Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. In this work, we demonstrate that it is possible to discover and learn these synergies from scratch through model-free deep reinforcement learning. Our method involves training two fully convolutional networks that map from visual observations to actions: one infers the utility of pushes for a dense pixel-wise sampling of end effector orientations and locations, while the other does the same for grasping. Both networks are trained jointly in a Q-learning framework and are entirely self-supervised by trial and error, where rewards are provided from successful grasps. In this way, our policy learns pushing motions that enable future grasps, while learning grasps that can leverage past pushes. During picking experiments in both simulation and real-world scenarios, we find that our system quickly learns complex behaviors amid challenging cases of clutter, and achieves better grasping success rates and picking efficiencies than baseline alternatives after only a few hours of training. We further demonstrate that our method is capable of generalizing to novel objects. Qualitative results (videos), code, pre-trained models, and simulation environments are available at this http URL
Non-prehensile manipulation. Planning non-prehensile motions, such as pushing, is a fundamental problem that dates back to the early days of robotic manipulation. The literature in this area is vast, emerging early from classical solutions that explicitly model the dynamics of pushing with frictional forces @cite_14 @cite_35 . While inspiring, many of these methods rely on modeling assumptions that do not hold in practice @cite_1 @cite_37 . For example, non-uniform friction distributions across object surfaces and the variability of friction are only some of the factors that can lead to erroneous predictions of friction-modeling pushing solutions in real-world settings. While recent methods have explored data-driven algorithms for learning the dynamics of pushing @cite_5 @cite_17 @cite_20 , many of these works have largely focused on the execution of stable pushes for one object at a time. Modeling the larger-scale consequences of pushing in the face of severe clutter and friction variation continues to be a complex problem; effectively using these models to discover optimal policies in real settings -- even more so.
@cite_35 @cite_37 @cite_14 @cite_1 @cite_5 @cite_20 @cite_17
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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. When possible, non-prehensile transportation (i.e. transporting objects without grasping them) can be faster and more efficient than prehensile transportation. However, the need to explicitly consider reaction and friction forces yields kino-dynamic constraints that are difficult to take into account by traditional planning algorithms. Based on the recently developed Admissible Velocity Propagation algorithm, we propose here a fast and general non-prehensile transportation scheme. Our contribution is twofold. First we show how to cast the dynamic balance constraints of a 3D object (e.g. a bottle) into a form compatible with the AVP algorithm. Second, we extend the AVP-RRT algorithm into a more efficient AVP-biRRT algorithm, which makes use of the idea of concurrently growing two trees, one rooted at the starting configuration and one rooted at the goal configuration. We also show both in simulations and on a real robot how our algorithm allows planning fast and dynamic trajectories for the non-prehensile transportation of a bottle. This paper investigates real-time control strategies for dynamical systems that involve frictional contact interactions. Hybridness and underactuation are key characteristics of these systems that complicate the design of feedback controllers. In this research, we examine and test a novel feedback controller design on a planar pushing system, where the purpose is to control the motion of a sliding object on a flat surface using a point robotic pusher. The pusher-slider is a simple dynamical system that retains many of the challenges that are typical of robotic manipulation tasks. Our results show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of the contact. In order to achieve real-time control, simplifications are proposed to speed up the integer program. The concept of Family of Modes (FOM) is introduced to solve an online convex optimization problem by selecting a set of contact mode schedules that spans a large set of dynamic behaviors that can occur during the prediction horizon. The controller design is applied to stabilize the motion of a sliding object about a nominal trajectory, and to re-plan its trajectory in real-time to follow a moving target. We validate the controller design through numerical simulations and experimental results on an industrial ABB IRB 120 robotic arm. We would like to give robots the ability to position and orient parts in the plane by pushing, particularly when the parts are too large or heavy to be grasped and lifted. Unfortunately, the motion of a pushed object is generally unpredictable due to unknown support friction forces. With multiple pushing contact points, however, it is possible to find pushing directions that cause the object to remain fixed to the manipulator. These are called stable pushing directions. In this article we consider the problem of planning pushing paths using stable pushes. Pushing imposes a set of nonholonomic velocity constraints on the motion of the object, and we study the issues of local and global controllability during pushing with point contact or stable line contact. We describe a planner for finding stable pushing paths among obstacles, and the planner is demon strated on several manipulation tasks.