Hi there! I'm a co-founder of Physical Intelligence (Pi) where we aim to bring AI into the physical world. Before starting Pi, I was a Staff Research Scientist at Google Deepmind and an Adjunct Professor at Stanford. I'm interested in enabling robots to acquire general-purpose skills in the real world. I also co-taught a class at Stanford on Deep Reinforcement Learning (CS 224R).
I'm happy to announce that We're starting a new company focused on solving Physical Intelligence (Pi). More updates soon!
I gave an Early Career Keynote at CoRL 2023. Video coming soon!
Happy to announce our biggest collaboration up-to-date: RT-X. It includes over 170 authors, 34 institutions and a dataset with over 1M episodes. Enjoy!
If you want to check if your visual representations will enable better generalization in robotics, go check out Kaylee Burns' recent work on segmenting features.
Our paper on RT-2 is out! See the coverage in the New York Times article (front page!) and podcast.
Our paper on PaLM-E is out!
Our new paper on SayCan is out! We did an interview on this work on Yannic's youtube channel (and the paper explanation here). You can also see a short description of it on Two Minutes Papers channel and in my twitter thread.
I split my research into three buckets: deep robotic learning, interactive and active perception but these days, I focus entirely on the first one. Lately I've been particularly excited about building and using foundation models for decision making. My research has been covered by a number of news outlets including: The New York Times (front page!), The Washington Post, 60 minutes, Techcrunch, WiRED, Quanta Magazine, CNET, The Verge and others. You can find all my work on my Google Scholar:
In my research on deep robotic learning, I focus on how we can apply modern deep learning techniques to various robotic applications.
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
under submission, 2023 A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Rai, A. Singh, A. Brohan, A. Raffin, A. Wahid, B. Burgess-Limerick, B. Kim, B. Schölkopf, B. Ichter, C. Lu, C. Xu, C. Finn, C. Xu, C. Chi, C. Huang, C. Chan, C. Pan, C. Fu, C. Devin, D. Driess, D. Pathak, D. Shah, D. Büchler, D. Kalashnikov, D. Sadigh, E. Johns, F. Ceola, F. Xia, F. Stulp, G. Zhou, G. S. Sukhatme, G. Salhotra, G. Yan, G. Schiavi, G. Kahn, H. Su, H. Fang, H. Shi, H. Ben Amor, H. I Christensen, H. Furuta, H. Walke, H. Fang, I. Mordatch, I. Radosavovic, I. Leal, J. Liang, J. Abou-Chakra, J. Kim, J. Peters, J. Schneider, J. Hsu, J. Bohg, J. Bingham, J. Wu, J. Wu, J. Luo, J. Gu, J. Tan, J. Oh, J. Malik, J. Tompson, J. Yang, J. J. Lim, J. Silvério, J. Han, K. Rao, K. Pertsch, K. Hausman, K. Go, K. Gopalakrishnan, K. Goldberg, K. Byrne, K. Oslund, K. Kawaharazuka, K. Zhang, K. Rana, K. Srinivasan, L. Y. Chen, L. Pinto, L. Tan, L. Ott, L. Lee, M. Tomizuka, M. Du, M. Ahn, M. Zhang, M. Ding, M. K. Srirama, M. Sharma, M. J. Kim, N. Kanazawa, N. Hansen, N. Heess, N. J. Joshi, N. Suenderhauf, N. Di Palo, N. M. M. Shafiullah, O. Mees, O. Kroemer, P. R. Sanketi, P. Wohlhart, P. Xu, P. Sermanet, P. Sundaresan, Q. Vuong, R. Rafailov, R. Tian, R. Doshi, R. Martín-Martín, R. Mendonca, R. Shah, R. Hoque, R. Julian, S. Bustamante, S. Kirmani, S. Levine, S. Moore, S. Bahl, S. Dass, S. Sonawani, S. Song, S. Xu, S. Haldar, S. Adebola, S. Guist, S. Nasiriany, S. Schaal, S. Welker, S. Tian, S. Dasari, S. Belkhale, T. Osa, T. Harada, T. Matsushima, T. Xiao, T. Yu, T. Ding, T. Davchev, T. Z. Zhao, T. Armstrong, T. Darrell, V. Jain, V. Vanhoucke, W. Zhan, W. Zhou, W. Burgard, X. Chen, X. Wang, X. Zhu, X. Li, Y. Lu, Y. Chebotar, Y. Zhou, Y. Zhu, Y. Xu, Y. Wang, Y. Bisk, Y. Cho, Y. Lee, Y. Cui, Y. Wu, Y. Tang, Y. Zhu, Y. Li, Y. Iwasawa, Y. Matsuo, Z. Xu, Z. J. Cui. |
