A Bayesian tracker for synthesizing mobile robot behaviour from demonstration

Magnenat, Stéphane; Colas, Francis

doi:10.1007/s10514-021-10019-4

Cited by 2 publications

(4 citation statements)

References 43 publications

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“…physical robots for tasks such as road following Pomerleau (1989), trajectory imitation Tan (2015) Vuković et al (2015), or for complete navigation systems Gamal et al (2020), Magnenat and Colas (2021), Caccavale and Finzi (2019) and Spencer et al (2022). As computational platforms, most authors have employed computers for offline training Vuković et al (2015) or processing in real-time Caccavale and Finzi (2019), and in some cases, the trained model has been embedded in microcontrollers for the imitation process, as seen in Tan ( 2015), Vuković et al (2015), Magnenat and Colas (2021) and Spencer et al (2022). In this work, we explore hardware acceleration using an SoC FPGA device to enable online training on a physical robot.…”

Section: Declarationsmentioning

confidence: 99%

“…Physical implementations have also been developed in software, as demonstrated in Vuković et al (2015), where it was implemented on the Kepera II robot, or in Tan (2015), where LfD was applied to the E-Puck robot. In the same way, Magnenat and Colas (2021), Caccavale and Finzi (2019), and Spencer et al (2022) were implemented on the marXbot and Artor mobile platforms using desktops and the last author, implemented the LfD on the NVIDIA Jets device. Table 1 provides a summary of previous works that have implemented LfD in mobile robots, highlighting the learning technique, the mobile robot application, the use of micro-skills, and the computational platform.…”

Section: Introductionmentioning

confidence: 99%

“…However, the robot does not implement the environment perception and only imitates motion patterns in a free space. In Magnenat and Colas (2021) a Bayesian filter was deployed on an ARM processor within the MarXbot robot for navigation applications. Nevertheless, the training phase was conducted on an external Intel Core i9-8950HK.…”

Section: Introductionmentioning

confidence: 99%

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Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots

Triana,

Arboleda,

de Oliveira

2024

Preprint

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Designing mobile robot controllers for non-expert users is a complex task that involves programming abilities and developing accurate mathematical models for representing the robot's kinematics, sensor observations, and parameter estimations. Learning from demonstration (LfD) is a technique that enables robots to autonomously learn and perform new tasks from the observations of human demonstrations. Obtaining models with low computational complexity is of utmost importance for embedded robotics systems. This paper proposes the usage of LfD for learning three simple micro-skills (move forward, turn clockwise, and turn counterclockwise) that can be combined to efficiently perform more complex skills on a mobile robot. An adaptive single-layer perceptron neural network (SLP) and a Particle Swarm Optimization (PSO) training algorithm were implemented using a low-cost System-on-chip (SoC) device, allowing the robot to learn the wheel's speed profile for each demonstrated micro-skill and compose them to navigate around obstacles in complex scenarios. Real-world scenarios were used to statistically analyze the robustness of the proposed methodology, achieving a success rate of 100% for the known scenarios and a success rate of 61.81% for the unknown scenarios. Experimental results demonstrated that the robot correctly moves on several unknown scenarios that compose the three taught micro-skills, avoiding obstacles and correcting deviations from the initial position.

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Section: Declarationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots

Triana,

Arboleda,

de Oliveira

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Artemiadis et al [33] described the dependencies among the human joint angles using a BN. Magnenat et al [34] proposed a learning-form-demonstration framework based the BN. This framework combines the demonstrated commands according to the similarity between the demonstrated sensory trajectories and the current replay trajectory.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Human-like Motion Planner for Anthropomorphic Arms Based on Diversified Arm Motion Models

Wei

2023

Electronics

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In this paper, the human-like motion issue for anthropomorphic arms is further discussed. An Intelligent Human-like Motion Planner (IHMP) consisting of Movement Primitive (MP), Bayesian Network (BN) and Coupling Neural Network (CPNN) is proposed to help the robot generate human-like arm movements. Firstly, the arm motion model is decoupled in the aspects of arm structure and motion process, respectively. In the former aspect, the arm model is decoupled into different simple models through the Movement Primitive. A Hierarchical Planning Strategy (HPS) is proposed to decouple a complete motion process into different sub-processes. Based on diversified arm motion models, the Bayesian Network is used to help the robot choose the suitable motion model among these arm motion models. Then, according to the features of diversified arm motion models, the Coupling Neural Network is proposed to obtain the inverse kinematic (IK) solutions. This network can integrate different models into a single network and reflect the features of these models by changing the network structure. Being a major contribution to this paper, specific focus is on the improvement of human-like motion accuracy and independent consciousness of robots. Finally, the availability of the IHMP is verified by experiments on a humanoid robot Pepper.

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A Bayesian tracker for synthesizing mobile robot behaviour from demonstration

Cited by 2 publications

References 43 publications

Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots

Learning from Demonstration in Embedded Hardware for the Composition of Micro-Skills in Mobile Robots

An Intelligent Human-like Motion Planner for Anthropomorphic Arms Based on Diversified Arm Motion Models

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