Category Archives: Applications Of Reinforcement Learning To Robots

Mixing Monte-Carlo Tree Search with Q-learning for robot learning

June 30, 2023 09:48 ,

Francesco Riccio, Roberto Capobianco, Daniele Nardi, LoOP: Iterative learning for optimistic planning on robots, . Robotics and Autonomous Systems, Volume 36, 2021 DOI: 10.1016/j.robot.2020.103693.

Efficient robotic behaviors require robustness and adaptation to dynamic changes of the environment, whose characteristics rapidly vary during robot operation. To generate effective robot action policies, planning and learning techniques have shown the most promising results. However, if considered individually, they present different limitations. Planning techniques lack generalization among similar states and require experts to define behavioral routines at different levels of abstraction. Conversely, learning methods usually require a considerable number of training samples and iterations of the algorithm. To overcome these issues, and to efficiently generate robot behaviors, we introduce LoOP, an iterative learning algorithm for optimistic planning that combines state-of-the-art planning and learning techniques to generate action policies. The main contribution of LoOP is the combination of Monte-Carlo Search Planning and Q-learning, which enables focused exploration during policy refinement in different robotic applications. We demonstrate the robustness and flexibility of LoOP in various domains and multiple robotic platforms, by validating the proposed approach with an extensive experimental evaluation.

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Deep learning RL methods for robot navigation

June 29, 2023 10:47 ,

Luong, M., Pham, C., Incremental Learning for Autonomous Navigation of Mobile Robots based on Deep Reinforcement Learning, . J Intell Robot Syst 101, 1 (2021) DOI: 10.1007/s10846-020-01262-5.

This paper presents an incremental learning method and system for autonomous robot navigation. The range finder laser sensor and online deep reinforcement learning are utilized for generating the navigation policy, which is effective for avoiding obstacles along the robot’s trajectories as well as for robot’s reaching the destination. An empirical experiment is conducted under simulation and real-world settings. Under the simulation environment, the results show that the proposed method can generate a highly effective navigation policy (more than 90% accuracy) after only 150k training iterations. Moreover, our system has slightly outperformed deep-Q, while having considerably surpassed Proximal Policy Optimization, two recent state-of-the art robot navigation systems. Finally, two experiments are performed to demonstrate the feasibility and effectiveness of our robot’s proposed navigation system in real-time under real-world settings.

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Improving the simulation-to-real transfer of learning robotic skills by learning smaller skills and how to connect them in reality

June 26, 2023 12:25 ,

Julian RC, Heiden E, He Z, et al., Scaling simulation-to-real transfer by learning a latent space of robot skills, . The International Journal of Robotics Research. 2020;39(10-11):1259-1278 DOI: 10.1177/0278364920944474.

We present a strategy for simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation–reality gap, we propose a method for increasing the sample efficiency and robustness of existing simulation-to-real approaches which exploits hierarchy and online adaptation. Instead of learning a unique policy for each desired robotic task, we learn a diverse set of skills and their variations, and embed those skill variations in a continuously parameterized space. We then interpolate, search, and plan in this space to find a transferable policy which solves more complex, high-level tasks by combining low-level skills and their variations. In this work, we first characterize the behavior of this learned skill space, by experimenting with several techniques for composing pre-learned latent skills. We then discuss an algorithm which allows our method to perform long-horizon tasks never seen in simulation, by intelligently sequencing short-horizon latent skills. Our algorithm adapts to unseen tasks online by repeatedly choosing new skills from the latent space, using live sensor data and simulation to predict which latent skill will perform best next in the real world. Importantly, our method learns to control a real robot in joint-space to achieve these high-level tasks with little or no on-robot time, despite the fact that the low-level policies may not be perfectly transferable from simulation to real, and that the low-level skills were not trained on any examples of high-level tasks. In addition to our results indicating a lower sample complexity for families of tasks, we believe that our method provides a promising template for combining learning-based methods with proven classical robotics algorithms such as model-predictive control.

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Combination of RL with human provided models for navigation

June 26, 2023 11:58 ,

Amarildo Likmeta, Alberto Maria Metelli, Andrea Tirinzoni, Riccardo Giol, Marcello Restelli, Danilo Romano, Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving, . Robotics and Autonomous Systems, Volume 131, 2020 DOI: 10.1016/j.robot.2020.103568.

The design of high-level decision-making systems is a topical problem in the field of autonomous driving. In this paper, we combine traditional rule-based strategies and reinforcement learning (RL) with the goal of achieving transparency and robustness. On the one hand, the use of handcrafted rule-based controllers allows for transparency, i.e., it is always possible to determine why a given decision was made, but they struggle to scale to complex driving scenarios, in which several objectives need to be considered. On the other hand, black-box RL approaches enable us to deal with more complex scenarios, but they are usually hardly interpretable. In this paper, we combine the best properties of these two worlds by designing parametric rule-based controllers, in which interpretable rules can be provided by domain experts and their parameters are learned via RL. After illustrating how to apply parameter-based RL methods (PGPE) to this setting, we present extensive numerical simulations in the highway and in two urban scenarios: intersection and roundabout. For each scenario, we show the formalization as an RL problem and we discuss the results of our approach in comparison with handcrafted rule-based controllers and black-box RL techniques.

