In-hand object manipulation is an important capability for dexterous manipulation. In this paper, we introduce a modeling and planning framework for in-hand object reconfiguration, focusing on frictional patch contacts between the robot's palms (or fingers) and the object. Our approach leverages two cooperative patch contacts on either side of the object to iteratively reposition it within the robot's grasp by alternating between sliding and sticking motions. Unlike previous methods that rely on single-point contacts or restrictive assumptions on contact dynamics, our framework models the complex interaction of dual frictional patches, allowing for greater control over object motion. We develop a planning algorithm that computes feasible motions to reorient and re-grasp objects without causing unintended slippage. We demonstrate the effectiveness of our approach in simulation and real-world experiments, showing significant improvements in object stability and pose accuracy across various object geometries.

This paper develops a scheduling protocol for a team of autonomous robots that operate in long-term persistent tasks. The proposed framework, called meSch, accounts for the robots' limited battery capacity and the presence of a single charging station, and achieves the following contributions: 1) First, it guarantees exclusive use of the charging station by one robot at a time; the approach is online, applicable to general nonlinear robot models, does not require robots to be deployed at different times, and can handle robots with different discharge rates. 2) Second, we consider the scenario when the charging station is mobile and subject to uncertainty. This approach ensures that the robots can rendezvous with the charging station while considering the uncertainty in its position. Finally, we provide the evaluation of the efficacy of meSch in simulation and experimental case studies.