin Albu-Schäffer, Bernhard M. Weber, Freek Stulp	ICRA	1
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	203
2022	CATs: Task Planning for Shared Control of Assistive Robots with Variable Autonomy abstract From robotic space assistance to healthcare robotics, there is increasing interest in robots that offer adaptable levels of autonomy. In this paper, we propose an action representation and planning framework that is able to generate plans that can be executed with both shared control and supervised autonomy, even switching between them during task execution. The action representation - Constraint Action Templates (CATs) - combine the advantages of Action Templates [1] and Shared Control Templates [2]. We demonstrate that CATs enable our planning framework to generate goal-directed plans for variations of a typical task of daily living, and that users can execute them on the wheelchair-robot EDAN in shared control or in autonomous mode. Samuel Bustamante-Gomez, Gabriel Quere, Daniel Leidner, Jörn Vogel, Freek Stulp	ICRA	1
2021	Learning and Interactive Design of Shared Control Templates abstract Controlling a robotic arm to achieve manipulation tasks is challenging for humans. Especially if only low-dimensional input signals can be provided, as is often the case for users with motor impairments. Using shared control to provide task-specific guidance and constraints facilitates control – for instance with the Shared Control Templates (SCT) framework – and enables even complex activities of daily living to be performed successfully. However, designing SCTs is a laborious task requiring robotic expertise. To make such design easier and faster, we propose a method for semi-automatically designing SCTs on the basis of demonstrations. Furthermore, we propose two similarity metrics, and demonstrate how these can be used to transfer knowledge from one SCT to another. We demonstrate that the SCTs so acquired can be successfully used in shared control for everyday tasks such as opening a drawer or a cupboard on our assistive robot EDAN. Gabriel Quere, Samuel Bustamante-Gomez, Annette Hagengruber, Jörn Vogel, Franz Steinmetz, Freek Stulp	IROS	2
2021	ArmSym: A Virtual Human-Robot Interaction Laboratory for Assistive Robotics abstract Research in human–robot interaction for assistive robotics usually presents many technical challenges for experimenters, forcing researchers to split their time between solving technical problems and conducting experiments. In addition, previous work in virtual reality setups tends to focus on a single assistive robotics application. In order to alleviate these problems, we present ArmSym, a virtual reality laboratory with a fully simulated and developer-friendly robot arm. The system is intended as a testbed to run many sorts of experiments on human control of a robotic arm in a realistic environment, ranging from an upper limb prosthesis to a wheelchair-mounted robotic manipulator. To highlight the possibilities of this system, we perform a study comparing different sorts of prosthetic control types. Looking at nonimpaired subjects, we study different psychological metrics that evaluate the interaction of the user with the robot under different control conditions. Subjects report a perception of embodiment in the absence of realistic cutaneous touch, supporting previous studies in the topic. We also find interesting correlations between control and perceived ease of use. Overall our results confirm that ArmSym can be used to gather data from immersive experiences prosthetics, opening the door to closer collaboration between device engineers and experience designers in the future. Samuel Bustamante-Gomez, Jan Peters 0001, Bernhard Schölkopf, Moritz Grosse-Wentrup, Vinay Jayaram	IEEE Trans. Hum. Mach. Syst.	1
2020	Shared Control Templates for Assistive Robotics abstract Light-weight robotic manipulators can be used to restore the manipulation capability of people with a motor disability. However, manipulating the environment poses a complex task, especially when the control interface is of low bandwidth, as may be the case for users with impairments. Therefore, we propose a constraint-based shared control scheme to define skills which provide support during task execution. This is achieved by representing a skill as a sequence of states, with specific user command mappings and different sets of constraints being applied in each state. New skills are defined by combining different types of constraints and conditions for state transitions, in a human-readable format. We demonstrate its versatility in a pilot experiment with three activities of daily living. Results show that even complex, high-dimensional tasks can be performed with a low-dimensional interface using our shared control approach. Gabriel Quere, Annette Hagengruber, Maged Iskandar, Samuel Bustamante-Gomez, Daniel Leidner, Freek Stulp, Jörn Vogel	ICRA	4
2020	EDAN: An EMG-controlled Daily Assistant to Help People With Physical Disabilities abstract Injuries, accidents, strokes, and other diseases can significantly degrade the capabilities to perform even the most simple activities in daily life. A large share of these cases involves neuromuscular diseases, which lead to severely reduced muscle function. However, even though affected people are no longer able to move their limbs, residual muscle function can still be existent. Previous work has shown that this residual muscular activity can suffice to apply an EMG-based user interface. In this paper, we introduce DLR's robotic wheelchair EDAN (EMG-controlled Daily Assistant), which is equipped with a torque-controlled, eight degree-of-freedom light-weight arm and a dexterous, five-fingered robotic hand. Using electromyography, muscular activity of the user is measured, processed and utilized to control both the wheelchair and the robotic manipulator. This EMG-based interface is enhanced with shared control functionality to allow for efficient and safe physical interaction with the environment. Jörn Vogel, Annette Hagengruber, Maged Iskandar, Gabriel Quere, Ulrike Leipscher, Samuel Bustamante-Gomez, Alexander Dietrich, Hannes Höppner, Daniel Leidner, Alin Albu-Schäffer	IROS	6