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cs.RO2024
Adaptive Querying for Reward Learning from Human Feedback
Yashwanthi Anand, Nnamdi Nwagwu, Kevin Sabbe +2
Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (intera…
cs.MA2024
Mitigating Side Effects in Multi-Agent Systems Using Blame Assignment
Pulkit Rustagi, Sandhya Saisubramanian
When independently trained or designed robots are deployed in a shared environment, their combined actions can lead to unintended negative side effects (NSEs). To ensure safe and e…