Advances in sensor technologies, object detection algorithms, planning frameworks and hardware designs have motivated the deployment of robots in warehouse automation. A variety of such applications, like order fulfillment or packing tasks, require picking objects from unstructured piles and carefully arranging them in bins or containers. Desirable solutions need to be low-cost, easily deployable and controllable, making minimalistic hardware choices desirable. The challenge in designing an effective solution to this problem relates to appropriately integrating multiple components, so as to achieve a robust pipeline that minimizes failure conditions. The current work proposes a complete pipeline for solving such packing tasks, given access only to RGB-D data and a single robot arm with a vacuum-based end-effector, which is also used as a pushing finger. To achieve the desired level of robustness, three key manipulation primitives are identified, which take advantage of the environment and simple operations to successfully pack multiple cubic objects. The overall approach is demonstrated to be robust to execution and perception errors. The impact of each manipulation primitive is evaluated by considering different versions of the proposed pipeline, which incrementally introduce reasoning about object poses and corrective manipulation actions.
Non-prehensile manipulation, such as pushing, has been shown to help grasping objects in clutter @cite_1 @cite_33 . In these works, pushing is used to reduce the uncertainty of a target object's pose. Through pushing, target objects move into graspable regions. This work follows the same principle and relies on pushing actions to counter the effects of inaccurate localization and point cloud registration. The problem is different because pushing is used for placing instead of grasping objects. The proposed method also uses pushing actions for toppling picked objects to change their orientations as well as to re-arrange misaligned objects. Other efforts have also considered pushing as a primitive in rearrangement tasks @cite_7 @cite_14 . The proposed system takes advantage of the compliance properties of the end-effector and leverages collisions with the environment to accurately place objects or topple them. A closely related approach @cite_19 performs within-hand manipulation of a grasped object by pushing it against its environment.
@cite_14 @cite_33 @cite_7 @cite_1 @cite_19
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Robotic manipulation systems suffer from two main problems in unstructured human environments: uncertainty and clutter. We introduce a planning framework addressing these two issues. The framework plans rearrangement of clutter using non-prehensile actions, such as pushing. Pushing actions are also used to manipulate object pose uncertainty. The framework uses an action library that is derived analytically from the mechanics of pushing and is provably conservative. The framework reduces the problem to one of combinatorial search, and demonstrates planning times on the order of seconds. With the extra functionality, our planner succeeds where traditional grasp planners fail, and works under high uncertainty by utilizing the funneling effect of pushing. We demonstrate our results with experiments in simulation and on HERB, a robotic platform developed at the Personal Robotics Lab at Carnegie Mellon University. We present a randomized kinodynamic planner that solves rearrangement planning problems. We embed a physics model into the planner to allow reasoning about interaction with objects in the environment. By carefully selecting this model, we are able to reduce our state and action space, gaining tractability in the search. The result is a planner capable of generating trajectories for full arm manipulation and simultaneous object interaction. We demonstrate the ability to solve more rearrangement by pushing tasks than existing primitive based solutions. Finally, we show the plans we generate are feasible for execution on a real robot. Humans use a remarkable set of strategies to manipulate objects in clutter. We pick up, push, slide, and sweep with our hands and arms to rearrange clutter surrounding our primary task. But our robots treat the world like the Tower of Hanoi — moving with pick-and-place actions and fearful to interact with it with anything but rigid grasps. This produces inefficient plans and is often inapplicable with heavy, large, or otherwise ungraspable objects. We introduce a framework for planning in clutter that uses a library of actions inspired by human strategies. The action library is derived analytically from the mechanics of pushing and is provably conservative. The framework reduces the problem to one of combinatorial search, and demonstrates planning times on the order of seconds. With the extra functionality, our planner succeeds where traditional grasp planners fail, and works under high uncertainty by utilizing the funneling effect of pushing. We demonstrate our results with experiments in simulation and on HERB, a robotic platform developed at the Personal Robotics Lab at Carnegie Mellon University. This paper explores the manipulation of a grasped object by pushing it against its environment. Relying on precise arm motions and detailed models of frictional contact, prehensile pushing enables dexterous manipulation with simple manipulators, such as those currently available in industrial settings, and those likely affordable by service and field robots.