Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well. Our work first explains this high performance -- we note a commonly used metric, reduction of Shannon entropy, is submodular under certain assumptions, rendering the greedy solution comparable to the optimal plan in the offline setting. However, reacting online to observations can increase performance. Recently developed notions of adaptive submodularity provide guarantees for a greedy algorithm in this online setting. In this work, we develop new methods based on adaptive submodularity for selecting a sequence of information gathering actions online. In addition to providing guarantees, we can capitalize on submodularity to attain additional computational speedups. We demonstrate the effectiveness of these methods in simulation and on a robot.
Dogar and Srinivasa @cite_26 use the natural interaction of an end effector and an object to handle uncertainty with a push-grasp. By utilizing offline simulation, they reduce the online problem to enclosing the object's uncertainty in a pre-computed capture region. Online, they simply plan a push-grasp which encloses the uncertainty inside the capture region. This work is complimentary to ours - the push-grasp works well on objects which slide easily, while we assume objects do not move. We believe each approach is applicable in different scenarios.
@cite_26
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We add to a manipulator's capabilities a new primitive motion which we term a push-grasp. While significant progress has been made in robotic grasping of objects and geometric path planning for manipulation, such work treats the world and the object being grasped as immovable, often declaring failure when simple motions of the object could produce success. We analyze the mechanics of push-grasping and present a quasi-static tool that can be used both for analysis and simulation. We utilize this analysis to derive a fast, feasible motion planning algorithm that produces stable pushgrasp plans for dexterous hands in the presence of object pose uncertainty and high clutter. We demonstrate our algorithm extensively in simulation and on HERB, a personal robotics platform developed at Intel Labs Pittsburgh.