Author Archives: Juan-antonio Fernández-madrigal

A new contribution along the DESPOT line focused on hybrid CPU+GPU platforms

June 15, 2023 10:09 ,

Cai P, Luo Y, Hsu D, Lee WS., HyP-DESPOT: A hybrid parallel algorithm for online planning under uncertainty, The International Journal of Robotics Research. 2021;40(2-3):558-573, DOI: 10.1177/0278364920937074.

Robust planning under uncertainty is critical for robots in uncertain, dynamic environments, but incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, Hybrid Parallel DESPOT (HyP-DESPOT) is a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs; it performs parallel Monte Carlo simulations at the leaf nodes of the search tree using GPUs. HyP-DESPOT provably converges in finite time under moderate conditions and guarantees near-optimality of the solution. Experimental results show that HyP-DESPOT speeds up online planning by up to a factor of several hundred in several challenging robotic tasks in simulation, compared with the original DESPOT algorithm. It also exhibits real-time performance on a robot vehicle navigating among many pedestrians.

Posted in: Robot task planning , Tagged: , ,

Abstraction of controllers

June 15, 2023 10:04 ,

Stanley W. Smith, Murat Arcak, Majid Zamani, Approximate abstractions of control systems with an application to aggregation, Automatica, 119 (2020) DOI: 10.1016/j.automatica.2020.109065.

Previous approaches to constructing abstractions for control systems rely on geometric conditions or, in the case of an interconnected control system, a condition on the interconnection topology. Since these conditions are not always satisfiable, we relax the restrictions on the choice of abstractions, instead opting to select ones which nearly satisfy such conditions via optimization-based approaches. To quantify the resulting effect on the error between the abstraction and concrete control system, we introduce the notions of practical simulation functions and practical storage functions. We show that our approach facilitates the procedure of aggregation, where one creates an abstraction by partitioning agents into aggregate areas. We demonstrate the results on an application where we regulate the temperature in three separate zones of a building.

Posted in: Control Engineering , Tagged: ,

Hybrid Monte Carlo + Interval-based localization

June 15, 2023 09:55 ,

Weiss, R., Glösekötter, P., Prestes, E. et al., Hybridisation of Sequential Monte Carlo Simulation with Non-linear Bounded-error State Estimation Applied to Global Localisation of Mobile Robots, J Intell Robot Syst 99, 335–357 (2020) DOI: 10.1007/s10846-019-01118-7.

Accurate self-localisation is a fundamental ability of any mobile robot. In Monte Carlo localisation, a probability distribution over a space of possible hypotheses accommodates the inherent uncertainty in the position estimate, whereas bounded-error localisation provides a region that is guaranteed to contain the robot. However, this guarantee is accompanied by a constant probability over the confined region and therefore the information yield may not be sufficient for certain practical applications. Four hybrid localisation algorithms are proposed, combining probabilistic filtering with non-linear bounded-error state estimation based on interval analysis. A forward-backward contractor and the Set Inverter via Interval Analysis are hybridised with a bootstrap filter and an unscented particle filter, respectively. The four algorithms are applied to global localisation of an underwater robot, using simulated distance measurements to distinguishable landmarks. As opposed to previous hybrid methods found in the literature, the bounded-error state estimate is not maintained throughout the whole estimation process. Instead, it is only computed once in the beginning, when solving the wake-up robot problem, and after kidnapping of the robot, which drastically reduces the computational cost when compared to the existing algorithms. It is shown that the novel algorithms can solve the wake-up robot problem as well as the kidnapped robot problem more accurately than the two conventional probabilistic filters.

Posted in: Mobile Robot Localization , Tagged: ,

Simultaneous localization, mapping and semantic labelling in mobile robots

June 15, 2023 09:43 ,

Taniguchi, Akira, Hagiwara, Yoshinobu, Taniguchi, Tadahiro, Inamura, Tetsunari, Improved and scalable online learning of spatial concepts and language models with mapping, Autonomous Robots 44(6), DOI: 10.1007/s10514-020-09905-0.

We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.

Posted in: Mobile robot SLAM, Semantic maps for robots , Tagged: ,

Bayesian estimation of the model in model-based RL for robots

September 23, 2021 14:26 ,

Senda, Kei, Hishinuma, Toru, Tani, Yurika, Approximate Bayesian reinforcement learning based on estimation of plant, Autonomous Robots 44(5), DOI: 10.1007/s10514-020-09901-4.

This study proposes an approximate parametric model-based Bayesian reinforcement learning approach for robots, based on online Bayesian estimation and online planning for an estimated model. The proposed approach is designed to learn a robotic task with a few real-world samples and to be robust against model uncertainty, within feasible computational resources. The proposed approach employs two-stage modeling, which is composed of (1) a parametric differential equation model with a few parameters based on prior knowledge such as equations of motion, and (2) a parametric model that interpolates a finite number of transition probability models for online estimation and planning. The proposed approach modifies the online Bayesian estimation to be robust against approximation errors of the parametric model to a real plant. The policy planned for the interpolating model is proven to have a form of theoretical robustness. Numerical simulation and hardware experiments of a planar peg-in-hole task demonstrate the effectiveness of the proposed approach.

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

Adapting the resolution of depth sensors and the location of the high-resolution area (fovea) as a possible attention mechanism in robots

September 23, 2021 14:12 ,

Tasneem Z, Adhivarahan C, Wang D, Xie H, Dantu K, Koppal SJ., Adaptive fovea for scanning depth sensors, The International Journal of Robotics Research. 2020;39(7):837-855, DOI: 10.1177/0278364920920931.

