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

04/02/2019
by   Èric Pairet, et al.
0

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.

READ FULL TEXT

page 1

page 4

page 6

page 7

research
05/25/2019

Learning and Composing Primitive Skills for Dual-arm Manipulation

In an attempt to confer robots with complex manipulation capabilities, d...
research
10/15/2021

Dual-Arm Adversarial Robot Learning

Robot learning is a very promising topic for the future of automation an...
research
10/08/2021

Learning to Centralize Dual-Arm Assembly

Even though industrial manipulators are widely used in modern manufactur...
research
09/13/2022

Bimanual crop manipulation for human-inspired robotic harvesting

Most existing robotic harvesters utilize a unimanual approach; a single ...
research
03/15/2022

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

Most successes in robotic manipulation have been restricted to single-ar...
research
01/23/2019

A Constraint Programming Approach to Simultaneous Task Allocation and Motion Scheduling for Industrial Dual-Arm Manipulation Tasks

Modern lightweight dual-arm robots bring the physical capabilities to qu...
research
07/02/2018

A Dataset of Daily Interactive Manipulation

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

Please sign up or login with your details

Forgot password? Click here to reset