优途加速器破解版永久免费2- quickq下载
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Stanford Intelligent and Interactive Autonomous Systems Group (ILIAD) develops algorithms for autonomous systems that safely and reliably interact with people. Using the tools from artificial intelligence, control theory, robotics, machine learning, and optimization, we develop practical algorithms and the theoretical foundations for interactive robots working with people in uncertain, and safety-critical environments.
优途加速器破解版永久免费2- quickq下载
Check out our YouTube channel for latest talks and supplementary videos for our publications.| Jul 14, 2024: | Our paper titled "Shared Autonomy with Learned Latent Actions" has been nominated for best student paper award at RSS 2024! |
| Jul 13, 2024: | The recordings of our RSS 2024 workshop on "Emergent Behaviors in Human-Robot Systems" are available on YouTube. |
| Jul 7, 2024: | Dorsa is giving a talk on "Active Learning of Robot Reward Functions" at the ICML 2024 Workshop on Real World Experiment Design and Active Learning on July 18. |
| Jul 7, 2024: |
Dorsa is giving talks at three RSS 2024 workshops on July 13: - On "The Role of Learned Representations in Assistive Teleoperation" at AI & Its Alternatives in Assistive & Collaborative Robotics: Decoding Intent - On "When Our Human Modeling Assumptions Fail" at Interaction and Decision-Making in Autonomous-Driving - On "To Ignore Humans or to Accept them with Open Arms: Challenges and Opportunities for Efficient, Robust, and Adaptive POGO Robots" at Power On and Go Robots |
| Jun 25, 2024: | Check our IJRR submission about learning reward functions by optimally combining demonstration and preference data on ladderVNP安卓版. |
| See All |