Yangmengfei Xu

Social alienation challenges community cohesion, particularly in diverse societies like Australia. This study introduces the Neighbor Koala, a community-sharing robot designed to foster social connections and promote sustainability. By facilitating item exchanges and capturing shared memories, it aims to address isolation while reducing waste. Leveraging the sociological concept of the “gift”, it strengthens emotional bonds and community belonging. This work provides a cornerstone for future studies on how robotics can enhance social well-being and environmental responsibility in urban contexts.

Recent studies have shown that motor adaptation is an optimisation process on both kinematic error and effort. This work aims to induce a motor adaption in an experimental setup solely relying on the effort without any explicit kinematic error. In this experiment, the intervention space and adaptation space are decoupled: while the force field only applies to the hand linear velocity, the adaptation is expected to happen in the arm joint null space (i.e. the swivel angle). The primary hypothesis is that such an effort-based force field can induce a movement pattern change in an indirect manner. Secondarily, assuming that this adaptation may be further promoted through subtle prompts to explore the cost space, a variation of the approach with a progressive goal is also tested. Twenty naive subjects were allocated into two groups with slightly different implementations of the force field: one with a Constant Goal (CG) and another one with a Progressively changing Goal (PG). Subjects were asked to perform reaching tasks while attached to a 3D manipulandum. During the intervention, the device applied a resistive viscous force at the subject’s hand as a function of the subject’s swivel angle to encourage an increase of the latter. Significant increases of the swivel angle of 4.9° and 6.3° were observed for the CG and the PG groups respectively. This result confirms the feasibility of inducing motor adaptation in the redundant joint space by providing a task space intervention without explicit error feedback.

The resolution of contact location is important in many applications in robotics and automation. This is generally done by using an array of contact or tactile receptors, which increases cost and complexity as the required resolution or area is increased. Tactile sensors have also been developed using a continuous deformable medium between the contact and the receptors, which allows few receptors to interpolate the information among them, avoiding the weakness highlighted in the former approach. The latter is generally used to measure contact force intensity or magnitude but rarely used to identify the contact locations. This paper presents a systematic design and characterisation procedure for magnetic-based soft tactile sensors (utilizing the latter approach with the deformable contact medium) with the goal of locating the contact force location. This systematic procedure provides conditions under which design parameters can be selected, supported by a selected machine learning algorithm, to achieve the desired performance of the tactile sensor in identifying the contact location. An illustrative example, which combines a particular sensor configuration (magnetic hall effect sensor as the receptor, a selected continuous medium and a selected sensing resolution) and a specific data-driven algorithm, is used to illustrate the proposed design procedure. The results of the illustrative example design demonstrates the efficacy of the proposed design procedure and the proposed sensing strategy in identifying a contact location. The resulting sensor is also tested on a robotic hand (Allegro Hand, SimLab Co) to demonstrate its application in real-world scenarios.