Category Archives: Robotics

Including safety learning in RL for improving the sim-to-lab gap

November 17, 2023 09:59 ,

Kai-Chieh Hsu, Allen Z. Ren, Duy P. Nguyen, Anirudha Majumdar, Jaime F. Fisac, Sim-to-Lab-to-Real: Safe reinforcement learning with shielding and generalization guarantees, Artificial Intelligence, Volume 314, 2023 DOI: 10.1016/j.artint.2022.103811.

Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using reinforcement learning often fail to generalize to novel environments due to unsafe behavior. In this paper, we propose Sim-to-Lab-to-Real to bridge the reality gap with a probabilistically guaranteed safety-aware policy distribution. To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis. In Sim-to-Lab transfer, we apply a supervisory control scheme to shield unsafe actions during exploration; in Lab-to-Real transfer, we leverage the Probably Approximately Correct (PAC)-Bayes framework to provide lower bounds on the expected performance and safety of policies in unseen environments. Additionally, inheriting from the HJ reachability analysis, the bound accounts for the expectation over the worst-case safety in each environment. We empirically study the proposed framework for ego-vision navigation in two types of indoor environments with varying degrees of photorealism. We also demonstrate strong generalization performance through hardware experiments in real indoor spaces with a quadrupedal robot. See https://sites.google.com/princeton.edu/sim-to-lab-to-real for supplementary material.

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

A RRT-based method that addresses combined task and motion planning

November 10, 2023 09:38 ,

Riccardo Caccavale, Alberto Finzi, A rapidly-exploring random trees approach to combined task and motion planning, Robotics and Autonomous Systems, Volume 157, 2022 DOI: 10.1016/j.robot.2022.104238.

Task and motion planning in robotics are typically addressed by separated intertwined methods. Task planners generate abstract high-level actions to be executed, while motion planners provide the associated discrete movements in the configuration space satisfying kinodynamic constraints. However, these two planning processes are strictly dependent, therefore the problem of combining task and motion planning with a uniform approach is very relevant. In this work, we tackle this issue by proposing a RRT-based method that addresses combined task and motion planning. Our approach relies on a combined metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and feasible movements in the configuration space. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in navigation and transportation tasks.

Posted in: Robot motion planning, Robot task planning , Tagged:

Using results from belief-based planning for Bayesian inference in robotics

November 10, 2023 09:35 ,

Farhi, E.I., Indelman, V., Bayesian incremental inference update by re-using calculations from belief space planning: a new paradigm, Auton Robot 46, 783\u2013816 (2022). DOI: 10.1007/s10514-022-10045-w.

Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference and decision making triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of these efforts, inference and control, as well as inference and belief space planning (BSP) are still treated as two separate processes. In this paper we propose a paradigm shift, a novel approach which deviates from conventional Bayesian inference and utilizes the similarities between inference and BSP. We make the key observation that inference can be efficiently updated using predictions made during the decision making stage, even in light of inconsistent data association between the two. We developed a two staged process that implements our novel approach and updates inference using calculations from the precursory planning phase. Using autonomous navigation in an unknown environment along with iSAM2 efficient methodologies as a test case, we benchmarked our novel approach against standard Bayesian inference, both with synthetic and real-world data (KITTI dataset). Results indicate that not only our approach improves running time by at least a factor of two while providing the same estimation accuracy, but it also alleviates the computational burden of state dimensionality and loop closures.

Posted in: Mobile Robot Localization, Mobile robot mapping , Tagged: ,

Real-time and Bayesian-enabled ICP for mobile robot localization and mapping in a Bayesian framework

November 10, 2023 09:31 ,

Maken FA, Ramos F, Ott L. , Bayesian iterative closest point for mobile robot localization, The International Journal of Robotics Research. 2022;41(9-10):851-874 DOI: 10.1177/02783649221101417.

