Mechanics and Planning of Manipulator Pushing Operations

By Matthew Thomas Mason. 1986

Pushing is an essential component of many manipulator operations. This paper presents a theoretical exploration of the mechanics of pushing and demonstrates application of the theory to analysis and synthesis of robotic manipulator oper ations.

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Citations in the corpus

Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, neutral): “based [1], [2], [3] or data-driven [4], [5], [6] approaches.”
Deep visual foresight for planning robot motion (2016, methodology): “This approach has been applied to a range of problems, including robotic pushing [7], [8], [9].”
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours (2015, methodology): “How do we predict grasp locations for this object? One approach is to fit 3D models to these objects, or to use a 3D depth sensor, and perform analytical 3D reasoning to predict the grasp locations [1]–[4].”
More than a million ways to be pushed. A high-fidelity experimental dataset of planar pushing (2016, background): “Condition Work examples Uniform friction* [all] Known pressure distribution [16, 24, 23, 6] Known center of friction [1] (the centroid of pressure distribution) Coulomb friction* [1, 24, 10, 3, 26, 6] Maximum power inequality* [all] (generalized Coulomb friction) Quasistatic interaction [24, 1, 3, 6”
A Planning Framework for Non-Prehensile Manipulation under Clutter and Uncertainty (2012, unknown): “The voting theorem (Mason 1986) states that vp and the edges of the friction cone votes on the direction the object will rotate.”
Feedback Control of the Pusher-Slider System: A Story of Hybrid and Underactuated Contact Dynamics (2016, methodology): “ pusher willstick tothe slider, else, it will slide. Depending on the direction of motion of the pusher, different contact interaction modes can arise between the pusherand the slider. The motioncone [14], shown in Fig. 4, is useful to determine if a given velocity of the pusher will result in stick”
A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems (2004, methodology): “Mason (1986) had earlier applied a similar control-theoretic model to box-pushing with dexterous manipulators.”
A probabilistic data-driven model for planar pushing (2017, methodology): “The approach, validation, and structure of this paper, are as follows: · Problem: We are interested in the statistical mechanics of the planar pusher-slider system [3, 4, 5, 6] where a frictional point contact pushes on a planar object sliding on a frictional surface.”
Affordance Prediction via Learned Object Attributes (2011, background): “Similarly, much work on object pushing could be used to help guide the manipulation of objects, once it is known to be pushable [16, 17].”
Learning Visual Affordances for Robotic Manipulation (2019, background): “The literature in this area is vast, emerging early from classical solutions that explicitly model the dynamics of pushing with frictional forces [70, 35].”
Policy transfer via modularity and reward guiding (2017, background): “Additionally, if a robot needs to grasp an object, it may need to ﬁrst push the object to a more ideal position or orientation before grasping [3], [4], [5].”
Planning algorithms (2006, unknown): “Compliant motions are also considered in [142, 285, 489, 681, 683, 779].”
A data-driven statistical framework for post-grasp manipulation (2014, background): “The 1980s and early 1990s saw several projects exploring grasping and manipulation under uncertainty, using both possibilistic and probabilistic models (Brost, 1988; Goldberg, 1990; Goldberg and Mason, 1990; Brost and Christiansen, 1996; Christiansen, 1992; Christiansen and Goldberg, 1995; Mason, 19”
On dexterity and dexterous manipulation (2011, background): “For the purposes of grasp acquisition, the use of compliant and adaptive fingers [31] and appropriate control strategies such as push-grasping [32] can circumvent strict sensor requirements by adapting to uncertainty rather than trying to eliminate it.”
Push planning for object placement on cluttered table surfaces (2011, methodology): “Our research is complementary to perception of surfaces and objects [1–3] and control of robot arms both for object placement and non-prehensile manipulation [4–10].”
Policy transfer via modularity (2017, background): “Additionally, if a robot needs to grasp an object, it may need to first push the object to a more ideal position or orientation before grasping [3], [4], [5].”
A Vision-Based Learning Method for Pushing Manipulation (1993, unknown): “The pushing and sliding manipulation problem has been studied extensively by Mason [Mason, 1986] from an analytical viewpoint, as well as from a learning perspective [Mason et hi., 1989].”