We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.

We discuss the process of building semantic maps, how to interactively label entities in them, and how to use them to enable context-aware navigation behaviors in human environments. We utilize planar surfaces, such as walls and tables, and static objects, such as door signs, as features for our semantic mapping approach. Users can interactively annotate these features by having the robot follow him/her, entering the label through a mobile app, and performing a pointing gesture toward the landmark of interest. Our gesture-based approach can reliably estimate which object is being pointed at, and detect ambiguous gestures with probabilistic modeling. Our person following method attempts to maximize future utility by search for future actions assuming constant velocity model for the human. We describe a method to extract metric goals from a semantic map landmark and to plan a human aware path that takes into account the personal spaces of people. Finally, we demonstrate context awareness for person following in two scenarios: interactive labeling and door passing.We believe that future navigation approaches and service robotics applications can be made more effective by further exploiting the structure of human environments.

Reliable bipedal walking over complex terrain is a challenging problem, using a curriculum can help learning. Curriculum learning is the idea of starting with an achievable version of a task and increasing the difficulty as a success criteria is met. We propose a 3-stage curriculum to train Deep Reinforcement Learning policies for bipedal walking over various challenging terrains. In the first stage, the agent starts on an easy terrain and the terrain difficulty is gradually increased, while forces derived from a target policy are applied to the robot joints and the base. In the second stage, the guiding forces are gradually reduced to zero. Finally, in the third stage, random perturbations with increasing magnitude are applied to the robot base, so the robustness of the policies are improved. In simulation experiments, we show that our approach is effective in learning walking policies, separate from each other, for five terrain types: flat, hurdles, gaps, stairs, and steps. Moreover, we demonstrate that in the absence of human demonstrations, a simple hand designed walking trajectory is a sufficient prior to learn to traverse complex terrain types. In ablation studies, we show that taking out any one of the three stages of the curriculum degrades the learning performance.

Remote teleoperation interfaces for precision manipulation should support operator awareness and usability while preserving human control, consistent with the human-centred goals of Industry 5.0. This study conducted a within-subject user evaluation with 18 participants using a UR5 arm. Three interface conditions were compared: an enhanced first-person interface with visual overlays (FPV+), a multi-view interface combining first-person and third-person views (PiP), and a multi-view interface with enhanced visual overlays (PiP+). Participants completed a ball task and a pen insertion task in pseudo-randomised order. Binary task success was analysed using generalised linear mixed-effects models, while System Usability Scale scores were analysed using a linear mixed-effects model. Neither adding a third-person view to the enhanced first-person interface nor adding the minimap and tactile-dot overlays to the multi-view interface was significantly associated with task success. A post hoc sensitivity analysis showed that these binary-success comparisons were sensitive only to large effects, so they are interpreted as inconclusive rather than as evidence of absence. Success was significantly lower for the pen task, and its relationship with prior human–robot interaction experience differed from that observed for the ball task. Adding a third-person view was associated with an approximately 16-point higher System Usability Scale score, whereas the minimap and tactile-dot overlays were not significantly associated with usability. These findings illustrate a human-centred evaluation principle relevant to Industry 5.0: an interface feature can improve the operator’s experience of a teleoperation system even when no corresponding change in objective task success is detectable, revealing a benefit that a performance-only evaluation might overlook.

We discuss the process of building semantic maps, how to interactively label entities in them, and how to use them to enable context-aware navigation behaviors in human environments. We utilize planar surfaces, such as walls and tables, and static objects, such as door signs, as features for our semantic mapping approach. Users can interactively annotate these features by having the robot follow him/her, entering the label through a mobile app, and performing a pointing gesture toward the landmark of interest. Our gesture based approach can reliably estimate which object is being pointed at and detect ambiguous gestures with probabilistic modeling. Our person following method attempts to maximize future utility by a search for future actions assuming constant velocity model for the human. We describe a method to extract metric goals from a semantic map landmark and to plan a human aware path that takes into account the personal spaces of people. Finally, we demonstrate context-awareness for person following in two scenarios: interactive labeling and door passing. We believe that future navigation approaches and service robotics applications can be made more effective by further exploiting the structure of human environments.