arXiv subjects

Barbara Webb

Publications and source records attributed to Barbara Webb.

7 recordsLinked to original sources

arXiv2026

Minimal Footprint Grasping Inspired by Ants

Ants are highly capable of grasping objects in clutter, and we have recently observed that this involves substantial use of their forelegs. The forelegs, more specifically the tarsi, have high friction microstructures (setal pads), are covered in hairs, and have a flexible under-actuated tip. Here we abstract these features to test their functional advantages for a novel low-cost gripper design, suitable for bin-picking applications. In our implementation, the gripper legs are long and slim, with high friction gripping pads, low friction hairs and single-segment tarsus-like structure to mimic the insect's setal pads, hairs, and the tarsi's interactive compliance. Experimental evaluation shows this design is highly robust for grasping a wide variety of individual consumer objects, with all grasp attempts successful. In addition, we demonstrate this design is effective for picking single objects from dense clutter, a task at which ants also show high competence. The work advances grasping technology and shed new light on the mechanical importance of hairy structures and tarsal flexibility in insects.

cs.RO↗

arXiv2026

Insect-inspired Visual Point-goal Navigation

Insect neuroethology provides a compelling biological template for efficient autonomous navigation. We draw an analogy between the formal embodied AI visual point-goal navigation task and the ability of insects to discover, learn, and refine visually guided paths around obstacles between a discovered food location and their nest. We develop a novel integrative model of mushroom body and central complex, two insect brain structures, that have been implicated, respectively, in associative learning and path integration. We demonstrate the mushroom body learning triggered by collisions results in adaptive obstacle avoidance and consequently optimised paths to the goal, corroborating the hypothesis of recent behavioural work that an insect can learn continuously as they travel. The embodied insect-inspired model achieves success rates comparable to recent state-of-the-art models at many orders of magnitude less computational cost in the standardised Habitat point-goal navigation benchmark. Testing in a more realistic simulated environment validates its robustness to perturbations.

cs.AI↗

arXiv2023

AntGrip -- Boosting Parallel Plate Gripper Performance Inspired by the Internal Hairs of Ant Mandibles

Ants use their mandibles - effectively a two-finger gripper - for a wide range of grasping activities. Here we investigate whether mimicking the internal hairs found on ant mandibles can improve performance of a two-finger parallel plate robot gripper. With bin picking applications in mind, the gripper fingers are long and slim, with interchangeable soft gripping pads that can be hairy or hairless. A total of 2400 video-documented experiments have been conducted, comparing hairless to hairy pads with different hair patterns. Simply by adding hairs, the grasp success rate was increased by at least 29%, and the number of objects that remain securely gripped during manipulation more than doubled. This result not only advances the state of the art in grasping technology, but also provides novel insight into the mechanical role of mandible hairs in ant biology.

cs.RO↗

arXiv2023

Vision-based route following by an embodied insect-inspired sparse neural network

We compared the efficiency of the FlyHash model, an insect-inspired sparse neural network (Dasgupta et al., 2017), to similar but non-sparse models in an embodied navigation task. This requires a model to control steering by comparing current visual inputs to memories stored along a training route. We concluded the FlyHash model is more efficient than others, especially in terms of data encoding.

cs.NE↗

arXiv2020

Implementing an insect brain computational circuit using III-V nanowire components in a single shared waveguide optical network

Recent developments in photonics include efficient nanoscale optoelectronic components and novel methods for sub-wavelength light manipulation. Here, we explore the potential offered by such devices as a substrate for neuromorphic computing. We propose an artificial neural network in which the weighted connectivity between nodes is achieved by emitting and receiving overlapping light signals inside a shared quasi 2D waveguide. This decreases the circuit footprint by at least an order of magnitude compared to existing optical solutions. The reception, evaluation and emission of the optical signals are performed by a neuron-like node constructed from known, highly efficient III-V nanowire optoelectronics. This minimizes power consumption of the network. To demonstrate the concept, we build a computational model based on an anatomically correct, functioning model of the central-complex navigation circuit of the insect brain. We simulate in detail the optical and electronic parts required to reproduce the connectivity of the central part of this network, using experimentally derived parameters. The results are used as input in the full model and we demonstrate that the functionality is preserved. Our approach points to a general method for drastically reducing the footprint and improving power efficiency of optoelectronic neural networks, leveraging the superior speed and energy efficiency of light as a carrier of information.

cond-mat.mes-hall↗

arXiv2019

Visual Appearance Analysis of Forest Scenes for Monocular SLAM

Monocular simultaneous localisation and mapping (SLAM) is a cheap and energy efficient way to enable Unmanned Aerial Vehicles (UAVs) to safely navigate managed forests and gather data crucial for monitoring tree health. SLAM research, however, has mostly been conducted in structured human environments, and as such is poorly adapted to unstructured forests. In this paper, we compare the performance of state of the art monocular SLAM systems on forest data and use visual appearance statistics to characterise the differences between forests and other environments, including a photorealistic simulated forest. We find that SLAM systems struggle with all but the most straightforward forest terrain and identify key attributes (lighting changes and in-scene motion) which distinguish forest scenes from "classic" urban datasets. These differences offer an insight into what makes forests harder to map and open the way for targeted improvements. We also demonstrate that even simulations that look impressive to the human eye can fail to properly reflect the difficult attributes of the environment they simulate, and provide suggestions for more closely mimicking natural scenes.

cs.CV↗

arXiv2015

Place Recognition with Event-based Cameras and a Neural Implementation of SeqSLAM

Event-based cameras offer much potential to the fields of robotics and computer vision, in part due to their large dynamic range and extremely high "frame rates". These attributes make them, at least in theory, particularly suitable for enabling tasks like navigation and mapping on high speed robotic platforms under challenging lighting conditions, a task which has been particularly challenging for traditional algorithms and camera sensors. Before these tasks become feasible however, progress must be made towards adapting and innovating current RGB-camera-based algorithms to work with event-based cameras. In this paper we present ongoing research investigating two distinct approaches to incorporating event-based cameras for robotic navigation: the investigation of suitable place recognition / loop closure techniques, and the development of efficient neural implementations of place recognition techniques that enable the possibility of place recognition using event-based cameras at very high frame rates using neuromorphic computing hardware.

cs.RO↗