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Events for June 22, 2022
Wed, Jun 22, 2022 @ 12:00 PM - 02:00 PM
Candidate: Aniruddh G. Puranic
Thesis title: Learning from Demonstrations with Temporal Logics
Committee: Jyotirmoy V. Deshmukh, Stefanos Nikolaidis, Gaurav Sukhatme, Mukund Raghothaman, Somil Bansal, Julie Shah (MIT)
Date: June 22, 2022 (Wednesday)
Time: 12pm - 2pm Pacific Time
Location: SAL 213
Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and raises concerns of safety and interpretability in the learned control policies. To address these issues, we propose to use Signal Temporal Logic (STL) to express high-level robotic tasks and use its quantitative semantics to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards and are also capable of defining interesting causal dependencies between tasks such as sequential task specifications. We present our completed work which proposed the LfD-STL framework that learns from even suboptimal/imperfect demonstrations and STL specifications to infer rewards on which reinforcement learning can be performed to obtain control policies. Through numerous experiments, we have shown that our approach outperforms prior LfD methods.
We then propose further extensions to this framework to develop metrics that provide intuitive explanations about demonstrators' behaviors, which combined with the interpretability of the learned robot policies, can help in building a safe and trusted robotic system for human interaction. As our long-term goals, we plan to use this metric as an optimization function to be used to potentially learn policies that perform better than the (imperfect) demonstrators.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon