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.
|May 7, 2020:||Dorsa is giving talks at Johns Hopkins University Applied Physics Laboratory on May 7, and at the NASA Formal Methods Conference (NFM) AI Safety Workshop on May 11.|
|May 6, 2020:||
Our 4 papers got accepted at RSS 2020:
- "Active Preference-Based Gaussian Process Regression for Reward Learning" by Biyik et al.
- "Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving" by Cao et al.
- "Shared Autonomy with Learned Latent Actions" by Jeon et al.
- "Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints" by Choudhury et al.
|Apr 28, 2020:||Check our preprint about a reference-less approach to automatic evaluation in natural language processing on arXiv.|
|Apr 24, 2020:||Dorsa's keynote talk at HSCC 2020 is publicly available on YouTube.|
|Apr 20, 2020:||Dorsa is giving a keynote talk at 23rd ACM International Conference on Hybrid Systems: Computation and Control (HSCC) on April 23.|