Object placement as inverse motion planning

Holladay, Anne; Barry, Jennifer; Kaelbling, Leslie Pack; Lozano-Pérez, Tomás

doi:10.1109/icra.2013.6631099

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…The choice of a good grasp is essential for the success of automated robot grasping and manipulation [16,17]. Considering task requirements may result in a less optimal grasp in terms of stability but it may increase the ability to manipulate the object as required by the task [18,19].…”

Section: Related Workmentioning

confidence: 99%

Generating Task-specific Robotic Grasps

Sridharan¹

2022

Preprint

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This paper describes a method for generating robot grasps by jointly considering stability and other task and object-specific constraints. We introduce a three-level representation that is acquired for each object class from a small number of exemplars of objects, tasks, and relevant grasps. The representation encodes task-specific knowledge for each object class as a relationship between a keypoint skeleton and suitable grasp points that is preserved despite intra-class variations in scale and orientation. The learned models are queried at run time by a simple sampling-based method to guide the generation of grasps that balance task and stability constraints. We ground and evaluate our method in the context of a Franka Emika Panda robot assisting a human in picking tabletop objects for which the robot does not have prior CAD models. Experimental results demonstrate that in comparison with a baseline method that only focuses on stability, our method is able to provide suitable grasps for different tasks.

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Section: Related Workmentioning

confidence: 99%

Generating Task-specific Robotic Grasps

Sridharan¹

2022

Preprint

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“…b) Grasping and Stacking: Current robot systems are increasingly proficient at robustly grasping objects [8]- [10] and subsequently dropping, placing, and balancing them [11], [12]. The outcomes of these actions vary based on the dexterity of the robot manipulator [13]. Longer-horizon tasks, like stacking multiple objects, require awareness of intermediate action success.…”

Section: Related Workmentioning

confidence: 99%

Improving Robot Success Detection using Static Object Data

Scalise

Thomason

Bisk

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We use static object data to improve success detection for stacking objects on and nesting objects in one another. Such actions are necessary for certain robotics tasks, e.g., clearing a dining table or packing a warehouse bin. However, using an RGB-D camera to detect success can be insufficient: same-colored objects can be difficult to differentiate, and reflective silverware cause noisy depth camera perception. We show that adding static data about the objects themselves improves the performance of an end-to-end pipeline for classifying action outcomes. Images of the objects, and language expressions describing them, encode prior geometry, shape, and size information that refine classification accuracy. We collect over 13 hours of egocentric manipulation data for training a model to reason about whether a robot successfully placed unseen objects in or on one another. The model achieves up to a 57% absolute gain over the task baseline on pairs of previously unseen objects.

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“…Kristek and Shell [57] extended the sensorless, non-prehensile manipulation to deformable polygonal parts. Other recent examples of sensorless manipulation include [58], [59], and [60].…”

Section: Sensorless Manipulation To Reduce Uncertainty In Positioningmentioning

confidence: 99%

Addressing perception uncertainty induced failure modes in robotic bin-picking

Kaipa

Kankanhalli-Nagendra

Kumbla

et al. 2016

Robotics and Computer-Integrated Manufacturing

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We present a comprehensive approach to handle perception uncertainty to reduce failure rates in robotic bin-picking. Our focus is on mixed-bins. We identify the main failure modes at various stages of the bin-picking task and present methods to recover from them. If uncertainty in part detection leads to perception failure, then human intervention is invoked. Our approach estimates the confidence in the part match provided by an automated perception system, which is used to detect perception failures. Human intervention is also invoked if uncertainty in estimated part location and orientation leads to a singulation planning failure. We have developed a user interface that enables remote human interventions when necessary. Finally, if uncertainty in part posture in the gripper leads to failure in placing the part with the desired accuracy, sensor-less fine-positioning moves are used to correct the final placement errors. We have developed a fine-positioning planner with a suite of fine-motion strategies that offer different tradeoffs between completion time and postural accuracy at the destination. We report our observations from system characterization experiments with a dual-armed Baxter robot, equipped with a Ensenso three-dimensional camera, to perform bin-picking on mixed-bins.

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Object placement as inverse motion planning

Cited by 11 publications

References 11 publications

Generating Task-specific Robotic Grasps

Generating Task-specific Robotic Grasps

Improving Robot Success Detection using Static Object Data

Addressing perception uncertainty induced failure modes in robotic bin-picking

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