Using manipulation primitives for brick sorting in clutter

Gupta, Megha; Sukhatme, Gaurav S.

doi:10.1109/icra.2012.6224787

Cited by 57 publications

(50 citation statements)

References 18 publications

(17 reference statements)

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“…Pushing enables robots to perform a wide variety of tasks that are not possible through pick-and-place manipulation: pushing can move objects that are too large or heavy to be grasped [6], is effective at manipulating objects under uncertainty [7,8], and can be used as pre-grasp manipulation to bring objects to configurations where they can be easily grasped [9,10]. Additionally, pushing has been used to simultaneously move multiple objects [11] and singulate an object from a pile [12,13]. Since pushing offers such a dramatic expansion of manipulation skills, there have been extensive research on the fundamental mechanics of pushing [14][15][16][17] and on the planning of pushing operations [17,18].…”

Section: Related Workmentioning

confidence: 99%

Pose estimation for contact manipulation with manifold particle filters

Koval

Doğar

Pollard

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We investigate the problem of estimating the state of an object during manipulation. Contact sensors provide valuable information about the object state during actions which involve persistent contact, e.g. pushing. However, contact sensing is very discriminative by nature, and therefore the set of object states which contact a sensor constitutes a lowerdimensional manifold in the state space of the object. This causes stochastic state estimation methods such as particle filters to perform poorly when contact sensors are used. We propose a new algorithm, the manifold particle filter, which uses dual particles directly sampled from the contact manifold to avoid this problem. The algorithm adapts to the probability of contact by dynamically changing the number of dual particles sampled from the manifold. We compare our algorithm to the particle filter through extensive experiments and we show that our algorithm is both faster and better at estimating the state. Our algorithm's performance improves with increasing sensor accuracy and the filter's update rate. We implement the algorithm on a real robot using a force/torque sensor and strain gauges to track the pose of a pushed object.

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Section: Related Workmentioning

confidence: 99%

Pose estimation for contact manipulation with manifold particle filters

Koval

Doğar

Pollard

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…Because the dense packing of objects in a pile poses considerable challenges for perception and grasping, Chang et al [20] proposed pushing strategies for the interactive singulation of objects. Gupta and Sukhatme [21] estimate how cluttered an area is and employ motion primitives to separate piled Lego bricks.…”

Section: Related Workmentioning

confidence: 99%

Active Recognition and Manipulation for Mobile Robot Bin Picking

Holz

Nieuwenhuisen

Droeschel

et al. 2014

Springer Tracts in Advanced Robotics

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Abstract. Grasping individual objects from an unordered pile in a box has been investigated in stationary scenarios so far. In this work, we present a complete system including active object perception and grasp planning for bin picking with a mobile robot. At the core of our approach is an efficient representation of objects as compounds of simple shape and contour primitives. This representation is used for both robust object perception and efficient grasp planning. For being able to manipulate previously unknown objects, we learn object models from single scans in an offline phase. During operation, objects are detected in the scene using a particularly robust probabilistic graph matching. To cope with severe occlusions we employ active perception considering not only previously unseen volume but also outcomes of primitive and object detection. The combination of shape and contour primitives makes our object perception approach particularly robust even in the presence of noise, occlusions, and missing information. For grasp planning, we efficiently pre-compute possible grasps directly on the learned object models. During operation, grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.

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“…More recently, there has been some work on interactive perception where the robot actively manipulates the world to complete the overall task at hand. Fitzpatrick [11], Chang [12], and Schiebener et al [13] use interaction with objects for segmentation and recognition of unfamiliar objects; Katz and Brock [14] employ it to obtain a kinematic model of an unknown object and use it for purposeful manipulation; Gupta and Sukhatme [15] use deliberate interactions with objects to sort them from clutter; and Dogar and Srinivasa [16] use it for more effective grasping in clutter.…”

Section: Related Workmentioning

confidence: 99%

Interactive environment exploration in clutter

Gupta

Rühr

Beetz

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Robotic environment exploration in cluttered environments is a challenging problem. The number and variety of objects present not only make perception very difficult but also introduce many constraints for robot navigation and manipulation. In this paper, we investigate the idea of exploring a small, bounded environment (e.g., the shelf of a home refrigerator) by prehensile and non-prehensile manipulation of the objects it contains. The presence of multiple objects results in partial and occluded views of the scene. This inherent uncertainty in the scene's state forces the robot to adopt an observe-planact strategy and interleave planning with execution. Objects occupying the space and potentially occluding other hidden objects are rearranged to reveal more of the unseen area. The environment is considered explored when the state (free or occupied) of every voxel in the volume is known. The presented algorithm can be easily adapted to real world problems like object search, taking inventory, and mapping. We evaluate our planner in simulation using various metrics like planning time, number of actions required, and length of planning horizon. We then present an implementation on the PR2 robot and use it for object search in clutter.

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Using manipulation primitives for brick sorting in clutter

Cited by 57 publications

References 18 publications

Pose estimation for contact manipulation with manifold particle filters

Pose estimation for contact manipulation with manifold particle filters

Active Recognition and Manipulation for Mobile Robot Bin Picking

Interactive environment exploration in clutter

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