Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning

Quality-diversity optimization: a novel branch of stochastic optimization

Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal optimization algorithms search for the highest peaks in the search space that can be more than one. Quality-Diversity algorithms are a recent addition to the evolutionary computation toolbox that do not only search for a single set of local optima, but instead try to illuminate the search space. In effect, they provide a holistic view of how high-performing solutions are distributed throughout a search space. The main differences with multimodal optimization algorithms are that (1) Quality-Diversity typically works in the behavioral space (or feature space), and not in the genotypic (or parameter) space, and (2) Quality-Diversity attempts to fill the whole behavior space, even if the niche is not a peak in the fitness landscape. In this chapter, we provide a gentle introduction to Quality-Diversity optimization, discuss the main representative algorithms, and the main current topics under consideration in the community. Throughout the chapter, we also discuss several successful applications of Quality-Diversity algorithms, including deep learning, robotics, and reinforcement learning

Behavior policy learning: Learning multi-stage tasks via solution sketches and model-based controllers

Multi-stage tasks are a challenge for reinforcement learning methods, and require either specific task knowledge (e.g., task segmentation) or big amount of interaction times to be learned. In this paper, we propose Behavior Policy Learning (BPL) that effectively combines 1) only few solution sketches, that is demonstrations without the actions, but only the states, 2) model-based controllers, and 3) simulations to effectively solve multi-stage tasks without strong knowledge about the underlying task. Our main intuition is that solution sketches alone can provide strong data for learning a high-level trajectory by imitation, and model-based controllers can be used to follow this trajectory (we call it behavior) effectively. Finally, we utilize robotic simulations to further improve the policy and make it robust in a Sim2Real style. We evaluate our method in simulation with a robotic manipulator that has to perform two tasks with variations: 1) grasp a box and place it in a basket, and 2) re-place a book on a different level within a bookcase. We also validate the Sim2Real capabilities of our method by performing real-world experiments and realistic simulated experiments where the objects are tracked through an RGB-D camera for the first task

Hierarchical quality-diversity for online damage recovery

Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical damages in a few minutes. These adaptation capabilities are directly linked to the behavioural diversity in the repertoire. The more alternatives the robot has to execute a skill, the better are the chances that it can adapt to a new situation. However, solving complex tasks, like maze navigation, usually requires multiple different skills. Finding a large behavioural diversity for these multiple skills often leads to an intractable exponential growth of the number of required solutions. In this paper, we introduce the Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural repertoire to learn diverse skills and leverages them to make the robot more adaptive to different situations. We show that the hierarchical decomposition of skills enables the robot to learn more complex behaviours while keeping the learning of the repertoire tractable. The experiments with a hexapod robot show that our method solves maze navigation tasks with 20% less actions in the most challenging scenarios than the best baseline while having 57% less complete failures

Alternating optimisation and quadrature for robust control

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator. This paper considers the problem of finding a robust policy while taking into account the impact of environment variables. We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings. ALOQ is robust to the presence of significant rare events, which may not be observable under random sampling, but play a substantial role in determining the optimal policy. Experimental results across different domains show that ALOQ can learn more efficiently and robustly than existing methods

Contribution a l'etude des interactions entre l'acier inoxydable Z2CN 18-10 ou le nickel et certains constituants du lait (Lactoserum ou caseinate de sodium) : mise en evidence et evolution de films adsorbees a la surface des materiaux

SIGLECNRS TD Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

A survey on policy search algorithms for learning robot controllers in a handful of trials

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time

Robust reinforcement learning with Bayesian optimisation and quadrature

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems. However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator. This article considers the problem of finding a robust policy while taking into account the impact of environment variables. We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings. We also present Transferable ALOQ (TALOQ), for settings where simulator inaccuracies lead to difficulty in transferring the learnt policy to the physical system. We show that our algorithms are robust to the presence of significant rare events, which may not be observable under random sampling but play a substantial role in determining the optimal policy. Experimental results across different domains show that our algorithms learn robust policies efficiently

Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization

Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components. Limbo is currently mostly used for data-efficient policy search in robot learning and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials. The implementation of Limbo follows a policy-based design that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (cost of virtual functions). The regression benchmarks show that the query time of Limbo’s Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library and optional bindings to Intel’s MKL). The library is distributed under the CeCILL-C license via a Github repository. The code is standard-compliant but it is currently mostly developed for GNU/Linux and Mac OS X with both the GCC and Clang compilers. New contributors can rely on a full API reference, while their developments are checked via a continuous integration platform (automatic unit-testing routines). Limbo is currently used in the ERC project ResiBots, which is focused on data-efficient trial-and-error learning for robot damage recovery, and in the H2020 projet PAL, which uses social robots to help coping with diabetes. It has been instrumental in many scientific publications since 2015Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components. Limbo is currently mostly used for data-efficient policy search in robot learning and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials. The implementation of Limbo follows a policy-based design that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (cost of virtual functions). The regression benchmarks show that the query time of Limbo’s Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library and optional bindings to Intel’s MKL). The library is distributed under the CeCILL-C license via a Github repository. The code is standard-compliant but it is currently mostly developed for GNU/Linux and Mac OS X with both the GCC and Clang compilers. New contributors can rely on a full API reference, while their developments are checked via a continuous integration platform (automatic unit-testing routines). Limbo is currently used in the ERC project ResiBots, which is focused on data-efficient trial-and-error learning for robot damage recovery, and in the H2020 projet PAL, which uses social robots to help coping with diabetes. It has been instrumental in many scientific publications since 201

Benchmark for Human-to-Robot Handovers of Unseen Containers With Unknown Filling

The real-time estimation through vision of the physical properties of objects manipulated by humans is important to inform the control of robots for performing accurate and safe grasps of objects handed over by humans. However, estimating the 3D pose and dimensions of previously unseen objects using only RGB cameras is challenging due to illumination variations, reflective surfaces, transparencies, and occlusions caused both by the human and the robot. In this letter, we present a benchmark for dynamic human-to-robot handovers that do not rely on a motion capture system, markers, or prior knowledge of specific objects. To facilitate comparisons, the benchmark focuses on cups with different levels of transparencies and with an unknown amount of an unknown filling. The performance scores assess the overall system as well as its components in order to help isolate modules of the pipeline that need improvements. In addition to the task description and the performance scores, we also present and distribute as open source a baseline implementation for the overall pipeline to enable comparisons and facilitate progress