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.
Recent NewsCheck out our YouTube channel for latest talks and supplementary videos for our publications.
|Aug 12, 2020:||
Our 2 papers got accepted at IROS 2020:
- "Learning User-Preferred Mappings for Intuitive Robot Control" by Li et al.
- "Multi-Agent Safe Planning with Gaussian Processes" by Zhu et al.
|Jul 14, 2020:||Our paper titled "Shared Autonomy with Learned Latent Actions" has been nominated for best student paper award at RSS 2020!|
|Jul 13, 2020:||The recordings of our RSS 2020 workshop on "Emergent Behaviors in Human-Robot Systems" are available on YouTube.|
|Jul 7, 2020:||Dorsa is giving a talk on "Active Learning of Robot Reward Functions" at the ICML 2020 Workshop on Real World Experiment Design and Active Learning on July 18.|
|Jul 7, 2020:||
Dorsa is giving talks at three RSS 2020 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