Collision-free state estimation

Wong, Lawson L. S.; Kaelbling, Leslie Pack; Lozano-Pérez, Tomás

doi:10.1109/icra.2012.6225309

Cited by 6 publications

(5 citation statements)

References 13 publications

(13 reference statements)

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“…An additional concern during estimation is the incorporation of physical constraints: objects may not interpenetrate one another and must be supported by other objects (not be floating in the air). We have integrated the approach of Wong et al (2012), solving a constrained optimization problem to find the maximum-likelihood arrangement of the objects that satisfies these constraints, and use that constrained maximum-likelihood estimate for motion planning.…”

Section: Object Posesmentioning

confidence: 99%

Integrated task and motion planning in belief space

Kaelbling¹,

Lozano-Pérez²

2013

The International Journal of Robotics Research

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289

223

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We describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains based on planning in the belief space of probability distributions over states using hierarchical goal regression (pre-image back-chaining). We develop a vocabulary of logical expressions that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators can give rise to task-oriented perception in support of the manipulation goals. An implementation of this method is demonstrated in simulation and on a real PR2 robot, showing robust, flexible solution of mobile manipulation problems with multiple objects and substantial uncertainty.

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Section: Object Posesmentioning

confidence: 99%

Integrated task and motion planning in belief space

Kaelbling¹,

Lozano-Pérez²

2013

The International Journal of Robotics Research

Self Cite

289

223

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“…In a closely related work, Wong et. al [13] propose collision-free state estimation where they attempt to solve a constrained optimization problem in order to find a feasible collision-free configuration. They assume that all the objects are resting stably on a 2D surface (i.e., no object is on top of another object).…”

Section: Related Workmentioning

confidence: 99%

A principle of minimum translation search approach for object pose refinement

Mojtahedzadeh

Lilienthal

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The state-of-the-art object pose estimation approaches represent the set of detected poses together with corresponding uncertainty. The inaccurate noisy poses may result in a configuration of overlapping objects especially in cluttered environments. Under a rigid body assumption the inter-penetrations between pairs of objects are geometrically inconsistent. In this paper, we propose the principle of minimum translation search, PROMTS, to find an inter-penetrationfree configuration of the initially detected objects. The target application is to automate the task of unloading shipping containers, where a geometrically consistent configuration of objects is required for high level reasoning and manipulation. We find that the proposed approach to resolve geometrical inconsistencies improves the overall pose estimation accuracy. We examine the utility of two selected search methods: A-star and Depth-Limited search. The performance of the search algorithms are tested on data sets generated in simulation and from real-world scenarios. The results show overall improvement of the estimated poses and suggest that depth-limited search presents the best overall performance.

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“…The second extension is the incorporation of known state constraints. Examples of constraints include object-object non-interpenetration (Wong, Kaelbling, and Lozano-Pérez 2012), support/containment relationships, and stability/contact. State estimation with hard constraints is challenging because they couple together many state variables.…”

Section: Expressivenessmentioning

confidence: 99%

Living and Searching in the World: Object-Based State Estimation for Mobile Robots

Wong

2014

AAAI

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Mobile-manipulation robots performing service tasks in human-centric indoor environments has long been a dream for developers of autonomous agents. Tasks such as cooking and cleaning require interaction with the environment, hence robots need to know relevant aspects of their spatial surroundings. However, unlike the structured settings that industrial robots operate in, service robots typically have little prior information about their environment. Even if this information was given, due to the involvement of many other agents (e.g., humans moving objects), uncertainty in the complete state of the world is inevitable over time. Additionally, most information about the world is irrelevant to any particular task at hand. Mobile manipulation robots therefore need to continuously perform the task of state estimation, using perceptual information to maintain the state, and its uncertainty, of task-relevant aspects of the world. Because indoor tasks frequently require the use of objects, objects should be given critical emphasis in spatial representations for service robots. Compared to occupancy grids and feature-based maps often used in navigation and SLAM, object-based representations are arguably still in their infancy. In my thesis, I propose a representation framework based on objects, their 'semantic' attributes, and their geometric realizations in the physical world.

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Collision-free state estimation

Cited by 6 publications

References 13 publications

Integrated task and motion planning in belief space

Integrated task and motion planning in belief space

A principle of minimum translation search approach for object pose refinement

Living and Searching in the World: Object-Based State Estimation for Mobile Robots

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