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About this paper
Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone
Researchers at The University of Texas at Austin and collaborators established a theoretical link between grounded action transformation and imitation from observation, developing an adversarial learning algorithm, GARAT, that significantly improves sim-to-real transfer performance and data efficiency by more accurately aligning source and target environment dynamics.
Key insights
- The process of learning a grounded action transformation can be framed as an Imitation from Observation problem, allowing the application of advanced IfO techniques to transfer learning.
- An adversarial minimax objective can effectively minimize the divergence between grounded source and target marginal transition distributions, leading to better dynamics alignment.
- Leveraging the IfO formulation enables GARAT to achieve significantly higher data efficiency in grounding, requiring substantially fewer target environment interactions than prior methods.
Summary by alphaXiv.
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