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What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
under submission, 2023 K. Burns, Z. Witzel, J. Ibn Hamid, T. Yu, C. Finn, K. Hausman |
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RoboVQA: Multimodal Long-Horizon Reasoning
for Robotics under submission, 2023 P. Sermanet, T. Ding, J. Zhao, F. Xia, D. Dwibedi, K. Gopalakrishnan, C. Chan, G. Dulac-Arnold, S. Maddineni, N. J. Joshi, P. Florence, W. Han, R. Baruch, Y. Lu, S. Mirchandani, P. Xu, P. Sanketi, K. Hausman, I. Shafran, B. Ichter, Y. Cao |
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RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches under submission, 2023 J. Gu, S. Kirmani, P. Wohlhart, Y. Lu, M. Gonzalez Arenas, K. Rao, W. Yu, C. Fu, K. Gopalakrishnan, Z. Xu, P. Sundaresan, P. Xu, H. Su, K. Hausman, C. Finn, Q. Vuong, T. Xiao |
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RT-Sketch: Goal-Conditioned Imitation
Learning from Hand-Drawn Sketches under submission, 2023 P. Sundaresan, Q. Vuong, J. Gu, P. Xu, T. Xiao, S. Kirmani, T. Yu, M. Stark, A. Jain, K. Hausman, D. Sadigh, J. Bohg, S. Schaal |
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Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Conference on Robot Learning (CoRL), 2023 Y. Chebotar, Q. Vuong, A. Irpan, K. Hausman, F. Xia, Y. Lu, A. Kumar, T. Yu, A. Herzog, K. Pertsch, K. Gopalakrishnan, J. Ibarz, O. Nachum, S. Sontakke, G. Salazar, H. T. Tran, J. Peralta, C. Tan, D. Manjunath, J. Singht, B. Zitkovich, T. Jackson, K. Rao, C. Finn, S. Levine |
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RT-2: Vision-Language-Action Models
Transfer Web Knowledge to Robotic Control Conference on Robot Learning (CoRL), 2023 A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, P. Florence, C. Fu, M. Gonzalez Arenas, K. Gopalakrishnan, K. Han, K. Hausman, A. Herzog, J. Hsu, B. Ichter, A. Irpan, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, L. Lee, T. E. Lee, S. Levine, Y. Lu, H. Michalewski, I. Mordatch, K. Pertsch, K. Rao, K. Reymann, M. Ryoo, G. Salazar, P. Sanketi, P. Sermanet, J. Singh, A. Singh, R. Soricut, H. Tran, V. Vanhoucke, Q. Vuong, A. Wahid, S. Welker, P. Wohlhart, J. Wu, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, B. Zitkovich |
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Open-World Object Manipulation using Pre-Trained Vision-Language Models Conference on Robot Learning (CoRL), 2023 A. Stone*, T. Xiao*, Y. Lu*, K. Gopalakrishnan, K. Lee, Q. Vuong, P. Wohlhart, B. Zitkovich, F. Xia, C. Finn, K. Hausman |
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Grounded Decoding:
Guiding Text Generation with Grounded Models for Robot Control Neural Information Processing Systems (NeurIPS), 2023 W. Huang, F. Xia, D. Shah, A. Zeng, Y. Lu, P. Florence, I. Mordatch, S. Levine, K. Hausman, B. Ichter |
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PaLM-E: An Embodied Multimodal Language Model
International Conference on Machine Learning (ICML), 2023 D. Driess, F. Xia, M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, P. Florence |
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Deep RL at Scale:
Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators Robotics: Science and Systems (RSS), 2023 A. Herzog*, K. Rao*, K. Hausman*, Y. Lu*, P. Wohlhart*, M. Yan, J. Lin, M. Gonzalez Arenas, T. Xiao, D. Kappler, D. Ho, J. Rettinghouse, Y. Chebotar, K. Lee, K. Gopalakrishnan, R. Julian, A. Li, C. Fu, B. Wei, S. Ramesh, K. Holden, K. Kleiven, D. Rendleman, S. Kirmani, J. Bingham, J. Weisz, Y. Xu, W. Lu, M. Bennice, C. Fong, D. Do, J. Lam, Y. Bai, B. Holson, M. Quinlan, N. Brown, M. Kalakrishnan, J. Ibarz, P. Pastor, S. Levine |
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Scaling Robot Learning with Semantically Imagined Experience Robotics: Science and Systems (RSS), 2023 T. Yu, T. Xiao, A. Stone, J. Tompson, A. Brohan, S. Wang, J. Singh, C. Tan, Dee M, J. Peralta, B. Ichter, K. Hausman, F. Xia |
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Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models
Robotics: Science and Systems (RSS), 2023 T. Xiao*, H. Chan*, P. Sermanet, A. Wahid, A. Brohan, K. Hausman, S. Levine, J. Tompson |
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RT-1: Robotics Transformer
for Real-World Control at Scale Robotics: Science and Systems (RSS), 2023 A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu, J. Ibarz, B. Ichter, A. Irpan, T. Jackson, S. Jesmonth, N. Joshi, R. Julian, D. Kalashnikov, Y. Kuang, I. Leal, K. Lee, S. Levine, Y. Lu, U. Malla, D. Manjunath, I. Mordatch, O. Nachum, C. Parada, J. Peralta, E. Perez, K. Pertsch, J. Quiambao, K. Rao, M. Ryoo, G. Salazar, P. Sanketi, K. Sayed, J. Singh, S. Sontakke, A. Stone, C. Tan, H. Tran, V. Vanhoucke, S. Vega, Q. Vuong, F. Xia, T. Xiao, P. Xu, S. Xu, T. Yu, B. Zitkovich |
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Code as Policies: Language Model Programs for Embodied Control International Conference on Robotics and Automation (ICRA), 2023 Best Learning Paper J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, A. Zeng |
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Inner Monologue: Embodied Reasoning through Planning with Language Models Conference on Robot Learning (CoRL), 2022 W. Huang*, F. Xia*, T. Xiao*, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar, P. Sermanet, N. Brown, T. Jackson, L. Luu, S. Levine, K. Hausman, B. Ichter |
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Offline Reinforcement Learning at Multiple Frequencies Conference on Robot Learning (CoRL), 2022 K. Burns, T. Yu, C. Finn, K. Hausman |
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Do As I Can, Not As I Say: Grounding Language in Robotic Affordances Conference on Robot Learning (CoRL), 2022 Special Innovation Award Michael Ahn*, Anthony Brohan*, Noah Brown*, Yevgen Chebotar*, Omar Cortes*, Byron David*, Chelsea Finn*, Keerthana Gopalakrishnan*, Karol Hausman*, Alex Herzog*, Daniel Ho*, Jasmine Hsu*, Julian Ibarz*, Brian Ichter*, Alex Irpan*, Eric Jang*, Rosario Jauregui Ruano*, Kyle Jeffrey*, Sally Jesmonth*, Nikhil J Joshi*, Ryan Julian*, Dmitry Kalashnikov*, Yuheng Kuang*, Kuang-Huei Lee*, Sergey Levine*, Yao Lu*, Linda Luu*, Carolina Parada*, Peter Pastor*, Jornell Quiambao*, Kanishka Rao*, Jarek Rettinghouse*, Diego Reyes*, Pierre Sermanet*, Nicolas Sievers*, Clayton Tan*, Alexander Toshev*, Vincent Vanhoucke*, Fei Xia*, Ted Xiao*, Peng Xu*, Sichun Xu*, Mengyuan Yan* |
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Jump-Start Reinforcement Learning International Conference on Machine Learning (ICML), 2023 I. Uchendu, T. Xiao, Y. Lu, B. Zhu, M. Yan, J. Simon, M. Bennice, C. Fu, C. Ma, J. Jiao, S. Levine, K. Hausman |
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Demonstration-Bootstrapped Autonomous Practicing via Multi-Task Reinforcement Learning International Conference on Robotics and Automation (ICRA), 2023 A. Gupta, C. Lynch, B. Kinman, G. Peake, S. Levine, K. Hausman |
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How to Leverage Unlabeled Data in Offline Reinforcement Learning International Conference on Machine Learning (ICML), 2022 T. Yu*, A. Kumar*, Y. Chebotar, K. Hausman, C. Finn, S. Levine |
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Autonomous Reinforcement Learning: Formalism and Benchmarking International Conference on Learning Representations (ICLR), 2022 A. Sharma*, K. Xu*, N. Sardana, A. Gupta, K. Hausman, S. Levine, C. Finn |
