Year: 2019
Type: preprint
Abstract: A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.
Source: arXiv (Cornell University)
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: RI: Small: High Confidence, Efficient Learning Under Rich Task Specifications, G3881771821, RI: Small: Collaborative Research: Hidden Parameter Markov Decision Processes: Exploiting Structure in Families of Tasks, G5537051941, CAREER: Safe and Efficient Robot Learning from Demonstration in the Real World, NRI: Collaborative Research: Scalable Robot Autonomy through Remote Operator Assistance and Lifelong Learning, S&AS: INT: Socially-Aware Autonomy for Long-Term Deployment of Always-On Heterogeneous Robot Teams
OpenAlex ID: W3127352841