Romina Adhami, Ebrahim Shahabi, Alessio Mondini, Samuel M. Youssef, Cosimo Della Santina, Gualtiero Volpe, Barbara Mazzolai
原文摘要
Robotic handling of delicate fruit benefits from compliant contact and quantitative sensing of the mechanical interaction at the end-effector. This study investigates an optoelectronic soft suction cup with a kiwifruit-specific bicomponent design for estimating interaction forces during contact with real fruit. During known-contact intervals, the optical signals accurately estimate interaction force on fruits excluded from model training: the best shallow network achieves grouped out-of-fold pooled R-squared = 0.968 +/- 0.005 (MAE = 0.138 N), with a median per-fruit held-out R-squared = 0.973 (MAE = 0.134 N). A simple monotonic mapping from the mean optical response alone achieves R-squared = 0.925 +/- 0.005, showing that substantial force-related information is already present in the optical response. Leave-one-collection-day-out validation remains strong (R-squared = 0.955 +/- 0.031), supporting robustness across acquisition sessions. As an exploratory secondary analysis, binary Soft/Firm classification from compression-test reference labels achieves moderate balanced accuracy (approximately 0.705) under grouped held-out-fruit validation, while finer three-class grading shows limited performance.
Duo Tang, Ping Deng, Sophie Armanini, Yajing Shen, Toshio Fukuda, Cosimo Della Santina, Peng Lu
原文摘要
Inspired by the coordination of global motion and local shape adaptation in snakes, elephant trunks, and octopus arms, we propose a unified planning and control framework for mobile continuum robots in cluttered and confined environments. Existing approaches often address high-level path planning and local motion control separately, limiting coordinated base-arm motion, kinematic consistency, and collision avoidance. Our framework integrates long-horizon trajectory planning with real-time geometric refinement under kinematic and environmental constraints. The global layer combines A* search with minimum-jerk planning to generate smooth reference trajectories. The local layer uses a geometry-consistent inverse kinematics solver and Riemannian optimization to coordinate mobile-base motion and continuum deformation while avoiding obstacles. We evaluate the framework through simulated pipe-traversal tasks with multiple baseline planners and real-world experiments involving navigation among dense obstacles and manipulation in confined spaces. Results demonstrate improved motion consistency, feasibility, and adaptability, supporting coordinated whole-body motion of mobile continuum robots in constrained environments.
Treating morphology and control as separate design problems has constrained the efficiency and adaptability of quadrupedal robots, while current co-design approaches are often constrained by restricted design spaces. We overcome this limit by introducing Dogger, a bio-inspired quadruped with modular three-segment legs, four-bar linkages and adjustable passive knee compliance. A two-stage framework combining reinforcement learning and Bayesian optimization is developed: the morphology-conditioned control policy is trained across designs and then held fixed during task-specific design selection. The resulting designs and policies deployed zero-shot reduce the cost of transport by 18.6% and 35.7% for inspection and payload-carrying tasks. Comparisons demonstrate that full geometry-compliance co-design outperforms fixed and restricted design scopes. Cross-task tests further reveal distinct task-specific morphologies with shared mechanical trade-offs. These results provide hardware-level evidence that expanding the physical design space alongside learned control is essential to realize the full potential of task-driven morphology-control co-design in legged robotics.