Reconfigurable tasks in belief-space planning

Ruiken, Dirk; Liu, Tiffany Q.; Takahashi, Takeshi; Grupen, Roderic A.

doi:10.1109/humanoids.2016.7803431

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…The assembly domain utilizes objects known as ARcubes introduced in (Ruiken et al 2016a). ARcubes are rigid cubes whose size can be adjusted to meet the requirements of the task.…”

Section: A Two-layer Assembly Hierarchymentioning

confidence: 99%

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Planning Robust Manual Tasks in Hierarchical Belief Spaces

Lanighan

Takahashi

Grupen

2018

ICAPS

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This paper introduces a technique for planning in hierarchical belief spaces and demonstrates the idea in an autonomous assembly task. The objective is to effectively propagate belief across multiple levels of abstraction to make control decisions that expose useful state information, manage uncertainty and risk, and actively satisfy task specifications. This approach is demonstrated by performing multiple instances of a simple class of assembly tasks using the uBot-6 mobile manipulator with a two-level hierarchy that manages uncertainty over objects and assembly geometry. The result is a method for composing a sequence of manual interactions that causes changes to the environment and exposes new information in order to support belief that the task is satisfied. This approach has the added virtue that it provides a natural way to accomplish tasks while simultaneously suppressing errors and mitigating risk. We compare performance in an example assembly task against a baseline hybrid approach that combines uncertainty management with a symbolic planner and show statistically significant improvement in task outcomes. In additional demonstrations that challenge the system we highlight useful artifacts of the approach---risk management and autonomous recovery from unexpected events.

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“…The assembly domain utilizes objects known as ARcubes introduced in (Ruiken et al 2016a). ARcubes are rigid cubes whose size can be adjusted to meet the requirements of the task.…”

Section: A Two-layer Assembly Hierarchymentioning

confidence: 99%

“…We use task partitions similar to Ruiken et al (Ruiken et al 2016a) to encode find tasks for the robot at the object level. The belief that hypothesis h belongs to a target task partition c target with goal state s j exceeds belief threshold…”

Section: Object Levelmentioning

confidence: 99%

Planning Robust Manual Tasks in Hierarchical Belief Spaces

Lanighan

Takahashi

Grupen

2018

ICAPS

Self Cite

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“…To address the first two sub tasks (finding and orienting the object), we employed the Active Belief Planner (ABP) of (Ruiken et al 2016a). Their algorithm computes the belief over the classes of models C by summing the belief of aspect nodes x ∈ C that satisfy a given specification.…”

Section: Belief Space Planningmentioning

confidence: 99%

“…A robot finds c target , a suitable class of object that contains the aspect necessary for the task. Following (Ruiken et al 2016a), we define the find(c target ) task as follows,…”

Section: Belief Space Planningmentioning

confidence: 99%

Hybrid Task Planning Grounded in Belief: Constructing Physical Copies of Simple Structures

Takahashi

Lanighan

Grupen

2017

ICAPS

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Symbolic planning methods have proved to be challenging in robotics due to partial observability and noise as well as unavoidable exceptions to rules that symbol semantics depend on. Often the symbols that a robot considers to support for planning are brittle, making them unsuited for even relatively short term use. Maturing probabilistic methods in robotics, however, are providing a sound basis for symbol grounding that supports using probabilistic distributions over symbolic entities as the basis for planning. In this paper, we describe a belief-space planner that stabilizes the semantics of feedback from the environment by actively interacting with a scene. When distributions over higher-level abstractions stabilize, powerful symbolic planning techniques can provide reliable guidance for problem solving. We present such an approach in a hybrid planning scheme that actively controls uncertainty and yields robust state estimation with bounds on uncertainty that can make effective use of powerful symbolic planning techniques. We illustrate the idea in a hybrid planner for autonomous construction tasks with a real robot system.

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“…Interestingly, research has shown that an aspect geometry alone is sufficient in describing a number of complex robotic tasks Ruiken et al (2016a). In particular, object identification and assembly tasks can be reconfigured into a model-referenced belief-space planner.…”

Section: Implications For Task Planningmentioning

confidence: 99%

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

Wong¹

2016

Preprint

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Despite outstanding success in vision amongst other domains, many of the recent deep learning approaches have evident drawbacks for robots. This manuscript surveys recent work in the literature that pertain to applying deep learning systems to the robotics domain, either as means of estimation or as a tool to resolve motor commands directly from raw percepts. These recent advances are only a piece to the puzzle. We suggest that deep learning as a tool alone is insufficient in building a unified framework to acquire general intelligence. For this reason, we complement our survey with insights from cognitive development and refer to ideas from classical control theory, producing an integrated direction for a lifelong learning architecture.

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Reconfigurable tasks in belief-space planning

Cited by 8 publications

References 15 publications

Planning Robust Manual Tasks in Hierarchical Belief Spaces

Planning Robust Manual Tasks in Hierarchical Belief Spaces

Hybrid Task Planning Grounded in Belief: Constructing Physical Copies of Simple Structures

Towards Lifelong Self-Supervision: A Deep Learning Direction for Robotics

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