5 accepted papers
Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection. In physical systems, additional challenge may be posed by domain noise, which is present in virtually all real-world applications. In this letter, w
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this work, we propose to address the problem of sim-to-real domain transfer by using meta learning to train a policy that can a…
Contextual skill models are learned to provide skills over a range of task parameters, often using regression across optimal task-specific policies. However, the sequential nature of the learning process is usually neglected. In this paper, we propose to use active incremental learning by selecting…
Reinforcement learning provides robots with an autonomous learning framework where a skill can be learned by exploration. Exploration in real world is, however, inherently unsafe and time consuming, and causes wear and tear. To address these, learning policies in simulation and then transferring the
To cope with varying conditions, motor primitives (MPs) must support generalization over task parameters to avoid learning separate primitives for each situation. In this regard, deterministic and probabilistic models have been proposed for generalizing MPs to new task parameters, thus providing lim…