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Learning value functions with relational state representations for guiding task-and-motion planning
1.
Learning value functions with
relational state representations for task-and-motion planning Beomjoon Kim Luke Shimanuki
2.
Geometric task-and-motion planning
3.
Geometric task-and-motion planning
4.
Geometric task-and-motion planning Complex,
hybrid search problems
5.
Discrete decisions -
which object?
6.
Continuous decisions -
which base pose?
7.
Pick(obj, grasp, base_pose) Parameterized
actions Push(obj, push_dir, base_pose) continuousdiscrete
8.
Planning: sampling-based forward
search Pick(obj1, ?grasp, ?base) Pick(obj1, ?grasp, ?base)
9.
Deciding the abstract
action Pick(obj1, ?grasp, ?base)
10.
Sampling the associated
continuous parameters g, b ~ Uniform(G,B)
11.
Simulating the concrete
action Pick(obj1, g, b)
12.
Guide the search
by learning an action-value function Q(s,Pick(obj1, g1, b1)) vs Q(s,Pick(obj2, g2, b2))
13.
Guide the search
by learning an action-value function Q(s,Pick(obj1, g1, b1)) vs Q(s,Pick(obj2, g2, b2))
14.
But planners are
applied to various environments and goals Q should be applicable to
15.
Values for detailed
actions including base poses and grasps are difficult to generalize across such variability
16.
Q(s, Pick(obj1)) vs Q(s, Pick(obj2) We
propose to guide planning by learning an abstract-action value function
17.
Q(s, Pick(obj1)) vs Q(s, Pick(obj2) Pick(obj1) Pick(obj1,
?g, ?b) Pick(obj2, ?g, ?b) Pick(obj2) We propose to guide planning by learning an abstract-action value function
18.
Abstract Q function
approximates which object should be moved Which object should be moved? Q(s, Pick(?obj))
19.
This abstract, high-level
knowledge is generally applicable Which object should be moved? Q(s, Pick(?obj))
20.
How do we
design a state-representation that affords aggressive generalization?
21.
We use a
relational state representation
22.
OccludesManip(obj1, obj2, home) OccludesPre(obj1,
obj2) IsReachable(obj1) ManipFree(obj1, home) Predicates based on reachability and occlusions
23.
OccludesManip(obj1, obj2, home) OccludesPre(obj1,
obj2) IsReachable(obj1) ManipFree(obj1, home) We encode a relational state using a graph neural network (GNN)
24.
IsReachable(obj1) IsObj(obj1) IsRegion(obj1) IsTargetObj(obj1) Node - predicates
on object or region
25.
OccludesPre(obj1, obj2) Edges -
relations on multiple entities
26.
Value as the
output of the GNN Q(s, Pick(obj1))
27.
Generalization to harder
problems without retraining
28.
Generalization to harder
problems without retraining
29.
30.
Generalization to harder
problems without retraining
31.
Generalization to different
environments without retraining
32.
Generalization to different
environments without retraining
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