Verification and validation of learning embodied agents

Develop verification and validation methodologies tailored to embodied agents that learn and adapt to novel experiences, providing meaningful safety assurances despite non-stationary, partially observable environments.

Background

Traditional verification and validation rely on fixed specifications and disturbance models that are difficult to obtain for adaptive, embodied agents operating in changing environments. Moreover, learning systems typically offer statistical guarantees that may not suffice for instantaneous safety claims.

The authors argue that new principles and approaches are needed to verify and validate learning embodied agents.

References

However, how to validate and verify the performance of an embodied agent that learns and adapts to novel experiences is an open question and it is likely that new principles for evaluating our embodied agents will be required.

— From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence  (2110.15245 - Roy et al., 2021) in Section 6.1 (Assessing Robot Learning: Verification and Validation)