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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.

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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.

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Application of Deep RL to person following by a robot, reducing the training effort of the network by reusing simple state situations in many artificially generated states

September 14, 2021 12:02 ,

Pang, L., Zhang, Y., Coleman, S. et al., Efficient Hybrid-Supervised Deep Reinforcement Learning for Person Following Robot, J Intell Robot Syst 97, 299–312 (2020), DOI: 10.1007/s10846-019-01030-0.

Traditional person following robots usually need hand-crafted features and a well-designed controller to follow the assigned person. Normally it is difficult to be applied in outdoor situations due to variability and complexity of the environment. In this paper, we propose an approach in which an agent is trained by hybrid-supervised deep reinforcement learning (DRL) to perform a person following task in end-to-end manner. The approach enables the robot to learn features autonomously from monocular images and to enhance performance via robot-environment interaction. Experiments show that the proposed approach is adaptive to complex situations with significant illumination variation, object occlusion, target disappearance, pose change, and pedestrian interference. In order to speed up the training process to ensure easy application of DRL to real-world robotic follower controls, we apply an integration method through which the agent receives prior knowledge from a supervised learning (SL) policy network and reinforces its performance with a value-based or policy-based (including actor-critic method) DRL model. We also utilize an efficient data collection approach for supervised learning in the context of person following. Experimental results not only verify the robustness of the proposed DRL-based person following robot system, but also indicate how easily the robot can learn from mistakes and improve performance.

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Mixing human advice and reward functions for improving reinforcement learning of motor skills in robots with a nice related work on interactive RL

October 20, 2020 19:31 ,

Carlos Celemin, Guilherme Maeda, Javier Ruiz-del-Solar, Jan Peters, Jens Kober, Reinforcement learning of motor skills using Policy Search and human corrective advice, The International Journal of Robotics Research, Vol 38, Issue 14, 2019, DOI: 10.1177/0278364919871998.

Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible. Some reinforcement learning methods, like Policy Search, offer stable convergence toward locally optimal solutions, whereas interactive machine learning or learning-from-demonstration methods allow fast transfer of human knowledge to the agents. However, most methods require expert demonstrations. In this work, we propose the use of human corrective advice in the actions domain for learning motor trajectories. Additionally, we combine this human feedback with reward functions in a Policy Search learning scheme. The use of both sources of information speeds up the learning process, since the intuitive knowledge of the human teacher can be easily transferred to the agent, while the Policy Search method with the cost/reward function take over for supervising the process and reducing the influence of occasional wrong human corrections. This interactive approach has been validated for learning movement primitives with simulated arms with several degrees of freedom in reaching via-point movements, and also using real robots in such tasks as “writing characters” and the ball-in-a-cup game. Compared with standard reinforcement learning without human advice, the results show that the proposed method not only converges to higher rewards when learning movement primitives, but also that the learning is sped up by a factor of 4–40 times, depending on the task.

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Reinforcement learning for improving autonomy of mobile robots in calibrating visual sensors

October 20, 2020 15:31 ,

Fernando Nobre, Christoffer Heckman, Learning to calibrate: Reinforcement learning for guided calibration of visual–inertial rigs,. The International Journal of Robotics Research, 38(12–13), 1352–1374, DOI: 10.1177/0278364919844824.

We present a new approach to assisted intrinsic and extrinsic calibration with an observability-aware visual–inertial calibration system that guides the user through the calibration procedure by suggesting easy-to-perform motions that render the calibration parameters observable. This is done by identifying which subset of the parameter space is rendered observable with a rank-revealing decomposition of the Fisher information matrix, modeling calibration as a Markov decision process and using reinforcement learning to establish which discrete sequence of motions optimizes for the regression of the desired parameters. The goal is to address the assumption common to most calibration solutions: that sufficiently informative motions are provided by the operator. We do not make use of a process model and instead leverage an experience-based approach that is broadly applicable to any platform in the context of simultaneous localization and mapping. This is a step in the direction of long-term autonomy and “power-on-and-go” robotic systems, making repeatable and reliable calibration accessible to the non-expert operator.

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A kind of reinforcement learning that decouples modelling from planning using Gaussian Processes for the former

October 10, 2019 15:07 ,

Rakicevic, N. & Kormushev, P., Active learning via informed search in movement parameter space for efficient robot task learning and transfer. Auton Robot (2019) 43: 1917, DOI: 10.1007/s10514-019-09842-7.

Learning complex physical tasks via trial-and-error is still challenging for high-degree-of-freedom robots. Greatest challenges are devising a suitable objective function that defines the task, and the high sample complexity of learning the task. We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function. The task model is specific to a task and maps the parameter space, defining a trial, to the trial outcome space. The exploration component enables efficient search in the trial-parameter space to generate the subsequent most informative trials, by simultaneously exploiting all the information gained from previous trials and reducing the task model’s overall uncertainty. We analyse the performance of our framework in a simulation environment and further validate it on a challenging bimanual-robot puck-passing task. Results show that the robot successfully acquires the necessary skills after only 100 trials without any prior information about the task or target positions. Decoupling the framework’s components also enables efficient skill transfer to new environments which is validated experimentally.

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