Abstract
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Learning reachable manifold and inverse mapping for a redundant robot manipulator
at
| Seungsu Kim, Julien Perez |
| IEEE International Conference on Robotics and Automation 2021 (ICRA), Xi’an, China, 30 May–5 June 2021 |
Validating the kinematic feasibility of a planned robot motion and finding corresponding inverse solutions are time-consuming processes, especially for long-horizon manipulation tasks. Most existing approaches are based on solving iterative gradient-based optimization, so the processes are time-consuming and have a high risk of falling in local minima. In this work, we propose a unified framework to learn a kinematic feasibility model and a one-shot inverse mapping model for a redundant robot manipulator. Once they are trained, the models can compute the kinematic reachability of a target pose and its inverse solutions without iterative process. We validate our approach using a 7-DOF robot arm with an object grasping application.
Related Content
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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