Comments (2)
Types are effectively just unary predicates, both in theory as well as in practice within FastDownward. Because of this, I've decided to not support them as a separate feature since they do not add expressiveness.
The set of preconditions does directly determine the number of action instances that are grounded for a finite set of objects. Look to add (static) preconditions involving several action parameters that are dependent on each other. For example, in a robotics application, a pose ?p
is typically only well-defined for a single object ?o
, which can be indicated by (Pose ?o ?p)
and included in action/stream conditions (https://github.com/caelan/pddlstream/blob/main/examples/pybullet/pr2/stream.pddl#L16).
I've been able to plan in domains with actions that have a large number of parameters without a performance hit by following this general strategy (https://github.com/caelan/LTAMP/blob/7e0a28dca1156b1dc6d3b9459fed58607249c3ed/plan_tools/pddl/domain.pddl#L91).
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Ah that makes sense. I should have checked what FastDownward actually uses. Much appreciated!
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Related Issues (20)
- 'examples.pybullet.kuka.run' failed when I used 'AtPose' HOT 3
- How to control the order of the objects to be manipulated? HOT 1
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- Pybullet doesn't have visualization beside for pr2_belief HOT 4
- What should I specify on domain and stream in order to plan with obstacles on the continuous_tamp example? HOT 1
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- Can't find a plan in the 'Kuka Cleaning and Cooking' scenario using a Panda HOT 1
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- PR2 has attribute error. HOT 1
- Does PDDLStream support multi agent planning? HOT 3
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- No module named pddl.f_expression HOT 1
- Issue with running the Tamp and Kuka examples with args.simulate=True
- TypeError: unsupported operand type(s) for -: 'tuple' and 'float' HOT 1
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