65 research outputs found
Social robot navigation tasks: combining machine learning techniques and social force model
© 2021 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.This research was supported by the grant MDM-2016-0656 funded by MCIN/AEI / 10.13039/501100011033, the grant ROCOTRANSP PID2019-106702RB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and the grant CANOPIES H2020-ICT-2020-2-101016906 funded by the European Union.Peer ReviewedPostprint (published version
Automated Reinforcement Learning:An Overview
Reinforcement Learning and recently Deep Reinforcement Learning are popular
methods for solving sequential decision making problems modeled as Markov
Decision Processes. RL modeling of a problem and selecting algorithms and
hyper-parameters require careful considerations as different configurations may
entail completely different performances. These considerations are mainly the
task of RL experts; however, RL is progressively becoming popular in other
fields where the researchers and system designers are not RL experts. Besides,
many modeling decisions, such as defining state and action space, size of
batches and frequency of batch updating, and number of timesteps are typically
made manually. For these reasons, automating different components of RL
framework is of great importance and it has attracted much attention in recent
years. Automated RL provides a framework in which different components of RL
including MDP modeling, algorithm selection and hyper-parameter optimization
are modeled and defined automatically. In this article, we explore the
literature and present recent work that can be used in automated RL. Moreover,
we discuss the challenges, open questions and research directions in AutoRL
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning
For robotic vehicles to navigate robustly and safely in unseen environments,
it is crucial to decide the most suitable navigation policy. However, most
existing deep reinforcement learning based navigation policies are trained with
a hand-engineered curriculum and reward function which are difficult to be
deployed in a wide range of real-world scenarios. In this paper, we propose a
framework to learn a family of low-level navigation policies and a high-level
policy for deploying them. The main idea is that, instead of learning a single
navigation policy with a fixed reward function, we simultaneously learn a
family of policies that exhibit different behaviors with a wide range of reward
functions. We then train the high-level policy which adaptively deploys the
most suitable navigation skill. We evaluate our approach in simulation and the
real world and demonstrate that our method can learn diverse navigation skills
and adaptively deploy them. We also illustrate that our proposed hierarchical
learning framework presents explainability by providing semantics for the
behavior of an autonomous agent.Comment: ICRA 2023. First two authors contributed equally. Code at
https://github.com/leekwoon/hrl-na
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
Deep reinforcement learning (RL) has brought many successes for autonomous
robot navigation. However, there still exists important limitations that
prevent real-world use of RL-based navigation systems. For example, most
learning approaches lack safety guarantees; and learned navigation systems may
not generalize well to unseen environments. Despite a variety of recent
learning techniques to tackle these challenges in general, a lack of an
open-source benchmark and reproducible learning methods specifically for
autonomous navigation makes it difficult for roboticists to choose what
learning methods to use for their mobile robots and for learning researchers to
identify current shortcomings of general learning methods for autonomous
navigation. In this paper, we identify four major desiderata of applying deep
RL approaches for autonomous navigation: (D1) reasoning under uncertainty, (D2)
safety, (D3) learning from limited trial-and-error data, and (D4)
generalization to diverse and novel environments. Then, we explore four major
classes of learning techniques with the purpose of achieving one or more of the
four desiderata: memory-based neural network architectures (D1), safe RL (D2),
model-based RL (D2, D3), and domain randomization (D4). By deploying these
learning techniques in a new open-source large-scale navigation benchmark and
real-world environments, we perform a comprehensive study aimed at establishing
to what extent can these techniques achieve these desiderata for RL-based
navigation systems
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