Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

Balancing the demands of two tasks: an investigation of cognitive-motor dual-tasking in relapsing remitting multiple sclerosis

Background: People with relapsing remitting MS (PwRRMS) suffer disproportionate decrements in gait under dual-task conditions, when walking and a cognitive task are combined. There has been much less investigation of the impact of cognitive demands on balance. This study investigated whether: (1) PwRRMS show disproportionate decrements in postural stability under dual-task conditions compared to healthy controls; (2) dual-task decrements are associated with everyday dual-tasking difficulties. In addition, the impact of mood, fatigue and disease severity on dual-tasking were also examined. Methods: 34 PwRRMS and 34 matched controls completed cognitive (digit span) and balance (movement of centre of pressure on a Biosway, on stable and unstable surfaces) tasks under single and dual-task conditions. Everyday dual-tasking was measured using the DTQ. Mood was measured by the HADS. Fatigue was measured via the MFIS. Results: No differences in age, gender, years of education, estimated pre-morbid IQ or baseline digit span between the groups. Compared to healthy controls, PwRRMS showed a significantly greater decrement in postural stability under dual-task conditions on an unstable surface (p=0.007), but not a stable surface (p=0.679). PwRRMS reported higher levels of everyday dual-tasking difficulties (p<0.001). Balance decrement scores were not correlated with everyday dual-tasking difficulties, or with fatigue. Stable surface balance decrement scores were significantly associated with levels of anxiety (rho=0.527, p=0.001) and depression (rho=0.451, p=0.007). Conclusion: RRMS causes difficulties with dual-tasking, impacting balance, particularly under challenging conditions, which may contribute to an increased risk of gait difficulties and falls. The striking relationship between anxiety/depression and dual-task decrement suggests that worry may be contributing to dual-task difficulties.