Towards Combining HTN Planning and Geometric Task Planning

Silva, Lavindra de; Pandey, Amit Kumar; Gharbi, Mamoun; Alami, Rachid

doi:10.48550/arxiv.1307.1482

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bidot et al [7] limit geometric backtracking with heuristics based on statistics for kinematic violations and collisions with movable objects. de Silva et al [8] use Hierarchical Task Networks (HTNs) to perform symbolic search using hierarchically abstracted tasks, performing HTN backtracking whenever geometric backtracking fails. Hierarchical Planning in the Now (HPN) [9] similarly uses a hierarchical approach, but interleaves planning with execution, such that primitive actions at the lowest level of the hierarchy are executed as soon as they are reached by the search algorithm.…”

Section: Related Workmentioning

confidence: 99%

Object-Centric Task and Motion Planning in Dynamic Environments

Migimatsu

Bohg

2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation plans, typically specified as joint-space trajectories, which are valid only as long as the environment is static and perception and control are highly accurate. In case of any changes in the environment, slow re-planning is required. We propose a TAMP algorithm that optimizes over Cartesian frames defined relative to target objects. The resulting plan then remains valid even if the objects are moving and can be executed by reactive controllers that adapt to these changes in real time. We apply our TAMP framework to a torque-controlled robot in a pick and place setting and demonstrate its ability to adapt to changing environments, inaccurate perception, and imprecise control, both in simulation and the real world.

show abstract

Section: Related Workmentioning

confidence: 99%

Object-Centric Task and Motion Planning in Dynamic Environments

Migimatsu

Bohg

2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…Most existing Task and Motion Planning (TAMP) approaches build on a discretization of the configuration space or action parameter space [18], [19], [20] and/or samplebased planners [21], [22], [23]. Our own previous work [12] proposed Logic-Geometric Programming (LGP), an optimization-based approach that can also plan tool-use and dynamic interactions using simplified Newtonian equations and impulse exchange.…”

Section: Related Workmentioning

confidence: 99%

Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning

Toussaint¹,

Ha²,

Driess³

2020

Preprint

View full text Add to dashboard Cite

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose path descriptions of physics which directly allow us to leverage optimization methods to solve planning problems, using multi-physics descriptions that enable the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target.

show abstract

“…Our definition of abstract plans is closely related to the notion of "plan skeletons" considered by several authors [Erdogan and Stilman, 2013; de Silva et al, 2013;Lozano-Pérez and Kaelbling, 2014]. Plan skeletons fix a sequence of operators but leave continuous parameters undefined.…”

Section: Related Workmentioning

confidence: 99%

Admissible Abstractions for Near-optimal Task and Motion Planning

Vega-Brown

Roy

2018

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving 10 13 states without using a separate task planner.

show abstract

Towards Combining HTN Planning and Geometric Task Planning

Cited by 4 publications

References 0 publications

Object-Centric Task and Motion Planning in Dynamic Environments

Object-Centric Task and Motion Planning in Dynamic Environments

Describing Physics For Physical Reasoning: Force-based Sequential Manipulation Planning

Admissible Abstractions for Near-optimal Task and Motion Planning

Contact Info

Product

Resources

About