Probabilistic Active Filtering for Object Search in Clutter

Poon, James; Cui, Yunduan; Ooga, Jun'ichiro; Ogawa, Akihito; Matsubara, Takamitsu

doi:10.1109/icra.2019.8794418

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…Prior efforts in object retrieval under clutter have focused on identifying which objects to relocate so as to enable a collision-free path to reach the target object [24], [25]. Uncertainty arising from occlusion or sensor noise complicate this problem and efforts aim to minimize their effects by inferring object shape [26], [27], reasoning about pose uncertainty [28], or probabilistic filtering [29]. To improve the success rate of such tasks, human interaction has been considered to supply a high-level plan, which informs an ordered sequence of objects and approximated goal positions [3].…”

Section: Related Workmentioning

confidence: 99%

Persistent Homology for Effective Non-Prehensile Manipulation

Vieira¹,

Nakhimovich²,

Gao³

et al. 2022

Preprint

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This work explores the use of topological tools for achieving effective non-prehensile manipulation in cluttered, constrained workspaces. In particular, it proposes the use of persistent homology as a guiding principle in identifying the appropriate non-prehensile actions, such as pushing, to clean a cluttered space with a robotic arm so as to allow the retrieval of a target object. Persistent homology enables the automatic identification of connected components of blocking objects in the space without the need for manual input or tuning of parameters. The proposed algorithm uses this information to push groups of cylindrical objects together and aims to minimize the number of pushing actions needed to reach to the target. Simulated experiments in a physics engine using a model of the Baxter robot show that the proposed topologydriven solution is achieving significantly higher success rate in solving such constrained problems relatively to state-of-the-art alternatives from the literature. It manages to keep the number of pushing actions low, is computationally efficient and the resulting decisions and motion appear natural for effectively solving such tasks.

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Section: Related Workmentioning

confidence: 99%

Persistent Homology for Effective Non-Prehensile Manipulation

Vieira¹,

Nakhimovich²,

Gao³

et al. 2022

Preprint

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show abstract

“…Previous works in reaching through clutter focused on recognizing objects to be relocated to allow a collisionfree region to reach a target [17], [18]. Uncertainty arising from occlusion or sensor noise complicates this problem and efforts aim to minimize their effects by inferring object shape [19], [20], reason about pose uncertainty [21], or use probabilistic filtering [22]. To achieve higher success rates, human interaction has been proposed to guide a high-level plan, which proposes an ordered sequence of objects to be pushed to approximate intermediate goal positions [23].…”

Section: Related Workmentioning

confidence: 99%

Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation

Vieira¹,

Gao²,

Nakhimovich³

et al. 2022

Preprint

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Performing object retrieval tasks in messy realworld workspaces involves the challenges of uncertainty and clutter. One option is to solve retrieval problems via a sequence of prehensile pick-n-place operations, which can be computationally expensive to compute in highly-cluttered scenarios and also inefficient to execute. The proposed framework selects the option of performing non-prehensile actions, such as pushing, to clean a cluttered workspace to allow a robotic arm to retrieve a target object. Non-prehensile actions, allow to interact simultaneously with multiple objects, which can speed up execution. At the same time, they can significantly increase uncertainty as it is not easy to accurately estimate the outcome of a pushing operation in clutter. The proposed framework integrates topological tools and Monte-Carlo tree search to achieve effective and robust pushing for object retrieval problems. In particular, it proposes using persistent homology to automatically identify manageable clustering of blocking objects in the workspace without the need for manually adjusting hyper-parameters. Furthermore, MCTS uses this information to explore feasible actions to push groups of objects together, aiming to minimize the number of pushing actions needed to clear the path to the target object. Real-world experiments using a Baxter robot, which involves some noise in actuation, show that the proposed framework achieves a higher success rate in solving retrieval tasks in dense clutter compared to state-of-the-art alternatives. Moreover, it produces high-quality solutions with a small number of pushing actions improving the overall execution time. More critically, it is robust enough that it allows to plan the sequence of actions offline and then execute them reliably online with Baxter.

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“…Our work falls within the broad domain of interactive perception [4], which involves leveraging interaction to infer otherwise hidden properties of an environment, such as masses of objects [5], [6], state of a cluttered scene [7], [8] or kinematic structure of articulated objects [9].…”

Section: Related Workmentioning

confidence: 99%

“…Estimating the state of such structured cluttered scenes has been explored by prior works [21], [22] by using passive and active perception techniques. Another approach to tackle the problem is to get the scene to a state where all objects are clearly visible [8]. However, this may not always be feasible since additional space might not be available in some scenarios.…”

Section: Related Workmentioning

confidence: 99%

Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning

Kumar¹,

Essa²,

Ha³

2021

Preprint

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We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to discover hidden objects with minimal interactions. First, we define a novel scene grammar to represent structured clutter. Then we train a Graph Neural Network (GNN) based Scene Generation agent using deep reinforcement learning (deep RL), to manipulate this Scene Grammar to create a diverse set of stable scenes, each containing multiple hidden objects. Given such cluttered scenes, we then train a Scene Exploration agent, using deep RL, to uncover hidden objects by interactively rearranging the scene. We show that our learned agents hide and discover significantly more objects than the baselines. We present quantitative results that prove the generalization capabilities of our agents. We also demonstrate sim-to-real transfer by successfully deploying the learned policy on a real UR10 robot to explore real-world cluttered scenes. The supplemental video can be found at: https://www.youtube.com/watch?v=T2Jo7wwaXss.

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Probabilistic Active Filtering for Object Search in Clutter

Cited by 9 publications

References 14 publications

Persistent Homology for Effective Non-Prehensile Manipulation

Persistent Homology for Effective Non-Prehensile Manipulation

Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation

Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning

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