Projects per year
Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning. Performing such adaptation, however, requires informative data about the environment to be available during the adaptation. In this paper, we present domain curiosity—a method of training exploratory policies that are explicitly optimized to provide data that allows a model to learn about the unknown aspects of the environment. In contrast to most curiosity methods, our approach explicitly rewards learning, which makes it robust to environment noise without sacrificing its ability to learn. We evaluate the proposed method by comparing how much a model can learn about environment dynamics given data collected by the proposed approach, compared to standard curious and random policies. The evaluation is performed using a toy environment, two simulated robot setups, and on a real-world haptic exploration task. The results show that the proposed method allows data-efficient and accurate estimation of dynamics.
|Title of host publication||Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021|
|Number of pages||8|
|Publication status||Published - 16 Dec 2021|
|MoE publication type||A4 Article in a conference publication|
|Event||IEEE/RSJ International Conference on Intelligent Robots and Systems - Prague, Czech Republic|
Duration: 27 Sep 2021 → 1 Oct 2021
|Name||Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Conference||IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Period||27/09/2021 → 01/10/2021|
- Adaptation models
- Predictive models
- Reinforcement learning
FingerprintDive into the research topics of 'Domain Curiosity: Learning Efficient Data Collection Strategies for Domain Adaptation'. Together they form a unique fingerprint.
- 1 Finished
AI spider silk threading
Kyrki, V., Arndt, K., Petrik, V. & Blanco Mulero, D.
01/01/2018 → 31/12/2022
Project: Academy of Finland: Other research funding