Accurate localization of a robot in a known environment is a fundamental capability for successfully performing path planning, manipulation, and grasping tasks. Particle filters, also known as Monte Carlo localization (MCL), are a commonly used method to determine the robot\u2019s pose within its environment. For ground robots, noisy wheel odometry readings are typically used as a motion model to predict the vehicle\u2019s location. Such a motion model requires tuning of various parameters based on terrain and robot type. However, such an ego-motion estimation is not always available for all platforms. Scan matching using the iterative closest point (ICP) algorithm is a popular alternative approach, providing ego-motion estimates for localization. Iterative closest point computes a point estimate of the transformation between two poses given point clouds captured at these locations. Being a point estimate method, ICP does not deal with the uncertainties in the scan alignment process, which may arise due to sensor noise, partial overlap, or the existence of multiple solutions. Another challenge for ICP is the high computational cost required to align two large point clouds, limiting its applicability to less dynamic problems. In this paper, we address these challenges by leveraging recent advances in probabilistic inference. Specifically, we first address the run-time issue and propose SGD-ICP, which employs stochastic gradient descent (SGD) to solve the optimization problem of ICP. Next, we leverage SGD-ICP to obtain a distribution over transformations and propose a Markov Chain Monte Carlo method using stochastic gradient Langevin dynamics (SGLD) updates. Our ICP variant, termed Bayesian-ICP, is a full Bayesian solution to the problem. To demonstrate the benefits of Bayesian-ICP for mobile robotic applications, we propose an adaptive motion model employing Bayesian-ICP to produce proposal distributions for Monte Carlo Localization. Experiments using both Kinect and 3D LiDAR data show that our proposed SGD-ICP method achieves the same solution quality as standard ICP while being significantly more efficient. We then demonstrate empirically that Bayesian-ICP can produce accurate distributions over pose transformations and is fast enough for online applications. Finally, using Bayesian-ICP as a motion model alleviates the need to tune the motion model parameters from odometry, resulting in better-calibrated localization uncertainty.

Posted in: Mobile Robot Localization , Tagged: ,

Adaptation of industrial robots to variations in tasks through RL

November 10, 2023 09:27 ,

Tian Yu, Qing Chang, User-guided motion planning with reinforcement learning for human-robot collaboration in smart manufacturing, Expert Systems with Applications, Volume 209, 2022 DOI: 10.1016/j.eswa.2022.118291.

In today\u2019s manufacturing system, robots are expected to perform increasingly complex manipulation tasks in collaboration with humans. However, current industrial robots are still largely preprogrammed with very little autonomy and still required to be reprogramed by robotics experts for even slightly changed tasks. Therefore, it is highly desirable that robots can adapt to certain task changes with motion planning strategies to easily work with non-robotic experts in manufacturing environments. In this paper, we propose a user-guided motion planning algorithm in combination with reinforcement learning (RL) method to enable robots automatically generate their motion plans for new tasks by learning from a few kinesthetic human demonstrations. Features of common human demonstrated tasks in a specific application environment, e.g., desk assembly or warehouse loading/unloading are abstracted and saved in a library. The definition of semantical similarity between features in the library and features of a new task is proposed and further used to construct the reward function in RL. To achieve an adaptive motion plan facing task changes or new task requirements, features embedded in the library are mapped to appropriate task segments based on the trained motion planning policy using Q-learning. A new task can be either learned as a combination of a few features in the library or a requirement for further human demonstration if the current library is insufficient for the new task. We evaluate our approach on a 6 DOF UR5e robot on multiple tasks and scenarios and show the effectiveness of our method with respect to different scenarios.

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

On the extended use of RL for navigation in UAVs

November 3, 2023 10:07 ,

Fadi AlMahamid, Katarina Grolinger, Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review, Engineering Applications of Artificial Intelligence, Volume 115, 2022 DOI: 10.1016/j.engappai.2022.105321.

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously \u2014 without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research.

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

Hierarchical RL with diverse methods integrated in the framework

October 27, 2023 06:12 ,

Ye Zhou, Hann Woei Ho, Online robot guidance and navigation in non-stationary environment with hybrid Hierarchical Reinforcement Learning, Engineering Applications of Artificial Intelligence, Volume 114, 2022 DOI: 10.1016/j.engappai.2022.105152.

