Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy

Haimin Hu, Zixu Zhang, Kensuke Nakamura, Andrea Bajcsy, Jaime Fernández Fisac

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty and its ability to quickly respond to future scenarios as they arise, by jointly considering the physical dynamics and the robot's learning algorithm. We leverage adversarial reinforcement learning for tractable safety analysis under high-dimensional learning dynamics and demonstrate our framework's ability to work with both Bayesian belief propagation and implicit learning through large pre-trained neural trajectory predictors.

Original languageEnglish (US)
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event7th Conference on Robot Learning, CoRL 2023 - Atlanta, United States
Duration: Nov 6 2023Nov 9 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


  • Active Information Gathering
  • Adversarial Reinforcement Learning
  • Learning-Aware Safety Analysis


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