Category Archives: Robotics

Improving safety in deep RL in the case of autonomous driving

December 15, 2023 15:35 ,

Eduardo Candela, Olivier Doustaly, Leandro Parada, Felix Feng, Yiannis Demiris, Panagiotis Angeloudis, Risk-aware controller for autonomous vehicles using model-based collision prediction and reinforcement learning, Artificial Intelligence, Volume 320, 2023 DOI: 10.1016/j.artint.2023.103923.

Autonomous Vehicles (AVs) have the potential to save millions of lives and increase the efficiency of transportation services. However, the successful deployment of AVs requires tackling multiple challenges related to modeling and certifying safety. State-of-the-art decision-making methods usually rely on end-to-end learning or imitation learning approaches, which still pose significant safety risks. Hence the necessity of risk-aware AVs that can better predict and handle dangerous situations. Furthermore, current approaches tend to lack explainability due to their reliance on end-to-end Deep Learning, where significant causal relationships are not guaranteed to be learned from data. This paper introduces a novel risk-aware framework for training AV agents using a bespoke collision prediction model and Reinforcement Learning (RL). The collision prediction model is based on Gaussian Processes and vehicle dynamics, and is used to generate the RL state vector. Using an explicit risk model increases the post-hoc explainability of the AV agent, which is vital for reaching and certifying the high safety levels required for AVs and other safety-sensitive applications. Experimental results obtained with a simulator and state-of-the-art RL algorithms show that the risk-aware RL framework decreases average collision rates by 15%, makes AVs more robust to sudden harsh braking situations, and achieves better performance in both safety and speed when compared to a standard rule-based method (the Intelligent Driver Model). Moreover, the proposed collision prediction model outperforms other models in the literature.

See also: https://doi.org/10.1016/j.artint.2023.103922
And also: https://doi.org/10.1177/02783649231169492

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

Embedding actual knowledge into Deep Learning to improve its reliability

December 5, 2023 10:33 ,

Lutter M, Peters J., Combining physics and deep learning to learn continuous-time dynamics models, The International Journal of Robotics Research. 2023;42(3):83-107 DOI: 10.1177/02783649231169492.

Deep learning has been widely used within learning algorithms for robotics. One disadvantage of deep networks is that these networks are black-box representations. Therefore, the learned approximations ignore the existing knowledge of physics or robotics. Especially for learning dynamics models, these black-box models are not desirable as the underlying principles are well understood and the standard deep networks can learn dynamics that violate these principles. To learn dynamics models with deep networks that guarantee physically plausible dynamics, we introduce physics-inspired deep networks that combine first principles from physics with deep learning. We incorporate Lagrangian mechanics within the model learning such that all approximated models adhere to the laws of physics and conserve energy. Deep Lagrangian Networks (DeLaN) parametrize the system energy using two networks. The parameters are obtained by minimizing the squared residual of the Euler\u2013Lagrange differential equation. Therefore, the resulting model does not require specific knowledge of the individual system, is interpretable, and can be used as a forward, inverse, and energy model. Previously these properties were only obtained when using system identification techniques that require knowledge of the kinematic structure. We apply DeLaN to learning dynamics models and apply these models to control simulated and physical rigid body systems. The results show that the proposed approach obtains dynamics models that can be applied to physical systems for real-time control. Compared to standard deep networks, the physics-inspired models learn better models and capture the underlying structure of the dynamics.

Posted in: Robot models , Tagged: , ,

Using proprioceptive, internal perceptions, in robots, with RL

December 5, 2023 10:30 ,

Agnese Augello, Salvatore Gaglio, Ignazio Infantino, Umberto Maniscalco, Giovanni Pilato, Filippo Vella, Roboception and adaptation in a cognitive robot, Robotics and Autonomous Systems, Volume 164, 2023 DOI: 10.1016/j.robot.2023.104400.

In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robot\u2019s body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show the integration of information from the somatosensory system and the choices that lead to the accomplishment of the task.

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

Active Inference and Behaviour Trees as alternatives to POMDPs and the like in the perception and action of robots

December 5, 2023 10:07 ,

C. Pezzato, C. H. Corbato, S. Bonhof and M. Wisse, Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics, IEEE Transactions on Robotics, vol. 39, no. 2, pp. 1050-1069, April 2023 DOI: 10.1109/TRO.2022.3226144.

In this article, we propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows handling partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree. In this work, we specify the nominal behavior offline, through BTs. However, in contrast to previous approaches, we introduce a new type of leaf node to specify the desired state to be achieved rather than an action to execute. The decision of which action to execute to reach the desired state is performed online through active inference. This results in continual online planning and hierarchical deliberation. By doing so, an agent can follow a predefined offline plan while still keeping the ability to locally adapt and take autonomous decisions at runtime, respecting safety constraints. We provide proof of convergence and robustness analysis, and we validate our method in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment. The results showed improved runtime adaptability with a fraction of the hand-coded nodes compared to classical BTs.

Posted in: Robotic architectures , Tagged: , ,

Real-time approach to POMDPs for robot navigation

December 5, 2023 10:00 ,

P. Cai and D. Hsu, Closing the Planning\u2013Learning Loop With Application to Autonomous Driving, IEEE Transactions on Robotics, vol. 39, no. 2, pp. 998-1011, April 2023 DOI: 10.1109/TRO.2022.3210767.

Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregulated urban traffic of cars, motorcycles, buses, etc. The robot vehicle has to plan in both short and long terms, in order to interact with many traffic participants of uncertain intentions and drive effectively. Planning explicitly over a long time horizon, however, incurs prohibitive computational cost and is impractical under real-time constraints. To achieve real-time performance for large-scale planning, this work introduces a new algorithm Learning from Tree Search for Driving (LeTS-Drive), which integrates planning and learning in a closed loop, and applies it to autonomous driving in crowded urban traffic in simulation. Specifically, LeTS-Drive learns a policy and its value function from data provided by an online planner, which searches a sparsely sampled belief tree; the online planner in turn uses the learned policy and value functions as heuristics to scale up its run-time performance for real-time robot control. These two steps are repeated to form a closed loop so that the planner and the learner inform each other and improve in synchrony. The algorithm learns on its own in a self-supervised manner, without human effort on explicit data labeling. Experimental results demonstrate that LeTS-Drive outperforms either planning or learning alone, as well as open-loop integration of planning and learning.

Posted in: Robot motion planning , Tagged: ,

Q-learning with a variation of e-greedy to learn the optimal management of energy in autonomous vehicles navigation

December 5, 2023 09:55 ,

Mojgan Fayyazi, Monireh Abdoos, Duong Phan, Mohsen Golafrouz, Mahdi Jalili, Reza N. Jazar, Reza Langari, Hamid Khayyam, Real-time self-adaptive Q-learning controller for energy management of conventional autonomous vehicles, Expert Systems with Applications, Volume 222, 2023 DOI: 10.1016/j.eswa.2023.119770.

Reducing emissions and energy consumption of autonomous vehicles is critical in the modern era. This paper presents an intelligent energy management system based on Reinforcement Learning (RL) for conventional autonomous vehicles. Furthermore, in order to improve the efficiency, a new exploration strategy is proposed to replace the traditional decayed \u03b5-greedy strategy in the Q-learning algorithm associated with RL. Unlike traditional Q-learning algorithms, the proposed self-adaptive Q-learning (SAQ-learning) can be applied in real-time. The learning capability of the controllers can help the vehicle deal with unknown situations in real-time. Numerical simulations show that compared to other controllers, Q-learning and SAQ-learning controllers can generate the desired engine torque based on the vehicle road power demand and control the air/fuel ratio by changing the throttle angle efficiently in real-time. Also, the proposed real-time SAQ-learning is shown to improve the operational time by 23% compared to standard Q-learning. Our simulations reveal the effectiveness of the proposed control system compared to other methods, namely dynamic programming and fuzzy logic methods.

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

There are people working on robotic software engineering these days :-O ! (real-time included)

December 5, 2023 09:48 ,

Arturo Laurenzi, Davide Antonucci, Nikos G. Tsagarakis, Luca Muratore, The XBot2 real-time middleware for robotics, Robotics and Autonomous Systems, Volume 163, 2023 DOI: 10.1016/j.robot.2023.104379.

This paper introduces XBot2, a novel real-time middleware for robotic applications with a strong focus on modularity and reusability of components, and seamless support for multi-threaded, mixed real-time (RT) and non-RT architectures. Compared to previous works, XBot2 focuses on providing a dynamic, ready-to-use hardware abstraction layer that allows users to make run-time queries about the robot topology, and act consequently, by leveraging an easy-to-use API that is fully RT-compatible. We provide an extensive description about implementation challenges and design decisions, and finally validate our architecture with multiple use-cases. These range from the integration of three popular simulation tools (i.e. Gazebo, PyBullet, and MuJoCo), to real-world tests involving complex, hybrid robotic platforms such as IIT\u2019s CENTAURO and MoCA robots.

Posted in: Robotic software , Tagged: ,

Survey on POMDPs for robotics

November 24, 2023 17:11 ,

M. Lauri, D. Hsu and J. Pajarinen, Partially Observable Markov Decision Processes in Robotics: A Survey, IEEE Transactions on Robotics, vol. 39, no. 1, pp. 21-40, Feb. 2023 DOI: 10.1109/TRO.2022.3200138.

Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multirobot systems, manipulation, and human\u2013robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research.

Posted in: Robotics , Tagged: ,

Review of RL applied to robotic manipulation

November 24, 2023 16:55 ,

��igo Elguea-Aguinaco, Antonio Serrano-Mu�oz, Dimitrios Chrysostomou, Ibai Inziarte-Hidalgo, Simon B�gh, Nestor Arana-Arexolaleiba, A review on reinforcement learning for contact-rich robotic manipulation tasks, Robotics and Computer-Integrated Manufacturing, Volume 81, 2023 DOI: 10.1016/j.rcim.2022.102517.

Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors has led reinforcement learning agents to be increasingly applied in real-life scenarios. However, there is still a long way ahead for reinforcement learning to become a core element in industrial applications. This paper examines the landscape of reinforcement learning and reviews advances in its application in contact-rich tasks from 2017 to the present. The analysis investigates the main research for the most commonly selected tasks for testing reinforcement learning algorithms in both rigid and deformable object manipulation. Additionally, the trends around reinforcement learning associated with serial manipulators are explored as well as the various technological challenges that this machine learning control technique currently presents. Lastly, based on the state-of-the-art and the commonalities among the studies, a framework relating the main concepts of reinforcement learning in contact-rich manipulation tasks is proposed. The final goal of this review is to support the robotics community in future development of systems commanded by reinforcement learning, discuss the main challenges of this technology and suggest future research directions in the domain.

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

Mapping unseen rooms by deducing them from known environment structure

November 24, 2023 16:11 ,

Matteo Luperto, Federico Amadelli, Moreno Di Berardino, Francesco Amigoni, Mapping beyond what you can see: Predicting the layout of rooms behind closed doors, Robotics and Autonomous Systems, Volume 159, 2023 DOI: 10.1016/j.robot.2022.104282.

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.

Posted in: Mobile robot mapping