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Conservative Data Sharing for Multi-Task Offline Reinforcement Learning Neural Information Processing Systems (NeurIPS), 2021 T. Yu*, A. Kumar*, Y. Chebotar, K. Hausman, S. Levine, C. Finn |
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Persistent Reinforcement Learning via Subgoal Curricula Neural Information Processing Systems (NeurIPS), 2021 A. Sharma, A. Gupta, S. Levine, K. Hausman, C. Finn |
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MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale Conference on Robot Learning (CoRL), 2021 D. Kalashnikov*, J. Varley*, Y. Chebotar, B. Swanson, R. Jonschkowski, C. Finn, S. Levine, K. Hausman* |
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AW-Opt: Learning Robotic Skills with
Imitation and Reinforcement Learning at Scale Conference on Robot Learning (CoRL), 2021 Yao Lu, Karol Hausman, Yevgen Chebotar, Mengyuan Yan, Eric Jang, Alexander Herzog, Ted Xiao, Alex Irpan, Mohi Khansari, Dmitry Kalashnikov, Sergey Levine |
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Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills International Conference on Machine Learning (ICML), 2021 Y.Chebotar, K. Hausman, Y. Lu, T. Xiao, D. Kalashnikov, J. Varley, A. Irpan, B. Eysenbach, R. Julian, C. Finn, S. Levine |
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A Geometric Perspective on Self-Supervised Policy Adaptation International Conference on Robotics and Automation (ICRA), 2020 C. Bodnar, K. Hausman, G. Dulac-Arnold, R. Jonschkowski |
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Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning Conference on Robot Learning (CoRL), 2020 R. Julian, B. Swanson, G. Sukhatme, S. Levine, C. Finn, K. Hausman |
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Modeling Long-horizon Tasks as Sequential Interaction Landscapes Conference on Robot Learning (CoRL), 2020 S. Pirk, K. Hausman, A. Toshev, M. Khansari |
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Gradient Surgery for Multi-Task Learning Neural Information Processing Systems (NeurIPS), 2020 T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, C. Finn |
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Quantile QT-Opt for Risk-Aware
Vision-Based Robotic Grasping Robotics: Science and Systems (RSS), 2020 Best Systems Paper Finalist C. Bodnar, A. Li, K. Hausman, P. Pastor, M. Kalakrishnan |
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Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning Robotics: Science and Systems (RSS), 2020 A. Sharma, M. Ahn, S. Levine, V. Kumar, K. Hausman*, S. Gu* |
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Thinking While Moving: Deep Reinforcement Learning with Concurrent Control International Conference on Learning Representations (ICLR), 2020 T. Xiao, E. Jang, D. Kalashnikov, S. Levine, J. Ibarz, K. Hausman*, A. Herzog* |
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Dynamics-Aware Unsupervised Discovery of Skills International Conference on Learning Representations (ICLR), 2020 oral presentation A. Sharma, S. Gu, S. Levine, V. Kumar, K. Hausman |
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Learning to Interactively
Learn and Assist AAAI 2020, oral presentation M. Woodward, C. Finn, K. Hausman |
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Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning Conference on Robot Learning (CoRL), 2019 A. Gupta, V. Kumar, C. Lynch, S. Levine, K. Hausman |
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Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Conference on Robot Learning (CoRL), 2019 T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, S. Levine |
