Non-prehensile manipulation offers versatile skills for moving and rearranging heavy or bulky objects, particularly when combined with a mobile manipulation platform. However, both model-based and model-free approaches struggle with the complex hybrid dynamics and the sparsity of the contact in these tasks. To address these challenges, we propose a contact-guided exploration strategy implemented within a Multi-Critic Reinforcement Learning (RL) framework. A dedicated exploration critic is trained with a dense contact-seeking reward that guides the end-effector toward meaningful contact points; its influence is progressively decayed to recover a task-optimal policy. We obtain candidate interaction points from a general-purpose grasping algorithm, enabling the exploration mechanism to generalise across various object geometries. We evaluate the approach on multiple tasks, including box pushing, chair transportation, and a dishwasher opening task. Finally, we validate the chair transportation policy through extensive experiments on a quadrupedal mobile manipulator, demonstrating deployable non-prehensile manipulation in the real world.
Simone Tolomei, Mayank Mittal, F. Angelini et al.· 0 citations
Aerial robots equipped with cables used as soft end-effectors represent a new, versatile robotic paradigm to solve multiple tasks. However, to fully exploit the versatility of soft robots, the modeling problem must be addressed. The main challenges depend on the hard-to-model continuum dynamics. To solve this challenge, this work proposes multiple physics-informed neural network architectures to model a cable’s dynamics using Cartesian measurements. We apply a classic Lagrangian Neural Network architecture and propose a new Lagrangian-informed Transformer-based architecture, and establish local contraction bounds for their training dynamics. Furthermore, both networks address the underactuation of the system, highlight its mechanical nature, and return the cable positions, velocities, and accelerations in Cartesian space, enforcing energy conservation by exploiting the Lagrangian structure. Finally, we test the modeling performance via experiments with varying trajectories and models, comparing the proposed architectures with state-of-the-art networks and reduced-order model baselines.
M. Pierallini, Yaolei Shen, Y. De Santis et al.· IEEE Robotics and Automation...· 0 citations
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