Category Archives: Robotics

Monte Carlo Tree Search (MTCS) with hybrid discrete-continuous beliefs, applied to robotics

December 15, 2023 16:59 ,

M. Barenboim, M. Shienman and V. Indelman, Monte Carlo Planning in Hybrid Belief POMDPs, IEEE Robotics and Automation Letters, vol. 8, no. 8, pp. 4410-4417, Aug. 2023 DOI: 10.1109/LRA.2023.3282773.

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online partially observable Markov decision processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not address the added computational burden due to an increasing number of hypotheses with the planning horizon, which can grow exponentially. As part of this work, we present a novel algorithm, Hybrid Belief Monte Carlo Planning (HB-MCP) that utilizes the Monte Carlo Tree Search (MCTS) algorithm to solve a POMDP while maintaining a hybrid belief. We illustrate how the upper confidence bound (UCB) exploration bonus can be leveraged to guide the growth of hypotheses trees alongside the belief trees. We then evaluate our approach in highly aliased simulated environments where unresolved data association leads to multi-modal belief hypotheses.

Posted in: Robot task planning , Tagged:

A survey of guided RL for improving its application on robotics

December 15, 2023 16:57 ,

J. E�er, N. Bach, C. Jestel, O. Urbann and S. Kerner, Guided Reinforcement Learning: A Review and Evaluation for Efficient and Effective Real-World Robotics [Survey], IEEE Robotics & Automation Magazine, vol. 30, no. 2, pp. 67-85, June 2023 DOI: 10.1109/MRA.2022.3207664.

Recent successes aside, reinforcement learning (RL) still faces significant challenges in its application to the real-world robotics domain. Guiding the learning process with additional knowledge offers a potential solution, thus leveraging the strengths of data- and knowledge-driven approaches. However, this field of research encompasses several disciplines and hence would benefit from a structured overview.

In this article, we propose a concept of guided RL that provides a systematic approach toward accelerating the training process and improving performance for real-world robotics settings. We introduce a taxonomy that structures guided RL approaches and shows how different sources of knowledge can be integrated into the learning pipeline in a practical way. Based on this, we describe available approaches in this field and quantitatively evaluate their specific impact in terms of efficiency, effectiveness, and sim-to-real transfer within the robotics domain.

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

Comprehensive survey of the history and state of the art of active SLAM

December 15, 2023 16:43 ,

J. A. Placed et al., A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers, IEEE Transactions on Robotics, vol. 39, no. 3, pp. 1686-1705 DOI: 10.1109/TRO.2023.3248510.

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

Posted in: Mobile robot SLAM , Tagged: , ,

Review of High Definition (HD) maps

December 15, 2023 16:06 ,

Zhibin Bao, Sabir Hossain, Haoxiang Lang, Xianke Lin, A review of high-definition map creation methods for autonomous driving, Engineering Applications of Artificial Intelligence, Volume 122, 2023 DOI: 10.1016/j.engappai.2023.106125.

Autonomous driving has been among the most popular and challenging topics in the past few years. Among all modules in autonomous driving, High-definition (HD) map has drawn lots of attention in recent years due to its high precision and informative level in localization. Since localization is a significant module for automated vehicles to navigate an unknown environment, it has immediately become one of the most critical components of autonomous driving. Big organizations like HERE, NVIDIA, and TomTom have created HD maps for different scenes and purposes for autonomous driving. However, such HD maps are not open-source and are only available for internal research or automotive companies. Even though researchers have proposed various methods to create HD maps using different types of sensor data, there are few papers that review and summarize those methods. New researchers do not have a clear insight into the current state of HD map creation methods to work on their HD map research. Due to the reason above, reviewing, classifying, comparing, and summarizing the state-of-the-art techniques for HD map creation is necessary. This paper reviews recent HD map creation methods that leverage both 2D and 3D map generation. This review introduces the concept of HD maps and their usefulness in autonomous driving and gives a detailed overview of HD map creation methods. We will also discuss the limitations of the current HD map creation methods to motivate future research. Additionally, a chronological overview is created with the most recent HD map creation methods in this paper.

Posted in: Mobile robot mapping , Tagged:

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: ,