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Scaling Simulation-to-real Transfer by Learning Composable Robot Skills. International Symposium on Experimental Robotics (ISER), 2018 International Journal of Robotics Research (IJRR), 2019 R. Julian*, E. Heiden*, Z. He, H. Zhang, S. Schaal, J. Lim, G. Sukhatme, K. Hausman |
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Learning an Embedding Space for Transferable Robot Skills International Conference on Learning Representations (ICLR), 2018 K. Hausman, J.T. Springenberg, Z. Wang, N. Heess, M. Riedmiller |
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Multi-Modal Imitation Learning from Unstructured Demonstrations using GANs Neural Information Processing Systems (NIPS), 2017 K. Hausman*, Y. Chebotar*, S. Schaal, G. Sukhatme, J. Lim |
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Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning International Conference on Machine Learning (ICML), 2017 Y. Chebotar*, K. Hausman*, M. Zhang*, G. Sukhatme, S. Schaal, S. Levine |
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Generalizing Regrasping with Supervised Policy Learning International Symposium on Experimental Robotics (ISER), 2016 Y. Chebotar*, K. Hausman*, O. Kroemer, G. Sukhatme, S. Schaal |
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Self-Supervised Regrasping using Spatio-Temporal Tactile Features and Reinforcement Learning International Conference on Intelligent Robots and Systems (IROS), 2016 Y. Chebotar, K. Hausman, Z. Su, G. Sukhatme, S. Schaal |
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Workshop PublicationsTraining an Interactive HelperNIPS Workshop on Emergent Communication and NIPS Workshop Learning by Instruction, 2018 M. Woodward, C. Finn, K. Hausman pdf Learning a System-ID Embedding Space for Domain Specialization with Deep Reinforcement Learning NIPS Workshop on Reinforcement Learning under Partial Observability, 2018 J. Preiss, K. Hausman, G. Sukhatme pdf Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing NIPS Deep Reinforcement Learning Workshop, 2018 Z. He, R. Julian, E. Heiden, H. Zhang, S. Schaal, J. Lim, G. Sukhatme, K. Hausman pdf Learning Skill Embeddings for Transferable Robot Skills NIPS Deep Reinforcement Learning Symposium, 2017 K. Hausman, J.T. Springenberg, Z. Wang, N. Heess, M. Riedmiller pdf Learning Robot Skill Embeddings NIPS Workshop on Acting and Interacting in the Real World: Challenges in Robot Learning, 2017 K. Hausman, J.T. Springenberg, Z. Wang, N. Heess, M. Riedmiller pdf IntentionGAN: Multi-Task Imitation Learning from Unstructured Demonstrations Conference on Robot Learning (CoRL), 2017 K. Hausman*, Y. Chebotar*, S. Schaal, G. Sukhatme, J. Lim bibtex pdf IntentionGAN: Multi-Modal Imitation Learning from Unstructured Demonstrations RSS Workshop on Learning from Demonstration in High-Dimensional Feature Spaces, 2017 K. Hausman*, Y. Chebotar*, S. Schaal, G. Sukhatme, J. Lim bibtex pdf Combining Model-Based and Model-Free Updates for Deep Reinforcement Learning RSS Workshop on New Frontiers for Deep Learning in Robotics, 2017 Best Paper Award Y. Chebotar*, K. Hausman*, M. Zhang*, G. Sukhatme, S. Schaal, S. Levine bibtex pdf Regrasping using Tactile Perception and Supervised Policy Learning AAAI Symposium on Interactive Multi-Sensory Object Perception for Embodied Agents, 2017 Y. Chebotar, K. Hausman, Z. Su, G. Sukhatme, S. Schaal bibtex pdf Supervised Policy Fusion with Application to Regrasping IROS Workshop on Closed-loop Grasping and Manipulation: Challenges and Progress, 2016 Y. Chebotar*, K. Hausman*, O. Kroemer, G. Sukhatme, S. Schaal bibtex pdf |
