Minna Lanz

Atakan Dag, Alexandre Angleraud, Wenyan Yang et al.

Simulation to real (Sim-to-Real) is an attractive approach to construct controllers for robotic tasks that are easier to simulate than to analytically solve. Working Sim-to-Real solutions have been demonstrated for tasks with a clear single objective such as "reach the target". Real world applications, however, often consist of multiple simultaneous objectives such as "reach the target" but "avoid obstacles". A straightforward solution in the context of reinforcement learning (RL) is to combine multiple objectives into a multi-term reward function and train a single monolithic controller. Recently, a hybrid solution based on pre-trained single objective controllers and a switching rule between them was proposed. In this work, we compare these two approaches in the multi-objective setting of a robot manipulator to reach a target while avoiding an obstacle. Our findings show that the training of a hybrid controller is easier and obtains a better success-failure trade-off than a monolithic controller. The controllers trained in simulator were verified by a real set-up.

Antti Hietanen, Jyrki Latokartano, Roel Pieters et al.

Industrial standards define safety requirements for Human-Robot Collaboration (HRC) in industrial manufacturing. The standards particularly require real-time monitoring and securing of the minimum protective distance between a robot and an operator. In this work, we propose a depth-sensor based model for workspace monitoring and an interactive Augmented Reality (AR) User Interface (UI) for safe HRC. The AR UI is implemented on two different hardware: a projector-mirror setup anda wearable AR gear (HoloLens). We experiment the workspace model and UIs for a realistic diesel motor assembly task. The AR-based interactive UIs provide 21-24% and 57-64% reduction in the task completion and robot idle time, respectively, as compared to a baseline without interaction and workspace sharing. However, subjective evaluations reveal that HoloLens based AR is not yet suitable for industrial manufacturing while the projector-mirror setup shows clear improvements in safety and work ergonomics.

Antti Hietanen, Jyrki Latokartano, Alessandro Foi et al.

Vision based object grasping and manipulation in robotics require accurate estimation of object's 6D pose. The 6D pose estimation has received significant attention in computer vision community and multiple datasets and evaluation metrics have been proposed. However, the existing metrics measure how well two geometrical surfaces are aligned - ground truth vs. estimated pose - which does not directly measure how well a robot can perform the task with the given estimate. In this work we propose a probabilistic metric that directly measures success in robotic tasks. The evaluation metric is based on non-parametric probability density that is estimated from samples of a real physical setup. During the pose evaluation stage the physical setup is not needed. The evaluation metric is validated in controlled experiments and a new pose estimation dataset of industrial parts is introduced. The experimental results with the parts confirm that the proposed evaluation metric better reflects the true performance in robotics than the existing metrics.