Depth sensors have been used extensively for perception in robotics. Typically these sensors have a fixed angular resolution and field of view (FOV). This is in contrast to human perception, which involves foveating: scanning with the eyes’ highest angular resolution over regions of interest (ROIs). We build a scanning depth sensor that can control its angular resolution over the FOV. This opens up new directions for robotics research, because many algorithms in localization, mapping, exploration, and manipulation make implicit assumptions about the fixed resolution of a depth sensor, impacting latency, energy efficiency, and accuracy. Our algorithms increase resolution in ROIs either through deconvolutions or intelligent sample distribution across the FOV. The areas of high resolution in the sensor FOV act as artificial fovea and we adaptively vary the fovea locations to maximize a well-known information theoretic measure. We demonstrate novel applications such as adaptive time-of-flight (TOF) sensing, LiDAR zoom, gradient-based LiDAR sensing, and energy-efficient LiDAR scanning. As a proof of concept, we mount the sensor on a ground robot platform, showing how to reduce robot motion to obtain a desired scanning resolution. We also present a ROS wrapper for active simulation for our novel sensor in Gazebo. Finally, we provide extensive empirical analysis of all our algorithms, demonstrating trade-offs between time, resolution and stand-off distance.

Posted in: Robot sensors , Tagged: ,

Interesting review of pshycological motivation and the role of RL in studying it

September 23, 2021 14:04 ,

Randall C. O’Reilly, Unraveling the Mysteries of Motivation, Trends in Cognitive Sciences, Volume 24, Issue 6, 2020, Pages 425-434, DOI: 10.1016/j.tics.2020.03.001.

Motivation plays a central role in human behavior and cognition but is not well captured by widely used artificial intelligence (AI) and computational modeling frameworks. This Opinion article addresses two central questions regarding the nature of motivation: what are the nature and dynamics of the internal goals that drive our motivational system and how can this system be sufficiently flexible to support our ability to rapidly adapt to novel situations, tasks, etc.? In reviewing existing systems and neuroscience research and theorizing on these questions, a wealth of insights to constrain the development of computational models of motivation can be found.

Posted in: Psycho-physiological bases of engineering , Tagged: ,

Path planning by merging random sampling (RRT) with informed heuristics (A*)

September 14, 2021 12:44 ,

Jonathan D Gammell, Timothy D Barfoot, Siddhartha S Srinivasa, Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search, The International Journal of Robotics Research. 2020;39(5):543-567, DOI: 10.1177/0278364919890396.

Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively.

Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient, but leaves their performance dependent on the chosen approximation. If the resolution of the chosen approximation is too low, then they may not find a (suitable) solution, but if it is too high, then they may take a prohibitively long time to do so.

Anytime sampling-based planners, such as RRT*, traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found, but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously, but may be prohibitively inefficient at containing a solution.

This article unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT* solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A*, and its approximation improves indefinitely with additional computational time, as in RRT*. It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.

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

Consciousness as a learning framework

September 14, 2021 12:39 ,

Axel Cleeremans, Dalila Achoui, Arnaud Beauny, Lars Keuninckx, Jean-Remy Martin, Santiago Muñoz-Moldes, Laurène Vuillaume, Adélaïde de Heering, Learning to Be Conscious, Trends in Cognitive Sciences, Volume 24, Issue 2, 2020, Pages 112-123 DOI: 10.1016/j.tics.2019.11.011.

Consciousness remains a formidable challenge. Different theories of consciousness have proposed vastly different mechanisms to account for phenomenal experience. Here, appealing to aspects of global workspace theory, higher-order theories, social theories, and predictive processing, we introduce a novel framework: the self-organizing metarerpresentational account (SOMA), in which consciousness is viewed as something that the brain learns to do. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of metarepresentations that qualify target first-order representations. Thus, experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. In this sense, consciousness is the brain’s (unconscious, embodied, enactive, nonconceptual) theory about itself.

Posted in: Psycho-physiological bases of engineering , Tagged:

Including the models into the state of a POMDP for learning them (using POMCPs in a robotic application)

September 14, 2021 12:22 ,

Akinobu Hayashi, Dirk Ruiken, Tadaaki Hasegawa, Christian Goerick, Reasoning about uncertain parameters and agent behaviors through encoded experiences and belief planning, Artificial Intelligence, Volume 280, 2020 DOI: 10.1016/j.artint.2019.103228.

Robots are expected to handle increasingly complex tasks. Such tasks often include interaction with objects or collaboration with other agents. One of the key challenges for reasoning in such situations is the lack of accurate models that hinders the effectiveness of planners. We present a system for online model adaptation that continuously validates and improves models while solving tasks with a belief space planner. We employ the well known online belief planner POMCP. Particles are used to represent hypotheses about the current state and about models of the world. They are sufficient to configure a simulator to provide transition and observation models. We propose an enhanced particle reinvigoration process that leverages prior experiences encoded in a recurrent neural network (RNN). The network is trained through interaction with a large variety of object and agent parametrizations. The RNN is combined with a mixture density network (MDN) to process the current history of observations in order to propose suitable particles and models parametrizations. The proposed method also ensures that newly generated particles are consistent with the current history. These enhancements to the particle reinvigoration process help alleviate problems arising from poor sampling quality in large state spaces and enable handling of dynamics with discontinuities. The proposed approach can be applied to a variety of domains depending on what uncertainty the decision maker needs to reason about. We evaluate the approach with experiments in several domains and compare against other state-of-the-art methods. Experiments are done in a collaborative multi-agent and a single agent object manipulation domain. The experiments are performed both in simulation and on a real robot. The framework handles reasoning with uncertain agent behaviors and with unknown object and environment parametrizations well. The results show good performance and indicate that the proposed approach can improve existing state-of-the-art methods.

Posted in: Applications of reinforcement learning to robots, Robot task planning , Tagged: , ,