Category Archives: Developmental Robotics

Modifications of Q-learning for better learning of robot navigation

October 13, 2023 09:30 ,

Ee Soong Low, Pauline Ong, Cheng Yee Low, Rosli Omar, Modified Q-learning with distance metric and virtual target on path planning of mobile robot, Expert Systems with Applications, Volume 199, 2022, DOI: 10.1016/j.eswa.2022.117191.

Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning \u2013 a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experimental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used.

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A nice summary of RL applied to robot navigation

October 13, 2023 09:20 ,

N. Khlif, N. Khraief and S. Belghith, Reinforcement Learning for Mobile Robot Navigation: An overview IEEE Information Technologies & Smart Industrial Systems (ITSIS), Paris, France, 2022, pp. 1-7 DOI: 10.1109/ITSIS56166.2022.10118362.

For several years, research shows that interest in autonomous mobile robots is increasing and it has more and more grown. Autonomous mobile robots is an object of discussion but nowadays it’s an emerging topic due to the all progress related to field like autonomous driving and UAV (drones). Integrating intelligence into robotic systems requires solving various research problems, including one of the most important problems of mobile robotic systems: navigation. Find the answers to the following three questions: What is the localisation of the robot? Where are the robot going? How can it get there? presenting the solution of mobile robot navigation problem. These questions are answered by basic navigation parts which are localization, mapping and path planning. The paper present an overview of research on autonomous mobile robot navigation. First, a quick introduction to the various features of navigation. We also discuss machine learning and reinforcement learning in mobile robotics. Furthermore, we will discuss some path planning techniques. Some future directions are also suggested.

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Mixing rule-based and reinforcement learning navigation for robots

October 13, 2023 09:18 ,

Y. Zhu, Z. Wang, C. Chen and D. Dong, Rule-Based Reinforcement Learning for Efficient Robot Navigation With Space Reduction, IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 846-857, April 2022 DOI: 10.1109/TMECH.2021.3072675.

For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously. Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map. These approaches often involve a variety of assumptions and prior knowledge. In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in real-world scenarios due to inefficient exploration. In this article, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rule-based RL (RuRL) algorithm for reducing the sample complexity and cost of time. First, we use the rule of wall-following to generate a closed-loop trajectory. Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space. Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space. Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage. Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.

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Incremental learning (i.e., non-stationary environments, online -live- learning, task adaptation, life-long learning,…) for robots with Q-learning

October 6, 2023 08:53 ,

Y. Hu, D. Li, Y. He and J. Han, Incremental Learning Framework for Autonomous Robots Based on Q-Learning and the Adaptive Kernel Linear Model IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 64-74, March 2022 DOI: 10.1109/TCDS.2019.2962228.

The performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q -learning and the adaptive kernel linear (AKL) model. The AKL model is used for storing behavioral policies that are learned by Q -learning. Both the structure and parameters of the AKL model can be trained using a novel L2-norm kernel recursive least squares (L2-KRLS) algorithm. The AKL model initially without nodes and gradually accumulates content. The proposed framework allows to learn new behaviors without forgetting the previous ones. A novel local \u03b5 -greedy policy is proposed to speed the convergence rate of Q -learning. It calculates the exploration probability of each state for generating and selecting more important training samples. The performance of our incremental learning framework was validated in two experiments. A curve-fitting example shows that the L2-KRLS-based AKL model is suitable for incremental learning. The second experiment is based on robot learning tasks. The results show that our framework can incrementally learn behaviors in varying environments. Local \u03b5 -greedy policy-based Q -learning is faster than the existing Q -learning algorithms.

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Adaptation of model-free RL to variations in the task under continuous state and action spaces applied to robot grasping

October 6, 2023 08:38 ,

Shahid, A.A., Piga, D., Braghin, F. et al. Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning, Auton Robot 46, 483\u2013498 (2022) DOI: 10.1007/s10514-022-10034-z.

This paper presents a learning-based method that uses simulation data to learn an object manipulation task using two model-free reinforcement learning (RL) algorithms. The learning performance is compared across on-policy and off-policy algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). In order to accelerate the learning process, the fine-tuning procedure is proposed that demonstrates the continuous adaptation of on-policy RL to new environments, allowing the learned policy to adapt and execute the (partially) modified task. A dense reward function is designed for the task to enable an efficient learning of the agent. A grasping task involving a Franka Emika Panda manipulator is considered as the reference task to be learned. The learned control policy is demonstrated to be generalizable across multiple object geometries and initial robot/parts configurations. The approach is finally tested on a real Franka Emika Panda robot, showing the possibility to transfer the learned behavior from simulation. Experimental results show 100% of successful grasping tasks, making the proposed approach applicable to real applications.

