Informing Multi-Modal Planning with Synergistic Discrete Leads

Kingston, Zachary; Wells, Andrew M.; Moll, Mark; Kavraki, Lydia E.

doi:10.1109/icra40945.2020.9197545

Cited by 21 publications

(11 citation statements)

References 36 publications

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“…Additionally, manipulation parameters such as grasping locations are manually selected, resulting in a finite set of manipulation behaviors. This framework has the potential to be generalizable, by automating perception and state inference, learning related object importance for task decomposition [28] to automatically prune causal graph, learning compositional models for symbolic planning [29], and using optimization or sampling techniques to select manipulation parameters [30], [31], such as final object locations.…”

Section: Discussionmentioning

confidence: 99%

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

et al. 2021

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This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization. This TAMP method exploits the discrete structure of sequential manipulation for long-horizon and versatile tasks in dynamic environments. At the symbolic planning level, we propose a scalable decision-making method for long-horizon manipulation tasks using the Planning Domain Definition Language (PDDL) with causal graph decomposition. At the motion planning level, we devise a trajectory optimization (TO) approach based on the Differential Dynamic Programming (DDP) and Alternating Direction Method of Multipliers (ADMM), suitable for solving constrained, large-scale nonlinear optimization in a distributed manner. Distinct from conventional geometric motion planners, our approach generates highly dynamic manipulation motions by incorporating the full robot and object dynamics. Furthermore, in lieu of a hierarchical planning approach, we solve a holistically integrated bilevel optimization problem involving costs from both the low-level TO and the high-level search. Simulation and experimental results demonstrate dynamic manipulation for long-horizon object sorting tasks in clutter and on a moving conveyor belt.

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Section: Discussionmentioning

confidence: 99%

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

et al. 2021

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“…Essentially, our work is solving a multi-modal planning problem (Hauser and Latombe, 2010; Kingston et al , 2020) that plans across of series of subspaces (identified by contact states). We identify a subspace using evenly sampled contact positions on a table surface.…”

Section: Discussionmentioning

confidence: 99%

Planning to repose long and heavy objects considering a combination of regrasp and constrained drooping

Raessa

Wan

Harada

2021

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Purpose This paper aims to present a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces. Design/methodology/approach The planner includes a task-level layer and a motion-level layer. This paper formulates the manipulation planning problem at the task level by considering grasp poses as nodes and object poses for edges. This paper considers regrasping and constrained in-hand slip (drooping) during building graphs and find mixed regrasping and drooping sequences by searching the graph. The generated sequences autonomously divide the object weight between the arm and the support surface and avoid configuration obstacles. Cartesian planning is used at the robot motion level to generate motions between adjacent critical grasp poses of the sequence found by the task-level layer. Findings Various experiments are carried out to examine the performance of the proposed planner. The results show improved capability of robot arms to manipulate long and heavy objects using the proposed planner. Originality/value The authors’ contribution is that they initially develop a graph-based planning system that reasons both in-hand and regrasp manipulation motion considering external supports. On one hand, the planner integrates regrasping and drooping to realize in-hand manipulation with external support. On the other hand, it switches states by releasing and regrasping objects when the object is in stably placed. The search graphs' nodes could be retrieved from remote cloud servers that provide a large amount of pre-annotated data to implement cyber intelligence.

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“…Though, sampling-based methods without informed exploration are vastly inefficient and suffer from time-complexities associated with over-exploration. Recent work has attempted to address this issue by using informed "leads" to guide exploration [28]. In our work, we build off these observations and constrain our planner's search according to physical properties of the SO(3) rotation group.…”

Section: Related Work 1) Modeling Manipulationmentioning

confidence: 99%

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

Morgan,

Hang,

Wen

et al. 2022

Preprint

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Informing Multi-Modal Planning with Synergistic Discrete Leads

Cited by 21 publications

References 36 publications

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

Planning to repose long and heavy objects considering a combination of regrasp and constrained drooping

Complex In-Hand Manipulation via Compliance-Enabled Finger Gaiting and Multi-Modal Planning

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