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"Robot learning"
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Dynamic Movement Primitives Based Robot Skills Learning
In this article, a robot skills learning framework is developed, which considers both motion modeling and execution. In order to enable the robot to learn skills from demonstrations, a learning method called dynamic movement primitives (DMPs) is introduced to model motion. A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint manipulators. The DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion sequences. In addition, motions are categorized into different goals and durations. It is worth mentioning that an adaptive neural networks (NNs) control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution, which is beneficial to the improvement of reliability of the skills learning system. The experiment test on the Baxter robot verifies the effectiveness of the proposed method.
Journal Article
Standing Balance Control of a Bipedal Robot Based on Behavior Cloning
Bipedal robots have gained increasing attention for their human-like mobility which allows them to work in various human-scale environments. However, their inherent instability makes it difficult to control their balance while they are physically interacting with the environment. This study proposes a novel balance controller for bipedal robots based on a behavior cloning model as one of the machine learning techniques. The behavior cloning model employs two deep neural networks (DNNs) trained on human-operated balancing data, so that the trained model can predict the desired wrench required to maintain the balance of the bipedal robot. Based on the prediction of the desired wrench, the joint torques for both legs are calculated using robot dynamics. The performance of the developed balance controller was validated with a bipedal lower-body robotic system through simulation and experimental tests by providing random perturbations in the frontal plane. The developed balance controller demonstrated superior performance with respect to resistance to balance loss compared to the conventional balance control method, while generating a smoother balancing movement for the robot.
Journal Article
Robot adaptation to human physical fatigue in human–robot co-manipulation
In this paper, we propose a novel method for human–robot collaboration, where the robot physical behaviour is adapted online to the human motor fatigue. The robot starts as a follower and imitates the human. As the collaborative task is performed under the human lead, the robot gradually learns the parameters and trajectories related to the task execution. In the meantime, the robot monitors the human fatigue during the task production. When a predefined level of fatigue is indicated, the robot uses the learnt skill to take over physically demanding aspects of the task and lets the human recover some of the strength. The human remains present to perform aspects of collaborative task that the robot cannot fully take over and maintains the overall supervision. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed approach with experiments on real-world co-manipulation tasks: material sawing and surface polishing.
Journal Article
Exploring the impact of robot interaction on learning engagement: a comparative study of two multi-modal robots
In recent years, there has been a growing interest in using robots within educational environments due to their potential to augment student engagement and motivation. However, current research has not adequately addressed the effectiveness of these robots in facilitating inclusive learning for diverse student populations, particularly those with dyslexia. This study proposes an inclusive learning system developed on two multi-modal robots, Kebbi and Minibo, with interactive (i.e., movable hands) and straightforward features. The system integrates various interactive elements, such as animations, songs, dance, gestures, and touch, to enhance students’ learning engagement, interaction, and motivation and cater to their diverse needs. The study aims to examine the influence of different features from two unique multi-modal robots on the engagement levels of students with/without dyslexia and their needs when engaging with robot learning. Two research questions are posed: (1) What are the features of multi-modal robots that could effectively improve the learning engagements of students with/without dyslexia? (2) What are the needs of students with/without dyslexia when engaging with robot learning? To this end, a comparative study is conducted where 64 students participate in a five-day robot-led training program, while another 73 students receive traditional teacher-led training. Pre/post questionnaires are administered to evaluate students’ engagement levels, and semi-structured interviews are conducted to obtain additional insights. The findings reveal that students with dyslexia are better suited to the interactive and multi-modal features of Kebbi. In contrast, students without dyslexia may prefer the more straightforward features of Minibo, which can still effectively promote engagement and learning. Multi-modal robots can boost engagement and motivation in students with and without dyslexia through novelty and cognitive load management. Emotional connections and interactive elements, such as empathetic and customizable features, enhance engagement and improve learning outcomes.
Journal Article
Human robot cooperation with compliance adaptation along the motion trajectory
In this paper we propose a novel approach for intuitive and natural physical human–robot interaction in cooperative tasks. Through initial learning by demonstration, robot behavior naturally evolves into a cooperative task, where the human co-worker is allowed to modify both the spatial course of motion as well as the speed of execution at any stage. The main feature of the proposed adaptation scheme is that the robot adjusts its stiffness in path operational space, defined with a Frenet–Serret frame. Furthermore, the required dynamic capabilities of the robot are obtained by decoupling the robot dynamics in operational space, which is attached to the desired trajectory. Speed-scaled dynamic motion primitives are applied for the underlying task representation. The combination allows a human co-worker in a cooperative task to be less precise in parts of the task that require high precision, as the precision aspect is learned and provided by the robot. The user can also freely change the speed and/or the trajectory by simply applying force to the robot. The proposed scheme was experimentally validated on three illustrative tasks. The first task demonstrates novel two-stage learning by demonstration, where the spatial part of the trajectory is demonstrated independently from the velocity part. The second task shows how parts of the trajectory can be rapidly and significantly changed in one execution. The final experiment shows two Kuka LWR-4 robots in a bi-manual setting cooperating with a human while carrying an object.
Journal Article
Design and research of telescopic arm of platform truss robot for building machine
To improve the performance of the truss robot and guide the subsequent design of the telescopic arm of the truss robot under different working conditions, the working principle of the telescopic arm of the truss robot is introduced, and a mathematical model for the size optimization of the telescopic arm is established under a given working condition with the goal of minimizing the mass of the telescopic arm and ensuring that the telescopic arm has a certain stiffness. According to the optimized dimension parameters, the prototype of the telescopic arm of the platform truss robot is manufactured and run.
Journal Article
Demonstration-Guided Reinforcement Learning for Continuous Sensorimotor Learning in Dynamic Environments
This paper investigates the mechanisms of sensorimotor learning in humanoid robots, taking cues from child development and insights from developmental psychology and parent-infant embodied interactions. It emphasizes the use of demonstration-guided reinforcement learning (Demo-RL), where expert feedback helps shaping robot behavior. By incorporating computational demonstrations into the online learning loop, the approach enables robots to physically adapt and recover from unexpected sensorimotor disturbances in real time. This work aims to provide a framework that supports and advances research into the emergence of complex nonverbal social behaviors in robotic systems.
Journal Article
Knowledge- and ambiguity-aware robot learning from corrective and evaluative feedback
In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL method that improves the human–robot interaction for non-expert and imperfect teachers in two directions. First, uncertainty estimation is included to endow the agents with a lack of knowledge awareness (epistemic uncertainty) and demonstration ambiguity awareness (aleatoric uncertainty), such that the robot can request human input when it is deemed more necessary. Second, the proposed method enables the teachers to train with the flexibility of using corrective demonstrations, evaluative reinforcements, and implicit positive feedback. The experimental results show an improvement in learning convergence with respect to other learning methods when the agent learns from highly ambiguous teachers. Additionally, in a user study, it was found that the components of the proposed method improve the teaching experience and the data efficiency of the learning process.
Journal Article
Robot Learning from Failed Demonstrations
Robot Learning from Demonstration (RLfD) seeks to enable lay users to encode desired robot behaviors as autonomous controllers. Current work uses a human’s demonstration of the target task to initialize the robot’s policy, and then improves its performance either through practice (with a known reward function), or additional human interaction. In this article, we focus on the initialization step and consider what can be learned when the humans do not provide successful examples. We develop probabilistic approaches that avoid reproducing observed failures while leveraging the variance across multiple attempts to drive exploration. Our experiments indicate that failure data do contain information that can be used to discover successful means to accomplish tasks. However, in higher dimensions, additional information from the user will most likely be necessary to enable efficient failure-based learning.
Journal Article