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In interactive perception (IP) any kind of forceful interactions with the environment are used to simplify and enhance perception, in turn enabling robust perceptually-guided manipulation. IP has two main benefits: i) physical interaction creates a novel sensory signal that would otherwise not be present, and ii) by exploiting knowledge of the regularity in the combined space of sensory data and action parameters, the prediction and interpretation of this novel signal becomes simpler and more robust. For more details, see our survey paper.
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Interactive Perception: Leveraging Action in Perception and Perception in Action
IEEE Transactions on Robotics (T-RO), 2016 J. Bohg*, K. Hausman*, B. Sankaran*, O. Brock, D. Kragic, S. Schaal, G. Sukhatme |
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Active Articulation Model Estimation through Interactive Perception International Conference on Robotics and Automation (ICRA), 2015 K. Hausman, S. Niekum, S. Osentoski , G. Sukhatme |
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Force Estimation and Slip Detection for Grip Control using a Biomimetic Tactile Sensor International Conference on Humanoid Robotics (Humanoids), 2015 Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. Loeb, G. Sukhatme, S. Schaal |
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Interactive Segmentation of Textured and Textureless Objects
Chapter in Handling Uncertainty and Networked Structure in Robot Control, L. Busoniu and L. Tamas (eds.), Springer, 2015 K. Hausman, D. Pangercic, Z. Marton, F. Belent-Benczedi, C. Bersch, M. Gupta, G. Sukhatme, M. Beetz |
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Tracking-based Interactive Segmentation of Textureless Objects
International Conference on Robotics and Automation (ICRA), 2013 Best Service Robotics Paper Finalist K. Hausman, F. Balint-Benczedi, D. Pangercic, Z. Marton, R. Ueda, K. Okada, M. Beetz |
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Workshop PublicationsBiGS: BioTac Grasp Stability DatasetICRA Workshop on Grasping and Manipulation Datasets, 2016 Y. Chebotar, K. Hausman, Z. Su, A. Molchanov, O. Kroemer, G. Sukhatme, S. Schaal website bibtex pdf Slip Classification Using Tangential and Torsional Skin Distortions on a Biomimetic Tactile Sensor BMVA Workshop on Visual, Tactile and Force Sensing for Robot Manipulation, 2015 Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. Loeb, G. Sukhatme, S. Schaal bibtex pdf Slip Detection and Classification for Grip Control using Multiple Sensory Modalities on a Biomimetic Tactile Sensor IROS Workshop on Multimodal Sensor-Based Robot Control for HRI and Soft Manipulation, 2015 Z. Su, K. Hausman, Y. Chebotar, A. Molchanov, G. Loeb, G. Sukhatme, S. Schaal bibtex pdf Towards Interactive Object Recognition IROS 3rd Workshop on Robots in Clutter: Perception and Interaction in Clutter, 2014 K. Hausman, C. Corcos, J. Mueller, F. Sha, G. Sukhatme bibtex pdf Segmentation of Cluttered Scenes through Interactive Perception ICRA Workshop on Semantic Perception and Mapping for Knowledge-enabled Service Robotics, 2012 K. Hausman, C. Bersch, D. Pangercic, S. Osentoski, Z. Marton, M. Beetz bibtex pdf Segmentation of Textured and Textureless Objects through Interactive Perception RSS Workshop on Robots in Clutter: Manipulation, Perception and Navigation in Human Environments, 2012 C. Bersch, D. Pangercic, S. Osentoski, K. Hausman, Z. Marton, R. Ueda, K. Okada, M. Beetz bibtex pdf |
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Active perception pioneered the insight that perception is active and exploratory. In my research, I try to show that, state estimation (perception) can be significantly improved when considered jointly with control (action). I demonstrate these research insights on various flying vehicles such as quadrotors.
