Sequential quadratic programming for task plan optimization

Hadfield-Menell, Dylan; Lin, Christopher H.; Chitnis, Rohan; Russell, Stuart; Abbeel, Pieter

doi:10.1109/iros.2016.7759740

Cited by 19 publications

(25 citation statements)

References 9 publications

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“…Therefore, this approach may have difficulty when the solution space is relatively small since the probability of being able to sample the correct solution is small. In contrast, the optimization-based approach used optimization techniques such as logic-geometric programming ( Toussaint et al., 2018 ) or sequential quadratic programming ( Hadfield-Menell et al., 2016 ). It is able to handle problems with a small solution space more efficiently if the local optima can be handled properly.…”

Section: Robot Tool Use Literaturementioning

confidence: 99%

Robot tool use: A survey

Qin

Brawer²,

Scassellati³

2023

Front. Robot. AI

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Using human tools can significantly benefit robots in many application domains. Such ability would allow robots to solve problems that they were unable to without tools. However, robot tool use is a challenging task. Tool use was initially considered to be the ability that distinguishes human beings from other animals. We identify three skills required for robot tool use: perception, manipulation, and high-level cognition skills. While both general manipulation tasks and tool use tasks require the same level of perception accuracy, there are unique manipulation and cognition challenges in robot tool use. In this survey, we first define robot tool use. The definition highlighted the skills required for robot tool use. The skills coincide with an affordance model which defined a three-way relation between actions, objects, and effects. We also compile a taxonomy of robot tool use with insights from animal tool use literature. Our definition and taxonomy lay a theoretical foundation for future robot tool use studies and also serve as practical guidelines for robot tool use applications. We first categorize tool use based on the context of the task. The contexts are highly similar for the same task (e.g., cutting) in non-causal tool use, while the contexts for causal tool use are diverse. We further categorize causal tool use based on the task complexity suggested in animal tool use studies into single-manipulation tool use and multiple-manipulation tool use. Single-manipulation tool use are sub-categorized based on tool features and prior experiences of tool use. This type of tool may be considered as building blocks of causal tool use. Multiple-manipulation tool use combines these building blocks in different ways. The different combinations categorize multiple-manipulation tool use. Moreover, we identify different skills required in each sub-type in the taxonomy. We then review previous studies on robot tool use based on the taxonomy and describe how the relations are learned in these studies. We conclude with a discussion of the current applications of robot tool use and open questions to address future robot tool use.

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Section: Robot Tool Use Literaturementioning

confidence: 99%

Robot tool use: A survey

Qin

Brawer²,

Scassellati³

2023

Front. Robot. AI

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“…For systems without access to a GPU or other neural network accelerator, it may be fruitful to explore other routes to compute a warm-start trajectory, e.g., different/smaller network design, or a nearest trajectory from the training dataset (42). There may be potential for using a deep learning-based warm start to speed up constrained optimizations in other fields of robotics, e.g., grasp contact models (43), task planning (44,45), and model predictive control (46,47)-potentially allowing such algorithms to run at interactive rates and enabling new applications.…”

Section: Opportunities For Future Researchmentioning

confidence: 99%

Deep learning can accelerate grasp-optimized motion planning

et al. 2020

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Robots for picking in e-commerce warehouses require rapid computing of efficient and smooth robot arm motions between varying configurations. Recent results integrate grasp analysis with arm motion planning to compute optimal smooth arm motions; however, computation times on the order of tens of seconds dominate motion times. Recent advances in deep learning allow neural networks to quickly compute these motions; however, they lack the precision required to produce kinematically and dynamically feasible motions. While infeasible, the network-computed motions approximate the optimized results. The proposed method warm starts the optimization process by using the approximate motions as a starting point from which the optimizing motion planner refines to an optimized and feasible motion with few iterations. In experiments, the proposed deep learning–based warm-started optimizing motion planner reduces compute and motion time when compared to a sampling-based asymptotically optimal motion planner and an optimizing motion planner. When applied to grasp-optimized motion planning, the results suggest that deep learning can reduce the computation time by two orders of magnitude (300×), from 29 s to 80 ms, making it practical for e-commerce warehouse picking.

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“…The robotics community, on the other hand, has recently shown interest in combined Task and Motion Planning (TAMP) (Srivastava et al 2014;Lozano-Pérez and Kaelbling 2014). Like our planner, some interesting approaches combine optimization with discrete search (Toussaint 2015;Hadfield-Menell et al 2016). These planners excel at highly constrained manipulation problems but do not scale to long planning horizons due to time discretization.…”

Section: Related Workmentioning

confidence: 99%

Mixed Discrete-Continuous Planning with Convex Optimization

Fernández-González

Karpas

Williams

2017

AAAI

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Robots operating in the real world must be able to handle both discrete and continuous change. Many robot behaviors can be controlled through numeric parameters (called control variables), which affect the rate of the continuous change. Previous approaches capable of reasoning efficiently with control variables impose severe restrictions that limit the expressivity of the problems that can be solved. A broad class of robotic applications require, for example, convex quadratic constraints on state variables and control variables that are jointly constrained and that affect multiple state variables simultaneously. However, extensions to prior approaches are not straightforward, since these characteristics are non-linear and hard to scale. We introduce cqScotty, a heuristic forward search planner that solves these problems efficiently. While naive formulations of consistency checks are not convex and do not scale, cqScotty uses an efficient convex formulation, in the form of a Second Order Cone Program (SOCP), that is very fast to solve. We demonstrate the scalability of our approach on three new realistic domains.

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Sequential quadratic programming for task plan optimization

Cited by 19 publications

References 9 publications

Robot tool use: A survey

Robot tool use: A survey

Deep learning can accelerate grasp-optimized motion planning

Mixed Discrete-Continuous Planning with Convex Optimization

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