Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower

Abstract

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerodynamic forces, a blowing controller must (i) continually adapt to unexpected changes from its actions, (ii) maintain finegrained control, since the slightest misstep can result in large unintended consequences (e.g., scatter objects already in a pile), and (iii) infer long-range plans (e.g., move the robot to strategic blowing locations). We tackle these challenges in the context of deep reinforcement learning, introducing a multi-frequency version of the spatial action maps framework. This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control for dynamic mobile manipulation. Experiments show that our system learns efficient behaviors for the task, demonstrating in particular that blowing achieves better downstream performance than pushing, and that our policies improve performance over baselines. Moreover, we show that our system naturally encourages emergent specialization between the different subpolicies spanning low-level fine-grained control and high-level planning. On a real mobile robot equipped with a miniature air blower, we show that our simulation-trained policies transfer well to a real environment and can generalize to novel objects.

DextAIRity: Deformable Manipulation Can be a Breeze

“…The idea of using airflow to generate directed force has been adopted in many aspects of robotic mechanical design. For example, airflow has been used in robot propulsion systems to produce thrust [40], as a pneumatic actuator to control a soft robot's geometry and articulation [30,31], or as a nonprehensile manipulator to move scattered objects [44]. Air manipulation is also used in many robot gripper designs, such as suction grippers [52,48,2] and non-contact grippers, using the Bernoulli principle [7,26,27,6].…”