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Simultaneous Self-Calibration and Navigation using Trajectory Optimization International Journal of Robotics Research (IJRR), 2018 J. Preiss, K. Hausman, G. Sukhatme, S. Weiss |
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Confidence-rich Grid Mapping International Symposium on Robotics Research (ISRR), 2017 International Journal of Robotics Research (IJRR), 2019 A. Agha-mohammadi, E. Heiden, K. Hausman, G. Sukhatme |
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Trajectory Optimization for Self-Calibration and Navigation
Robotics: Science and Systems (RSS), 2017 J. Preiss, K. Hausman, G. Sukhatme, S. Weiss |
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Planning High-speed Safe Trajectories in Confidence-rich Map International Conference on Intelligent Robots and Systems (IROS), 2017 E. Heiden, K. Hausman, G. Sukhatme, A. Agha-mohammadi |
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Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs Robotics and Automation Letter (RA-L), 2017 K. Hausman, J. Preiss, G. Sukhatme, S. Weiss |
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Occlusion-Aware Multi-Robot 3D Tracking
International Conference on Intelligent Robots and Systems (IROS), 2016 K. Hausman, G. Kahn, S. Patil, J. Mueller, K. Goldberg, P. Abbeel, G. Sukhatme |
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Cooperative Multi-Robot Control for Target Tracking with Onboard Sensing
International Journal of Robotics Research (IJRR), 2015 K. Hausman, J. Mueller, A. Hariharan, N. Ayanian, G. Sukhatme |
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Self-Calibrating Multi-Sensor Fusion with Probabilistic Measurement Validation for Seamless Sensor Switching on a UAV
International Conference on Robotics and Automation (ICRA), 2016 K. Hausman, S. Weiss, R. Brockers, L. Matthies, G. Sukhatme |
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Cooperative Control for Target Tracking with Onboard Sensing
International Symposium on Experimental Robotics (ISER), 2014 K. Hausman, J. Mueller, A. Hariharan, N.Ayanian, G. Sukhatme |
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Workshop PublicationsConfidence-aware Occupancy GridsIROS Workshop on Vision-based Agile Autonomous Navigation of UAVs, 2017 A. Agha-mohammadi, E. Heiden, K. Hausman, G. Sukhatme bibtex pdf High-speed Safe Trajectory Planning in Confidence-rich Maps IROS Workshop on Vision-based Agile Autonomous Navigation of UAVs, 2017 E. Heiden, K. Hausman, G. Sukhatme, A. Agha-mohammadi bibtex pdf Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs RSS Workshop on Robot-Environment Interaction for Perception and Manipulation, 2016 K. Hausman, J. Preiss, G. Sukhatme, S. Weiss bibtex pdf Optimization-based Cooperative Multi-Robot Target Tracking with Reasoning about Occlusions IROS Workshop on On-line Decision-Making in Multi-Robot Coordination, 2015 K. Hausman, G. Kahn, S. Patil, J. Mueller, K. Goldberg, P. Abbeel, G. Sukhatme bibtex pdf Cooperative Multi-Robot Control for Target Tracking with Efficient Switching of Onboard Sensing Topologies IROS Workshop on Taxonomies of Interconnected Systems: Topology in Distributed Robotics, 2014 K. Hausman, J. Mueller, A. Hariharan, N. Ayanian, G. Sukhatme bibtex pdf |
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