🤖 AI Summary
This work addresses the challenge of simultaneously coordinating locomotion and interaction control for wheeled bipedal robots during planar object sliding manipulation. The authors propose a hierarchical control framework that integrates nonlinear model predictive control (NMPC) with a three-rigid-body dynamics model to generate reference trajectories incorporating stick-slip transitions. Notably, the approach explicitly models the hip roll degree of freedom and multi-wheel ground contact modes for the first time, enabling unified control of rolling locomotion and object manipulation. Experimental results demonstrate successful execution of two representative pedipulation tasks: retrieving a 1 kg object from under a table and scooting a 4 kg object over a distance of 0.228 meters. These results validate the effectiveness and robustness of the proposed method in complex contact-rich interactions.
📝 Abstract
In this letter, we present a hierarchical control framework that enables wheeled bipedal robots to perform planar object sliding tasks with their wheeled legs. The proposed approach formulates a nonlinear model predictive controller (NMPC) based on a reduced-order three rigid bodies (TRB) dynamical model that explicitly accounts for the hip roll degree of freedom and multiple wheel-environment contact modes, which is essential for lateral stepping and pedipulation tasks. Within this framework, the NMPC simultaneously regulates robot locomotion and interaction forces, allowing the robot to stably execute both rolling and object manipulation behaviors. A trajectory-optimization-based robot-object motion planner is developed to generate reference motions that incorporate stick-slip transitions in ground-object contact. Two representative pedipulation motions, namely scooting and lateral sliding, are validated through real-world hardware experiments, in which the robot successfully retrieves a 1 kg object from under a desk and slides a 4 kg object over a distance of 0.228 m via scooting.