Posted in: Applications of reinforcement learning to robots, Industrial robots , Tagged: ,

Using physical human-robot interaction to deduce the goals of the human during learning

September 29, 2023 15:31 ,

Losey DP, Bajcsy A, O’Malley MK, Dragan AD, Physical interaction as communication: Learning robot objectives online from human corrections, The International Journal of Robotics Research. 2022;41(1):20-44 DOI: 10.1177/02783649211050958.

When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human\u2013robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.

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A survey of morphological development in developmental robotics

September 25, 2023 16:51 ,

M. Naya-Varela, A. Faíña and R. J. Duro, Morphological Development in Robotic Learning: A Survey, IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 4, pp. 750-768 DOI: 10.1109/TCDS.2021.3052548.

Humans and animals undergo morphological development (MD) processes from infancy to adulthood that have been shown to facilitate learning. However, most of the work on developmental robotics (DRs) considers fixed morphologies, addressing only the development of the cognitive system of the robots. This article aims to provide a survey of the work that is being carried out within the relatively new field of MD in robots. In particular, it contemplates MD as the changes that occur in the properties of the joints, links and sensors of a robot during its lifetime and focuses on the work carried out by different authors to try to determine their influence on robot learning. To this end, walking, reaching, grasping and vocalization have been identified as the four most representative tasks addressed in the field, clustering the work of the different authors around them. The approach followed is multidisciplinary, discussing the relationships among DRs, embodied artificial intelligence and developmental psychology in humans in general, as well as for each of the tasks, and providing an overview of the many avenues of research that are still open in this field.

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Learning rewards from diverse human sources

July 21, 2023 09:46 ,

Bıyık E, Losey DP, Palan M, Landolfi NC, Shevchuk G, Sadigh D., Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences, . The International Journal of Robotics Research. 2022;41(1):45-67 DOI: 10.1177/02783649211041652.

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework..

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Including a safety procedure in RL to avoid physical agent problems while learning

July 11, 2023 09:09 ,

Kim Peter Wabersich, Melanie N. Zeilinger, A predictive safety filter for learning-based control of constrained nonlinear dynamical systems, . Automatica, Volume 129, 2021 DOI: 10.1016/j.automatica.2021.109597.

The transfer of reinforcement learning (RL) techniques into real-world applications is challenged by safety requirements in the presence of physical limitations. Most RL methods, in particular the most popular algorithms, do not support explicit consideration of state and input constraints. In this paper, we address this problem for nonlinear systems with continuous state and input spaces by introducing a predictive safety filter, which is able to turn a constrained dynamical system into an unconstrained safe system and to which any RL algorithm can be applied ‘out-of-the-box’. The predictive safety filter receives the proposed control input and decides, based on the current system state, if it can be safely applied to the real system, or if it has to be modified otherwise. Safety is thereby established by a continuously updated safety policy, which is based on a model predictive control formulation using a data-driven system model and considering state and input dependent uncertainties.

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A hierarchical robot control architecture that supports learning of skills at different levels through “curriculum learning” and an interesting approach to mix behaviours

July 7, 2023 11:37 ,

Suro, F., Ferber, J., Stratulat, T. et al., A hierarchical representation of behaviour supporting open ended development and progressive learning for artificial agents, . Auton Robot 45, 245–264 (2021) DOI: 10.1007/s10514-020-09960-7.

One of the challenging aspects of open ended or lifelong agent development is that the final behaviour for which an agent is trained at a given moment can be an element for the future creation of one, or even several, behaviours of greater complexity, whose purpose cannot be anticipated. In this paper, we present modular influence network design (MIND), an artificial agent control architecture suited to open ended and cumulative learning. The MIND architecture encapsulates sub behaviours into modules and combines them into a hierarchy reflecting the modular and hierarchical nature of complex tasks. Compared to similar research, the main original aspect of MIND is the multi layered hierarchy using a generic control signal, the influence, to obtain an efficient global behaviour. This article shows the ability of MIND to learn a curriculum of independent didactic tasks of increasing complexity covering different aspects of a desired behaviour. In so doing we demonstrate the contributions of MIND to open-ended development: encapsulation into modules allows for the preservation and re-usability of all the skills acquired during the curriculum and their focused retraining, the modular structure serves the evolving topology by easing the coordination of new sensors, actuators and heterogeneous learning structures.

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