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. Specifically, we leverage halfspaces derived from policy optimality conditions to incorporate failed demonstrations under Bayesian Inverse Reinforcement Learning (BIRL) framework. Under the continuous control setting, the reward function and policy are learned in an alternative manner, both of which are estimated by function approximators to guarantee the learning ability. Our approach is formulated as a modelfree Inverse Reinforcement Learning (IRL) method that naturally accommodates more complex environments with continuous state and action spaces. In experiments, we demonstrate the proposed method in a virtual grasping task, achieving a significant performance boost compared to existing methods.

PixelAttentive Policy Gradient for MultiFingered Grasping in Cluttered Scenes Recent advances in onpolicy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with highdimensional and continuous observation and action spaces. However, leveraging this family of algorithms in multifingered robotic grasping remains a challenge due to large simtoreal fidelity gaps and the high sample complexity of onpolicy RL algorithms. This work aims to bridge these gaps by first reinforcementlearning a multifingered robotic grasping policy in simulation that operates in the pixel space of the input: a single depth image. Using a mapping from pixel space to Cartesian space according to the depth map, this method transfers to the real world with high fidelity and introduces a novel attention mechanism that substantially improves grasp success rate in cluttered environments. Finally, the directgenerative nature of this method allows learning of multifingered grasps that have flexible endeffector positions, orientations and rotations, as well as all degrees of freedom of the hand.