Becoming the Expert - Interactive Multi-Class Machine Teaching

Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important and representative images first, followed by less important images later - or not at all. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithm chooses, online, which labeled images from a teaching set should be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student's ability and progress, based on their correct and incorrect answers, produces better 'experts'. We present results using real human participants across several varied and challenging real-world datasets.Comment: CVPR 201

SceneScore: Learning a Cost Function for Object Arrangement

Arranging objects correctly is a key capability for robots which unlocks a wide range of useful tasks. A prerequisite for creating successful arrangements is the ability to evaluate the desirability of a given arrangement. Our method "SceneScore" learns a cost function for arrangements, such that desirable, human-like arrangements have a low cost. We learn the distribution of training arrangements offline using an energy-based model, solely from example images without requiring environment interaction or human supervision. Our model is represented by a graph neural network which learns object-object relations, using graphs constructed from images. Experiments demonstrate that the learned cost function can be used to predict poses for missing objects, generalise to novel objects using semantic features, and can be composed with other cost functions to satisfy constraints at inference time.Comment: Presented at CoRL 2023 LEAP Workshop. Webpage: https://sites.google.com/view/scenescor

Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer

Domain randomisation is a very popular method for visual sim-to-real transfer in robotics, due to its simplicity and ability to achieve transfer without any real-world images at all. Nonetheless, a number of design choices must be made to achieve optimal transfer. In this paper, we perform a comprehensive benchmarking study on these different choices, with two key experiments evaluated on a real-world object pose estimation task. First, we study the rendering quality, and find that a small number of high-quality images is superior to a large number of low-quality images. Second, we study the type of randomisation, and find that both distractors and textures are important for generalisation to novel environments.Comment: The paper has been accepted to be published in ICRA 202

Few-Shot In-Context Imitation Learning via Implicit Graph Alignment

Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a class makes it difficult to infer the task-relevant relationship between the new objects and the objects in the demonstrations. We address this by formulating imitation learning as a conditional alignment problem between graph representations of objects. Consequently, we show that this conditioning allows for in-context learning, where a robot can perform a task on a set of new objects immediately after the demonstrations, without any prior knowledge about the object class or any further training. In our experiments, we explore and validate our design choices, and we show that our method is highly effective for few-shot learning of several real-world, everyday tasks, whilst outperforming baselines. Videos are available on our project webpage at https://www.robot-learning.uk/implicit-graph-alignment.Comment: Published at CoRL 2023. Videos are available on our project webpage at https://www.robot-learning.uk/implicit-graph-alignmen

Where To Start? Transferring Simple Skills to Complex Environments

Robot learning provides a number of ways to teach robots simple skills, such as grasping. However, these skills are usually trained in open, clutter-free environments, and therefore would likely cause undesirable collisions in more complex, cluttered environments. In this work, we introduce an affordance model based on a graph representation of an environment, which is optimised during deployment to find suitable robot configurations to start a skill from, such that the skill can be executed without any collisions. We demonstrate that our method can generalise a priori acquired skills to previously unseen cluttered and constrained environments, in simulation and in the real world, for both a grasping and a placing task.Comment: Accepted at CoRL 2022. Videos are available on our project webpage at https://www.robot-learning.uk/where-to-star

Pipe smoothing genetic algorithm for least cost water distribution network design

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.GECCO '13 Proceedings of the 15th annual conference on Genetic and evolutionary computation Amsterdam, Netherlands — July 06 - 10, 2013This paper describes the development of a Pipe Smoothing Genetic Algorithm (PSGA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we propose a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm compared to a standard genetic algorithm. Both PSGA and the standard genetic algorithm were tested on benchmark water distribution networks from the literature. In all cases PSGA achieves higher optimality in fewer solution evaluations than the standard genetic algorithm

Neural regulation of the kidney function in rats with cisplatin induced renal failure

Aim: Chronic kidney disease (CKD) is often associated with a disturbed cardiovascular homeostasis. This investigation explored the role of the renal innervation in mediating deranged baroreflex control of renal sympathetic nerve activity (RSNA) and renal excretory function in cisplatin-induced renal failure.Methods: Rats were either intact or bilaterally renally denervated 4 days prior to receiving cisplatin (5 mg/kg i.p.) and entered a chronic metabolic study for 8 days. At day 8, other groups of rats were prepared for acute measurement of RSNA or renal function with either intact or denervated kidneys.Results: Following the cisplatin challenge, creatinine clearance was 50% lower while fractional sodium excretion and renal cortical and medullary TGF-β1 concentrations were 3–4 fold higher in both intact and renally denervated rats compared to control rats. In cisplatin-treated rats, the maximal gain of the high-pressure baroreflex curve was only 20% that of control rats, but following renal denervation not different from that of renally denervated control rats. Volume expansion reduced RSNA by 50% in control and in cisplatin-treated rats but only following bilateral renal denervation. The volume expansion mediated natriuresis/diuresis was absent in the cisplatin-treated rats but was normalized following renal denervation.Conclusions: Cisplatin-induced renal injury impaired renal function and caused a sympatho-excitation with blunting of high and low pressure baroreflex regulation of RSNA, which was dependent on the renal innervation. It is suggested that in man with CKD there is a dysregulation of the neural control of the kidney mediated by its sensory innervation

DALL-E-Bot: Introducing Web-Scale Diffusion Models to Robotics

We introduce the first work to explore web-scale diffusion models for robotics. DALL-E-Bot enables a robot to rearrange objects in a scene, by first inferring a text description of those objects, then generating an image representing a natural, human-like arrangement of those objects, and finally physically arranging the objects according to that image. The significance is that we achieve this zero-shot using DALL-E, without needing any further data collection or training. Encouraging real-world results with human studies show that this is a promising direction for the future of web-scale robot learning. We also propose a list of recommendations to the text-to-image community, to align further developments of these models with applications to robotics.Comment: Webpage and videos: ( https://www.robot-learning.uk/dall-e-bot ) V1: initial submission. V2: new baseline

One-Shot Imitation Learning: A Pose Estimation Perspective

In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge. We show how, with these constraints, imitation learning can be formulated as a combination of trajectory transfer and unseen object pose estimation. To explore this idea, we provide an in-depth study on how state-of-the-art unseen object pose estimators perform for one-shot imitation learning on ten real-world tasks, and we take a deep dive into the effects that camera calibration, pose estimation error, and spatial generalisation have on task success rates. For videos, please visit https://www.robot-learning.uk/pose-estimation-perspective.Comment: Published at the 7th Conference on Robot Learning (CoRL 2023). For more details please visit https://www.robot-learning.uk/pose-estimation-perspectiv