Effects of a Social Force Model Reward in Robot Navigation Based on Deep Reinforcement Learning

Viyuela, Óscar Gil; Sanfeliu, Alberto

doi:10.1007/978-3-030-36150-1_18

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…In [34], the moving agents are simulated using the Optimal Reciprocal Collision Avoidance algorithm (ORCA) [36], and Imitation Learning is used to improve the performance. Although, as is described in [37], the performance is only high when the robot is close to the goal with few moving obstacles around.…”

Section: Deep Reinforcement Learning In Robot Navigationmentioning

confidence: 96%

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Gil

Garrell

Sanfeliu

2021

Sensors

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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.

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Section: Deep Reinforcement Learning In Robot Navigationmentioning

confidence: 96%

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Gil

Garrell

Sanfeliu

2021

Sensors

Self Cite

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“…For example, Yue et al [198] integrated SFM and a deep neural network in their Neural Social Physics model with learnable parameters. Gil and Sanfeliu [199] presented Social Force Generative Adversarial Network (SoFGAN) that uses a GAN and SFM to generate different plausible people trajectories reducing collisions in a scene.…”

Section: Human Trajectory Predictionmentioning

confidence: 99%

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Karwowski,

Szynkiewicz,

Niewiadomska-Szynkiewicz

2024

Sensors

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Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

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“…They receive a higher collaboration reward r co for not staying too close to each other. 25 Denote c 1 and c 2 as distance thresholds; and k another agent. The collaboration reward is computed as follows:…”

Section: Preliminariesmentioning

confidence: 99%

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

Chen

Liu

Wong

2021

Computer Animation & Virtual

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This paper proposes a framework that integrates reinforcement learning and blend-trees to generate animation of multiple agents for object transportation. The main idea is that in the learning stage, policies are learned to control agents to perform specific skills, including navigation, pushing, and orientation adjustment. The policies determine the blending parameters of the blend-trees to achieve locomotion control of the agents. In the simulation stage, the policies are combined to control the agents to navigate, push objects, and adjust orientation of the objects. We demonstrated several examples to show that the framework is capable of generating animation of multiple agents in different scenarios.

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Effects of a Social Force Model Reward in Robot Navigation Based on Deep Reinforcement Learning

Cited by 7 publications

References 27 publications

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation

Generation of multiagent animation for object transportation using deep reinforcement learning and blend‐trees

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