IROS

[IROS19] Learning Virtual Grasp with Failed Demonstrations via Bayesian Inverse Reinforcement Learning

We propose Bayesian Inverse Reinforcement Learning with Failure (BIRLF), which makes use of failed demonstrations that were often ignored or filtered in previous methods due to the difficulties to incorporate them in addition to the successful ones. …

[IROS17] A Glove-based System for Studying Hand-Object Manipulation via Joint Pose and Force Sensing

We present a design of an easy-to-replicate glove-based system that can reliably perform simultaneous hand pose and force sensing in real time, for the purpose of collecting human hand data during fine manipulative actions. The design consists of a …

[IROS17] Feeling the Force: Integrating Force and Pose for Fluent Discovery through Imitation Learning to Open Medicine Bottles

Learning complex robot manipulation policies for real-world objects is challenging, often requiring significant tuning within controlled environments. In this paper, we learn a manipulation model to execute tasks with multiple stages and variable …