Hierarchical Reinforcement Learning (HRL) provides an option to solve complex guidance and navigation problems with high-dimensional spaces, multiple objectives, and a large number of states and actions. The current HRL methods often use the same or similar reinforcement learning methods within one application so that multiple objectives can be easily combined. Since there is not a single learning method that can benefit all targets, hybrid Hierarchical Reinforcement Learning (hHRL) was proposed to use various methods to optimize the learning with different types of information and objectives in one application. The previous hHRL method, however, requires manual task-specific designs, which involves engineers\u2019 preferences and may impede its transfer learning ability. This paper, therefore, proposes a systematic online guidance and navigation method under the framework of hHRL, which generalizes training samples with a function approximator, decomposes the state space automatically, and thus does not require task-specific designs. The simulation results indicate that the proposed method is superior to the previous hHRL method, which requires manual decomposition, in terms of the convergence rate and the learnt policy. It is also shown that this method is generally applicable to non-stationary environments changing over episodes and over time without the loss of efficiency even with noisy state information.

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

Human+machine sequential decision making

October 27, 2023 05:59 ,

Q. Zhang, Y. Kang, Y. -B. Zhao, P. Li and S. You, Traded Control of Human\u2013Machine Systems for Sequential Decision-Making Based on Reinforcement Learning, IEEE Transactions on Artificial Intelligence, vol. 3, no. 4, pp. 553-566, Aug. 2022 DOI: 10.1109/TAI.2021.3127857.

Sequential decision-making (SDM) is a common type of decision-making problem with sequential and multistage characteristics. Among them, the learning and updating of policy are the main challenges in solving SDM problems. Unlike previous machine autonomy driven by artificial intelligence alone, we improve the control performance of SDM tasks by combining human intelligence and machine intelligence. Specifically, this article presents a paradigm of a human\u2013machine traded control systems based on reinforcement learning methods to optimize the solution process of sequential decision problems. By designing the idea of autonomous boundary and credibility assessment, we enable humans and machines at the decision-making level of the systems to collaborate more effectively. And the arbitration in the human\u2013machine traded control systems introduces the Bayesian neural network and the dropout mechanism to consider the uncertainty and security constraints. Finally, experiments involving machine traded control, human traded control were implemented. The preliminary experimental results of this article show that our traded control method improves decision-making performance and verifies the effectiveness for SDM problems.

Posted in: Human-robot interaction , Tagged: ,

New algorithms for optimal path planning with certain optimality guarantees

October 27, 2023 05:48 ,

Strub MP, Gammell JD. Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning, The International Journal of Robotics Research. 2022;41(4):390-417 DOI: 10.1177/02783649211069572.

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on 12 problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.

Posted in: Robot motion planning , Tagged:

Probabilistic ICP (Iterative Closest Point) with an intro on classical ICP

October 27, 2023 05:45 ,

Breux Y, Mas A, Lapierre L. On-manifold probabilistic Iterative Closest Point: Application to underwater karst exploration, The International Journal of Robotics Research. 2022;41(9-10):875-902 DOI: 10.1177/02783649221101418.

This paper proposes MpIC, an on-manifold derivation of the probabilistic Iterative Correspondence (pIC) algorithm, which is a stochastic version of the original Iterative Closest Point. It is developed in the context of autonomous underwater karst exploration based on acoustic sonars. First, a derivation of pIC based on the Lie group structure of SE(3) is developed. The closed-form expression of the covariance modeling the estimated rigid transformation is also provided. In a second part, its application to 3D scan matching between acoustic sonar measurements is proposed. It is a prolongation of previous work on elevation angle estimation from wide-beam acoustic sonar. While the pIC approach proposed is intended to be a key component in a Simultaneous Localization and Mapping framework, this paper focuses on assessing its viability on a unitary basis. As ground truth data in karst aquifer are difficult to obtain, quantitative experiments are carried out on a simulated karst environment and show improvement compared to previous state-of-the-art approach. The algorithm is also evaluated on a real underwater cave dataset demonstrating its practical applicability.

See also: Maken FA, Ramos F, Ott L. Bayesian iterative closest point for mobile robot localization. The International Journal of Robotics Research. 2022;41(9-10):851-874. doi:10.1177/02783649221101417

Posted in: Mobile Robot Localization , Tagged: ,