We quantify the accuracy of various simulators compared to a real world robotic reaching and interaction task. Simulators are used in robotics to design solutions for real world hardware without the need for physical access. The reality gap' prevents solutions developed or learnt in simulation from performing well, or at at all, when transferred to real-world hardware. Making use of a Kinova robotic manipulator and a motion capture system, we record a ground truth enabling comparisons with various simulators, and present quantitative data for various manipulation-oriented robotic tasks. We show the relative strengths and weaknesses of numerous contemporary simulators, highlighting areas of significant discrepancy, and assisting researchers in the field in their selection of appropriate simulators for their use cases. All code and parameter listings are publicly available from: this https URL .
Robotics is an embodied discipline focused on building systems that act in the physical world. However, for numerous reasons highlighted in Section I, simulation is a key tool to many successful robotic engineering and integration efforts. Simulation is fast, cheap, and allows for rapid prototyping and iteration over the composition and control of a robotic system. These benefits are perhaps most strongly felt when learning is used, due to the data-hungry nature of many contemporary learning approaches. Because simulators necessarily abstract various features (e.g., sensory delays, actuator slop), away from the physical reality, there exists a gap between what is simulated and how the final system performs in the real world. Of course, we can in some situations learn directly on real hardware, however this requires sophisticated learning testbeds @cite_25 @cite_5 @cite_4 and, depending on the amount of data required, may be prohibitive in terms of required resources @cite_3 . Here we focus on simulated efforts to learn.
@cite_5 @cite_4 @cite_25 @cite_3
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Current learning-based robot grasping approaches exploit human-labeled datasets for training the models. However, there are two problems with such a methodology: (a) since each object can be grasped in multiple ways, manually labeling grasp locations is not a trivial task; (b) human labeling is biased by semantics. While there have been attempts to train robots using trial-and-error experiments, the amount of data used in such experiments remains substantially low and hence makes the learner prone to over-fitting. In this paper, we take the leap of increasing the available training data to 40 times more than prior work, leading to a dataset size of 50K data points collected over 700 hours of robot grasping attempts. This allows us to train a Convolutional Neural Network (CNN) for the task of predicting grasp locations without severe overfitting. In our formulation, we recast the regression problem to an 18-way binary classification over image patches. We also present a multi-stage learning approach where a CNN trained in one stage is used to collect hard negatives in subsequent stages. Our experiments clearly show the benefit of using large-scale datasets (and multi-stage training) for the task of grasping. We also compare to several baselines and show state-of-the-art performance on generalization to unseen objects for grasping. Evolutionary algorithms have previously shown promise in generating controllers for legged robots. Multiple evaluations across many evolutionary generations are typically required — simulators are frequently used to accommodate this. However, performance degradation is frequently observed when transferring controllers from simulation to reality due to inconsistencies between the two. In this paper we demonstrate a testbed that permits repeated, direct evolution of hexapod controllers as a closed-loop system. The testbed uses a two-stage evolutionary process. In stage 1, a multi-objective evolutionary algorithm spreads a population of controllers across a space of desirable criteria. The second stage allows for specific criteria to be selected for on a per-mission basis, with promising initial controller parameters taken from the first stage. As the optimisation occurs directly on the robot, performance is guaranteed. Furthermore, controllers can be made specific to irregularities in e.g., motor wear, and robot mass distribution, creating controllers that are sensitive to the hardware state of the individual robot. We describe an experimental platform that uses an evolutionary algorithm to automatically tune the gains of a cascaded PID quadcopter controller. All parameters are tuned simultaneously, few platform assumptions are necessary, and no modeling is required. The platform is able to run back-to-back experiments for over 24 hours without human intervention. In a sample experiment, we apply the system to solve a hovering task — the behaviors generated by an initially-random population of gain vectors are evaluated and gradually improved, with the attainment of high fitness hover controllers reported within 12 hours. We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion of the gripper will result in successful grasps, using only monocular camera images and independently of camera calibration or the current robot pose. This requires the network to observe the spatial relationship between the gripper and objects in the scene, thus learning hand-eye coordination. We then use this network to servo the gripper in real time to achieve successful grasps. To train our network, we collected over 800,000 grasp attempts over the course of two months, using between 6 and 14 robotic manipulators at any given time, with differences in camera placement and hardware. Our experimental evaluation demonstrates that our method achieves effective real-time control, can successfully grasp novel objects, and corrects mistakes by continuous servoing.