Safe reinforcement learning of dynamic high-dimensional robotic tasks : navigation, manipulation, interaction
Liu, Puze ; Zhang, Kuo ; Tateo, Davide LU
; Jauhri, Snehal
; Hu, Zhiyuan
; Peters, Jan
and Chalvatzaki, Georgia
(2023)
2023 IEEE International Conference on Robotics and Automation, ICRA 2023
In Proceedings - IEEE International Conference on Robotics and Automation
2023-May.
p.9449-9456
- Abstract
Safety is a fundamental property for the real-world deployment of robotic platforms. Any control policy should avoid dangerous actions that could harm the environment, humans, or the robot itself. In reinforcement learning (RL), safety is crucial when exploring a new environment to learn a new skill. This paper introduces a new formulation of safe exploration for robotic RL in the tangent space of the constraint manifold that effectively transforms the action space of the RL agent for always respecting safety constraints locally. We show how to apply this approach to a wide range of robotic platforms and how to define safety constraints that represent dynamic articulated objects like humans in the context of robotic RL. Our proposed... (More)
Safety is a fundamental property for the real-world deployment of robotic platforms. Any control policy should avoid dangerous actions that could harm the environment, humans, or the robot itself. In reinforcement learning (RL), safety is crucial when exploring a new environment to learn a new skill. This paper introduces a new formulation of safe exploration for robotic RL in the tangent space of the constraint manifold that effectively transforms the action space of the RL agent for always respecting safety constraints locally. We show how to apply this approach to a wide range of robotic platforms and how to define safety constraints that represent dynamic articulated objects like humans in the context of robotic RL. Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment. We show safe real-world deployment of our learned controller on a TIAGo++ robot, achieving remarkable performance in manipulation and human-robot interaction tasks.
(Less)
- author
- Liu, Puze
; Zhang, Kuo
; Tateo, Davide
LU
; Jauhri, Snehal
; Hu, Zhiyuan
; Peters, Jan
and Chalvatzaki, Georgia
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation - IEEE International Conference on Robotics and Automation
- series title
- Proceedings - IEEE International Conference on Robotics and Automation
- volume
- 2023-May
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2023 IEEE International Conference on Robotics and Automation, ICRA 2023
- conference location
- London, United Kingdom
- conference dates
- 2023-05-29 - 2023-06-02
- external identifiers
-
- scopus:85168661321
- ISSN
- 1050-4729
- ISBN
- 9798350323658
- DOI
- 10.1109/ICRA48891.2023.10161548
- language
- English
- LU publication?
- no
- id
- 688c63a0-338f-4748-8627-c0dc0ba65f3f
- date added to LUP
- 2025-10-16 14:08:42
- date last changed
- 2025-11-03 16:17:54
@inproceedings{688c63a0-338f-4748-8627-c0dc0ba65f3f,
abstract = {{<p>Safety is a fundamental property for the real-world deployment of robotic platforms. Any control policy should avoid dangerous actions that could harm the environment, humans, or the robot itself. In reinforcement learning (RL), safety is crucial when exploring a new environment to learn a new skill. This paper introduces a new formulation of safe exploration for robotic RL in the tangent space of the constraint manifold that effectively transforms the action space of the RL agent for always respecting safety constraints locally. We show how to apply this approach to a wide range of robotic platforms and how to define safety constraints that represent dynamic articulated objects like humans in the context of robotic RL. Our proposed approach achieves state-of-the-art performance in simulated high-dimensional and dynamic tasks while avoiding collisions with the environment. We show safe real-world deployment of our learned controller on a TIAGo++ robot, achieving remarkable performance in manipulation and human-robot interaction tasks.</p>}},
author = {{Liu, Puze and Zhang, Kuo and Tateo, Davide and Jauhri, Snehal and Hu, Zhiyuan and Peters, Jan and Chalvatzaki, Georgia}},
booktitle = {{Proceedings - ICRA 2023 : IEEE International Conference on Robotics and Automation}},
isbn = {{9798350323658}},
issn = {{1050-4729}},
language = {{eng}},
pages = {{9449--9456}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings - IEEE International Conference on Robotics and Automation}},
title = {{Safe reinforcement learning of dynamic high-dimensional robotic tasks : navigation, manipulation, interaction}},
url = {{http://dx.doi.org/10.1109/ICRA48891.2023.10161548}},
doi = {{10.1109/ICRA48891.2023.10161548}},
volume = {{2023-May}},
year = {{2023}},
}