RLBench: The Robot Learning Benchmark &amp; Learning Environment

James, Stephen; L., Z.; Arrojo, David Rovick; Davison, Andrew J.

doi:10.1109/lra.2020.2974707

Cited by 219 publications

(162 citation statements)

References 50 publications

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“…Scaling meta-imitation learning to different “skill-level” tasks (i.e., pushing, pick-and-place, peg insertion, etc.) is the ongoing work in the robot learning community (Yu et al, 2019 , 2020 ; James et al, 2020 ). Concurrently, since Ajay et al ( 2020 ) reports that their model learns these skill embedding based on probabilistic inference, we suppose that our framework can scale to more diverse tasks, which we would like to evaluate as future work.…”

Section: Discussionmentioning

confidence: 99%

Modeling Task Uncertainty for Safe Meta-Imitation Learning

et al. 2020

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To endow robots with the flexibility to perform a wide range of tasks in diverse and complex environments, learning their controller from experience data is a promising approach. In particular, some recent meta-learning methods are shown to solve novel tasks by leveraging their experience of performing other tasks during training. Although studies around meta-learning of robot control have worked on improving the performance, the safety issue has not been fully explored, which is also an important consideration in the deployment. In this paper, we firstly relate uncertainty on task inference with the safety in meta-learning of visual imitation, and then propose a novel framework for estimating the task uncertainty through probabilistic inference in the task-embedding space, called PETNet. We validate PETNet with a manipulation task with a simulated robot arm in terms of the task performance and uncertainty evaluation on task inference. Following the standard benchmark procedure in meta-imitation learning, we show PETNet can achieve the same or higher level of performance (success rate of novel tasks at meta-test time) as previous methods. In addition, by testing PETNet with semantically inappropriate or synthesized out-of-distribution demonstrations, PETNet shows the ability to capture the uncertainty about the tasks inherent in the given demonstrations, which allows the robot to identify situations where the controller might not perform properly. These results illustrate our proposal takes a significant step forward to the safe deployment of robot learning systems into diverse tasks and environments.

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Section: Discussionmentioning

confidence: 99%

Modeling Task Uncertainty for Safe Meta-Imitation Learning

et al. 2020

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“…Procedural content generation (PCG) has been widely used for the automated creation of environments in physics simulations and video games [75,68,10]. Recently, PCG tools have been used to create benchmarks for robot learning and reinforcement learning [47,86,69,88,40]. Deign of these task environments can be labor intensive and heavily relies on human expertise.…”

Section: Procedural Task Generationmentioning

confidence: 99%

Discovering Generalizable Skills via Automated Generation of Diverse Tasks

Fang¹,

Zhu²,

Savarese³

et al. 2021

Robotics: Science and Systems XVII

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The learning efficiency and generalization ability of an intelligent agent can be greatly improved by utilizing a useful set of skills. However, the design of robot skills can often be intractable in real-world applications due to the prohibitive amount of effort and expertise that it requires. In this work, we introduce Skill Learning In Diversified Environments (SLIDE), a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills which incentivizes the skills to produce different outcomes in the same environment, our method pairs each skill with a unique task produced by a trainable task generator. To encourage generalizable skills to emerge, our method trains each skill to specialize in the paired task and maximizes the diversity of the generated tasks. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks. We demonstrate that the proposed method can effectively learn a variety of robot skills in two tabletop manipulation domains. Our results suggest that the learned skills can effectively improve the robot's performance in various unseen target tasks compared to existing reinforcement learning and skill learning methods.

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“…In 2018, Yu et al [146] applied deep reinforcement learning to image repair and achieved good results. James et al [147] proposed a new benchmark and learning environment for challenging robotic learning: RLBench, which is designed to accelerate progress in the field of visually guided manipulation. The above research lays a foundation for the application of deep reinforcement learning in machine vision to guide robots in recognizing and grasping objects.…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…Robot [147,151,156,184] Computer vision [185][186][187][188][189][190] Game [191][192][193] Autonomous driving [185,186]…”

Section: Learningmentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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RLBench: The Robot Learning Benchmark & Learning Environment

Cited by 219 publications

References 50 publications

Modeling Task Uncertainty for Safe Meta-Imitation Learning

Modeling Task Uncertainty for Safe Meta-Imitation Learning

Discovering Generalizable Skills via Automated Generation of Diverse Tasks

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

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