Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation
This work addresses robust robotic manipulation for real-world applications, but it appears incremental as it builds on existing human-inspired methods.
The paper tackled the problem of robust dual-arm manipulation in dynamic environments by leveraging human motion knowledge to develop a general framework, achieving successful pick-and-place tasks with unexpected obstacles on the iCub robot.
Robots are becoming a vital ingredient in society. Some of their daily tasks require dual-arm manipulation skills in the rapidly changing, dynamic and unpredictable real-world environments where they have to operate. Given the expertise of humans in conducting these activities, it is natural to study humans' motions to use the resulting knowledge in robotic control. With this in mind, this work leverages human knowledge to formulate a more general, real-time, and less task-specific framework for dual-arm manipulation. The proposed framework is evaluated on the iCub humanoid robot and several synthetic experiments, by conducting a dual-arm pick-and-place task of a parcel in the presence of unexpected obstacles. Results suggest the suitability of the method towards robust and generalisable dual-arm manipulation.