Improving through Interaction: Searching Behavioral Representation Spaces with CMA-ES-IG
Robots that interact with humans must adapt to individual users' preferences to operate effectively in human-centered environments. An intuitive and effective technique to learn non-expert users' preferences is through rankings of robot behaviors, e.g., trajectories, gestures, or voices. Existing techniques primarily focus on generating queries that optimize preference learning outcomes, such as sample efficiency or final preference estimation accuracy. However, the focus on outcome overlooks key user expectations in the process of providing these rankings, which can negatively impact users' adoption of robotic systems. This work proposes the Covariance Matrix Adaptation Evolution Strateg...
| Authors | Nathaniel Dennler, Zhonghao Shi, Yiran Tao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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