DeepAI AI Chat
Log In Sign Up

Learning and Generalisation of Primitives Skills Towards Robust Dual-arm Manipulation

by   Èric Pairet, et al.
Heriot-Watt University

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.


page 1

page 4

page 6

page 7


Learning and Composing Primitive Skills for Dual-arm Manipulation

In an attempt to confer robots with complex manipulation capabilities, d...

Dual-Arm Adversarial Robot Learning

Robot learning is a very promising topic for the future of automation an...

Learning to Centralize Dual-Arm Assembly

Even though industrial manipulators are widely used in modern manufactur...

Bimanual crop manipulation for human-inspired robotic harvesting

Most existing robotic harvesters utilize a unimanual approach; a single ...

Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning

Most successes in robotic manipulation have been restricted to single-ar...

A Dataset of Daily Interactive Manipulation

Robots that succeed in factories stumble to complete the simplest daily ...