Autonomous loading of ore piles with Load-Haul-Dump machines using Deep Reinforcement Learning

This work presents a deep reinforcement learning-based approach to train controllers for the autonomous loading of ore piles with a Load-Haul-Dump (LHD) machine. These controllers must perform a complete loading maneuver, filling the LHD's bucket with material while avoiding wheel drift, dumping material, or getting stuck in the pile. The training process is conducted entirely in simulation, using a simple environment that leverages the Fundamental Equation of Earth-Moving Mechanics so as to achieve a low computational cost. Two different types of policies are trained: one with a hybrid action space and another with a continuous action space. The RL-based policies are evaluated both in simulation and in the real world using a scaled LHD and a scaled muck pile, and their performance is compared to that of a heuristics-based controller and human teleoperation. Additional real-world experiments are performed to assess the robustness of the RL-based policies to measurement errors in the characterization of the piles. Overall, the RL-based controllers show good performance in the real world, achieving fill factors between 71-94%, and less wheel drift than the other baselines during the loading maneuvers. A video showing the training environment and the learned behavior in simulation, as well as some of the performed experiments in the real world, can be found in https://youtu.be/jOpA1rkwhDY.

YotoR-You Only Transform One Representation

This paper introduces YotoR (You Only Transform One Representation), a novel deep learning model for object detection that combines Swin Transformers and YoloR architectures. Transformers, a revolutionary technology in natural language processing, have also significantly impacted computer vision, offering the potential to enhance accuracy and computational efficiency. YotoR combines the robust Swin Transformer backbone with the YoloR neck and head. In our experiments, YotoR models TP5 and BP4 consistently outperform YoloR P6 and Swin Transformers in various evaluations, delivering improved object detection performance and faster inference speeds than Swin Transformer models. These results highlight the potential for further model combinations and improvements in real-time object detection with Transformers. The paper concludes by emphasizing the broader implications of YotoR, including its potential to enhance transformer-based models for image-related tasks.

Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning

This paper proposes a method to combine reinforcement learning (RL) and imitation learning (IL) using a dynamic, performance-based modulation over learning signals. The proposed method combines RL and behavioral cloning (IL), or corrective feedback in the action space (interactive IL/IIL), by dynamically weighting the losses to be optimized, taking into account the backpropagated gradients used to update the policy and the agent's estimated performance. In this manner, RL and IL/IIL losses are combined by equalizing their impact on the policy's updates, while modulating said impact such that IL signals are prioritized at the beginning of the learning process, and as the agent's performance improves, the RL signals become progressively more relevant, allowing for a smooth transition from pure IL/IIL to pure RL. The proposed method is used to learn local planning policies for mobile robots, synthesizing IL/IIL signals online by means of a scripted policy. An extensive evaluation of the application of the proposed method to this task is performed in simulations, and it is empirically shown that it outperforms pure RL in terms of sample efficiency (achieving the same level of performance in the training environment utilizing approximately 4 times less experiences), while consistently producing local planning policies with better performance metrics (achieving an average success rate of 0.959 in an evaluation environment, outperforming pure RL by 12.5% and pure IL by 13.9%). Furthermore, the obtained local planning policies are successfully deployed in the real world without performing any major fine tuning. The proposed method can extend existing RL algorithms, and is applicable to other problems for which generating IL/IIL signals online is feasible. A video summarizing some of the real world experiments that were conducted can be found in https://youtu.be/mZlaXn9WGzw.

Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition

This work proposes a scheme that allows learning complex multi-agent behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is formulated as a Markov game, and solved using deep reinforcement learning. We propose a basic multi-agent extension of TD3 for learning the policy of each player, in a decentralized manner. To ease learning, the task of 2v2 soccer is divided in three stages: 1v0, 1v1 and 2v2. The process of learning in multi-agent stages (1v1 and 2v2) uses agents trained on a previous stage as fixed opponents. In addition, we propose using experience sharing, a method that shares experience from a fixed opponent, trained in a previous stage, for training the agent currently learning, and a form of frame-skipping, to raise performance significantly. Our results show that high quality soccer play can be obtained with our approach in just under 40M interactions. A summarized video of the resulting game play can be found in https://youtu.be/f25l1j1U9RM.

Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach

Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems (e.g., robotics applications). For instance, long training times are required and cannot be accelerated in contrast to simulated environments, and reward functions may be hard to specify/model and/or to compute. Moreover, the transfer of policies learned in a simulator to the real-world has limitations (reality gap). On the other hand, machine learning methods that rely on the transfer of human knowledge to an agent have shown to be time efficient for obtaining well performing policies and do not require a reward function. In this context, we analyze the use of human corrective feedback during task execution to learn policies with high-dimensional state spaces, by using the D-COACH framework, and we propose new variants of this framework. D-COACH is a Deep Learning based extension of COACH (COrrective Advice Communicated by Humans), where humans are able to shape policies through corrective advice. The enhanced version of D-COACH, which is proposed in this paper, largely reduces the time and effort of a human for training a policy. Experimental results validate the efficiency of the D-COACH framework in three different problems (simulated and with real robots), and show that its enhanced version reduces the human training effort considerably, and makes it feasible to learn policies within periods of time in which a DRL agent do not reach any improvement.

Playing Soccer without Colors in the SPL: A Convolutional Neural Network Approach

The goal of this paper is to propose a vision system for humanoid robotic soccer that does not use any color information. The main features of this system are: (i) real-time operation in the NAO robot, and (ii) the ability to detect the ball, the robots, their orientations, the lines and key field features robustly. Our ball detector, robot detector, and robot's orientation detector obtain the highest reported detection rates. The proposed vision system is tested in a SPL field with several NAO robots under realistic and highly demanding conditions. The obtained results are: robot detection rate of 94.90%, ball detection rate of 97.10%, and a completely perceived orientation rate of 99.88% when the observed robot is static, and 95.52% when the observed robot is moving.

Visual SLAM-based Localization and Navigation for Service Robots: The Pepper Case

We propose a Visual-SLAM based localization and navigation system for service robots. Our system is built on top of the ORB-SLAM monocular system but extended by the inclusion of wheel odometry in the estimation procedures. As a case study, the proposed system is validated using the Pepper robot, whose short-range LIDARs and RGB-D camera do not allow the robot to self-localize in large environments. The localization system is tested in navigation tasks using Pepper in two different environments: a medium-size laboratory, and a large-size hall.

Near Real-Time Object Recognition for Pepper based on Deep Neural Networks Running on a Backpack

The main goal of the paper is to provide Pepper with a near real-time object recognition system based on deep neural networks. The proposed system is based on YOLO (You Only Look Once), a deep neural network that is able to detect and recognize objects robustly and at a high speed. In addition, considering that YOLO cannot be run in the Pepper's internal computer in near real-time, we propose to use a Backpack for Pepper, which holds a Jetson TK1 card and a battery. By using this card, Pepper is able to robustly detect and recognize objects in images of 320x320 pixels at about 5 frames per second.

Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demanding, so unfeasible in physical systems for most applications. In this work, we approach an alternative Interactive Machine Learning (IML) strategy for training DNN policies based on human corrective feedback, with a method called Deep COACH (D-COACH). This approach not only takes advantage of the knowledge and insights of human teachers as well as the power of DNNs, but also has no need of a reward function (which sometimes implies the need of external perception for computing rewards). We combine Deep Learning with the COrrective Advice Communicated by Humans (COACH) framework, in which non-expert humans shape policies by correcting the agent's actions during execution. The D-COACH framework has the potential to solve complex problems without much data or time required. Experimental results validated the efficiency of the framework in three different problems (two simulated, one with a real robot), with state spaces of low and high dimensions, showing the capacity to successfully learn policies for continuous action spaces like in the Car Racing and Cart-Pole problems faster than with DRL.

A Survey on Deep Learning Methods for Robot Vision

Deep learning has allowed a paradigm shift in pattern recognition, from using hand-crafted features together with statistical classifiers to using general-purpose learning procedures for learning data-driven representations, features, and classifiers together. The application of this new paradigm has been particularly successful in computer vision, in which the development of deep learning methods for vision applications has become a hot research topic. Given that deep learning has already attracted the attention of the robot vision community, the main purpose of this survey is to address the use of deep learning in robot vision. To achieve this, a comprehensive overview of deep learning and its usage in computer vision is given, that includes a description of the most frequently used neural models and their main application areas. Then, the standard methodology and tools used for designing deep-learning based vision systems are presented. Afterwards, a review of the principal work using deep learning in robot vision is presented, as well as current and future trends related to the use of deep learning in robotics. This survey is intended to be a guide for the developers of robot vision systems.