Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance

What would you do? Acting by learning to predict

We propose to learn tasks directly from visual demonstrations by learning to predict the outcome of human and robot actions on an environment. We enable a robot to physically perform a human demonstrated task without knowledge of the thought processes or actions of the human, only their visually observable state transitions. We evaluate our approach on two table-top, object manipulation tasks and demonstrate generalisation to previously unseen states. Our approach reduces the priors required to implement a robot task learning system compared with the existing approaches of Learning from Demonstration, Reinforcement Learning and Inverse Reinforcement Learning

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Breaking Through The Brick Wall - Using An Interdisciplinary Strategy To Market High-Tech Products

Marketing high-Tech products poses challenges to classically trained marketers who may be unfamiliar with a product\u27s underlying science or technology. Often such marketing teams, lacking scientific backgrounds, find it particularly difficult to interface with R&D and truly understand a product\u27s inner workings - something we show to be critical to fully assessing a product for effective differentiation and marketing. By utilizing a novel marketing strategy, the Interdisciplinary Strategic Marketing Framework, we show the need to hire interdisciplinarians - marketers with science backgrounds - to champion marketing teams which are able to overcome the R&D-To-Marketing communicative brick wall and delve into a product\u27s technology, such that it can be marketed most effectively, fully assessing all potential science-based and classical aspects of a marketing campaign. Provided within the framework is a strategy for organizing a firm\u27s marketing functionaries (possibly even integrating them into product development), and a system with which the (interdisciplinarian) marketer can efficiently categorize high-Tech products, as well as corresponding methodologies which with to approach differentiating and marketing high-Tech products. An illustrative consulting case is also included to demonstrate an application of the marketing methodologies. © 2011 World Scientific Publishing Company

BREAKING THROUGH THE "BRICK WALL" — USING AN INTERDISCIPLINARY STRATEGY TO MARKET HIGH-TECH PRODUCTS

Marketing high-tech products poses challenges to classically trained marketers who may be unfamiliar with a product's underlying science or technology. Often such marketing teams, lacking scientific backgrounds, find it particularly difficult to interface with R&D and truly understand a product's inner workings — something we show to be critical to fully assessing a product for effective differentiation and marketing. By utilizing a novel marketing strategy, the Interdisciplinary Strategic Marketing Framework, we show the need to hire interdisciplinarians — marketers with science backgrounds — to champion marketing teams which are able to overcome the R&D-to-Marketing communicative "brick wall" and delve into a product's technology, such that it can be marketed most effectively, fully assessing all potential science-based and classical aspects of a marketing campaign. Provided within the framework is a strategy for organizing a firm's marketing functionaries (possibly even integrating them into product development), and a system with which the (interdisciplinarian) marketer can efficiently categorize high-tech products, as well as corresponding methodologies which with to approach differentiating and marketing high-tech products. An illustrative consulting case is also included to demonstrate an application of the marketing methodologies.New products introduction, inter-disciplinary strategy, firm performance

Solid Freeform Fabrication of Soft Tissue Simulators for Needle Injection

Medical training and surgical planning are becoming important applications for Solid Freeform Fabrication (SFF). To date, the vast majority of these training applications have relied on the production of stiff materials to replicate bones. Others have used soft materials to replicate soft tissues without regard for replicating the mechanical properties of the tissues. Varying the Young’s Modulus of a printed object using various propriety materials and processes, we were able to replicate the injection force profile of a sharp hypodermic needle stick using blunted needles safer for training usage. The composite structures and needle pairs have a puncture force of 2.8 Newtons at a depth of 9 - 15mm, within the reported range for human skin. This will provide a safer training alternative in the use of hypodermic needles without the need for training on humans or animals.Mechanical Engineerin

A robustness analysis of Deep Q Networks

Deep Q Networks are a type of deep reinforcement learning algorithm that have been shown to be particularly adept at learning a variety of tasks with minimal priors. Specifically, DQN agents have been shown to learn a variety of Atari 2600 video games using only raw images of the game screen and the game score. To leverage DQNs in real world robotics applications, we must first understand how robust these networks are to the perceptual noise common to all robotics domains. In this pa- per, we present an analysis of the robustness of Deep Q Networks to various types of perceptual noise (changing brightness, Gaussian blur, salt and pepper, distractors). We present a benchmark example that involves playing the game Breakout though a webcam and screen environment, like humans do. We present a simple training approach to improve the performance maintained when transferring a DQN agent trained in simulation to the real world (36% vs. 1% maintained performance - see Table 1). We also evaluate DQN agents trained under a variety of simulation environments to report for the first time how DQNs cope with perceptual noise, common to real world robotic applications

The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB). Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of <i>complete</i> robotic systems -- including perception and manipulation -- instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot