T ASKOGRAPHY : Evaluating robot task planning over large 3D scene graphs
Anonymous Author(s)
AfﬁliationAddress email
Abstract: 3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
However, typical 3DSGscontain hundreds of objects and symbols even for small environments; rendering task planning on the full graph impractical.
We construct T ASKOGRAPHY ,the ﬁrst large-scale robotic task planning benchmark over 3DSGs.
While mostbenchmarking efforts in this area focus on vision-based planning , we systematically study symbolic planning, to decouple planning performance from visual representation learning.
We observe that, among existing methods, neither classicalnor learning-based planners are capable of real-time planning over full 3DSGs.
Enabling real-time planning demands progress on both (a) sparsifying 3DSGsfor tractable planning and (b) designing planners that better exploit 3DSG hierarchies.
Towards the former goal, we propose SCRUB , a task-conditioned 3DSGsparsiﬁcation method; enabling classical planners to match and in some cases surpass state-of-the-art learning-based planners.
Towards the latter goal, we propose
SEEK , a procedure enabling learning-based planners to exploit 3DSG structure,reducing the number of replanning queries required by current best approaches byan order of magnitude.
We will open-source all code and baselines to spur furtherresearch along the intersections of robot task planning, learning and 3DSGs.
Introduction
Real-world robotic task planning problems in large environments require reasoning over tens ofthousands of object-action pairs.
Faced with long-horizon tasks in such environments, state-ofthe-art task planners struggle to reliably ground actions towards the goal due to this abundance ofchoices.
Hence, designing actionable scene abstractions that enable a range of robotic tasks hasdrawn long-standing attention from the robotics and computer vision communities [1, 2, 3, 4, 5, 6].
A promising approach for building symbolic abstractions from raw perception data are 3D scenegraphs (3DSGs, see Fig. 1) [7, 8, 9] – hierarchical representations of a scene that capture metric,semantic, and relational information, such as affordances, properties, and relationships among sceneentities.
While 3DSGs have to date been applied to simpler planning problems like goal-directednavigation [6], active object search [10], and node classiﬁcation [11], their amenability to morecomplex robotic task planning problems has yet to be thoroughly evaluated.
To investigate the joint application of 3DSGs and modern task planners to complex robotics taskswe propose T ASKOGRAPHY : the ﬁrst large-scale benchmark comprising a number of challengingtask planning domains designed for 3DSGs.
Analyzing planning times and costs on a diversity ofdomains in T ASKOGRAPHY reveals that neither classical nor learning-based planners are capable ofreal-time planning over full 3DSGs, however, that they become so only when 3DSGs are sparsiﬁed.
Many real-world problems only require reasoning over a small subset of scene objects.
E.g., the task“ fetch a mug from the kitchen ” primarily involves reasoning about scene elements associated withmugs or kitchens, rendering a vast majority of the remaining environment contextually irrelevant.
Most planners aren’t able to exploit such implicitly deﬁned task contexts, instead spending most oftheir computation time reasoning about extraneous scene attributes and actions [12] (see Fig. 5).
We argue that performant task planning over 3DSGs demands progress on two fronts: (a) sparsifying 3DSGs to make planning problems tractable, and (b) designing task planners that exploit thespatial hierarchies encapsulated in 3DSGs.
To address (a), we present SCRUB –a planner-agnosticstrategy guaranteed to produce a minimal sufﬁcient object set for grounded planning problems.
Thatis, planning on this reduced subset of scene entities sufﬁces to solve the planning problem deﬁnedover the full 3DSG.
Classical planning over state spaces (3DSGs) augmented by SCRUB outperforms state-of-the-art learning-based planners on the majority of tasks on our benchmark, withoutrequiring any prior learning, establishing a strong baseline for future work in robotic task planning.
To address (b), we present SEEK : a procedure tailored to 3DSGs, which supplements learningbased incremental planners by imposing 3DSG structure, ensuring all objects in the sufﬁcient setare reachable from the start state.
In our experiments, augmenting state-of-the-art planners with
SEEK results in computational savings and an order of magnitude fewer replanning iterations.
In summary, we make the following contributions:
• T ASKOGRAPHY : a large-scale benchmark to evaluate robotic task planning over 3DSGs,• SCRUB : a planner-agnostic strategy to adapt 3DSGs for performant planning,
• SEEK : a procedure that enables learning-based planners to better exploit 3DSGs
We will open source all code and baselines in T ASKOGRAPHY -API, enabling the construction of new task planning domains, and benchmarking the performance of newer
learning-
based planners
Related work
Early research in symbolic planning was centered around optimal planning [13, 14, 15, 16, 17];planners producing solutions that preserve cost or plan length optimality.
These methods are computationally expensive and thereby untenable to even moderately sized problems.
This spurred workon satisﬁcing planners that forgo optimal solutions for cheaper, feasible plans.
Notable paradigmsinclude regression planning [18], tree search [19], and heuristic search [20, 21, 22, 23, 24].
Whilstthe many successes of heuristic planners [25, 26], computing low-cost informative heuristics is deterred by many extraneous objects [27, 12]; an inauspicious characteristic of large 3DSGs.
Robot task planning techniques have focused on constructing more effective representations to planupon [28, 29, 30].
There are also approaches that integrate task and motion planning [31, 32, 33]–further demonstrated in hierarchical task space
[34]–but which fall outside the scope of our work.
Several approaches exploit task hierarchies for robot task planning [35] and control [36, 37, 38].
Different from these, our work focuses on exploiting abstractions in spatial structure encapsulatedin 3DSGs, not to be conﬂated with hierarchical planners that exploit task structure [39].
State-of-the-art learning-based planners have demonstrated promising performance in small-tomoderate problem sizes.
However, techniques such as relational policy learning [40], relationalheuristic learning [41], action grounding [42], program guided symbolic planning [43, 44, 45, 46,47], and regression planning networks [48] fail in large problem instances with branching factorsand operators of the order considered (see Fig. 2) in the T ASKOGRAPHY benchmark.
Moreover,several planners that learn to search [49, 50, 51, 52] depend on hard-to-obtain dense rewards or donot scale with domain complexity [53, 54, 55].
The simpliﬁcation of planning problems via pruning strategies to enable efﬁcient search has beenexplored in both propositional [56, 57, 42] and numeric [58] planning contexts.
Among these,
PLOI [12] is a particularly performant learning-based approach that leverages object-centric reasoning [59, 60, 61, 62] to score and prune extraneous objects to the task.
While PLOI outperformsexisting classical planners on the T ASKOGRAPHY benchmark, it incurs a large number of replanningsteps owing to inaccurate neural network predictions; and inability to exploit 3DSG hierarchies.
Ourproposed SEEK procedure decreases replanning steps by two orders of magnitude.
Planning benchmarks in the symbolic planning communities have featured a variety of tasks withtime complexities ranging from polynomial (e.g., shortest-path) to NP-hard problems (e.g., travelingsalesman).
There also exists a handful of environments [63, 64, 65, 66, 67] for benchmarking learnedaction policies from language directives and ego-centric visual observations, task and motion planning [68], or the modelling of physical interactions [69, 70].
Another recent benchmark [71] onlysupports navigation and block-stacking tasks.
However, there isn’t currently a large-scale benchmark tailored to robotic task planning in 3DSGs with several hundreds of objects.
Background
[Figure]
Task planning.
A task planning problem Π is a tuple (cid:104)O , P , A , T , C , I , G(cid:105) .
As a running example,consider the task ﬁnd an apple, slice it, and place it on the counter.
O is the set of all groundobjects (instances) in the problem.
P is a set of properties, each deﬁned over one or more objects;weight(apple) =
70 grams.
Predicates are subclasses of properties in that they are boolean-valued;canPlace(apple, refrigerator) =
True.
A is a ﬁnite set of lifted actions operating over object tuples;slice(apple), place(apple, counter).
T is a transition model and C denotes state transition costs.
I and
G are initial and goal states.
A state is an assignment of all possible properties to all ground objects.
For the running example, a goal state may be speciﬁed as on(apple, counter) and sliced(apple).
Suchplanning problems may be grounded–slice this apple, or lifted–slice an apple.
3D scene graphs (3DSGs).
A 3DSG [7, 8]is a hierarchical multigraph G =
( V, E ) withk ∈ { 1 · · · K } levels, where V k ∈ V denotesthe set of vertices at level k .
Edges originatingfrom a vertex v ∈ V k may only terminate in
V k − 1 ∪ V k
∪ V k +1 (i.e., edges connect nodeswithin one level of each other).
Each 3DSGin our work comprises at least 5 levels with increasing spatial precision as we move down thehierarchy: the topmost level in the hierarchy isa root node representing a scene.
This nodebranches out to indicate the various ﬂoors inthe building, which in turn branches out to denote various rooms in a ﬂoor, and subsequentlyplaces within a room.
A place is a collection of objects , which may themselves containother objects (to allow for container types suchas cabinets and refrigerators).
At each level,edges indicate various types of relations amongnodes (e.g., at the room level, an edge indicates the existence of a traversible path between tworooms; at the object level, edges indicate multiple affordance relations).
Each node also stores semantic attributes such as node type, functionality, affordances, etc., following [7].
T ASKOGRAPHY
We propose T ASKOGRAPHY : the ﬁrst large scale benchmark to evaluate symbolic planning over3DSGs.
Currently, T ASKOGRAPHY comprises 20 challenging robotic task planning domains totaling 3734 tasks.
Different from current benchmarks for embodied AI that focus primarily on egocentric visual reasoning [64, 72, 73, 63, 70, 66]; T ASKOGRAPHY is designed to evaluate symbolicreasoning over 3DSGs.
To emulate the complexity of real-world task planning problems, T ASKOGRAPHY builds atop the Gibson [74] dataset comprising real-world scans of large building interiors(averaging 2-3 ﬂoors per building; 7 rooms per ﬂoor), and the corresponding 3DSGs [7].
Augmenting 3DSGs with plannable attributes.
A prerequisite for planning over 3DSGs—absentin existing work [9, 7, 8]—is a database of plannable attributes : predicates, actions, and transitionmodels.
To support task planning, we augment each 3DSG in Gibson [74] (tiny and medium splits)with several layers of additional unary and binary predicates.
For each 3DSG node, we obtainclass labels, object dimensions and pose from [7].
We annotate object affordances by buildinga knowledge base of lifted object-action pairs and recursively applying it to every 3DSG, whileaccounting for exceptions (object that are concealed or contained within others).
We also detectdoor objects in the 3DSG and use this to add additional edges describing room connectivity.
Asidefrom ground properties, we also annotate lifted properties per object (e.g., “ is this object typically areceptacle? ”) to enable tasks like (e.g., “ store a set of 10 given objects in empty containers acrossthe building ”).
Altogether, the annotated plannable attributes enable easy and ﬂexible speciﬁcationof a wide variety of planning problems.
Benchmark statistics.
Each of the 20 T ASKOGRAPHY domains speciﬁes a class of planning problems that resemble real-world use cases (and theoretically complex extensions) that a robot wouldencounter in ofﬁce, house, or building scale environments.
These domains range from groundedplanning domains to lifted planning domains, domains with no extraneous objects to domains wheremost objects are extraneous, and domains for which polynomial time solutions exist to NP-hardproblems.
The simplest domains in the benchmark have 1000 state variables and an average branching factor of 5; for hard domains, these are 4000 and 60 respectively (see Fig. 2).
T ASKOGRAPHY -API.
We (will) release all code and data in the form of T ASKOGRAPHY -API,an open-source python package providing access to 18 classical and learned symbolic planners,templates to implement novel domains, generate problem instances of varying complexities, andtrain/evaluate learning-based planners.
Planners considered.
T ASKOGRAPHY supports a comprehensive set of planners to facilitate standardized evaluation on novel domains.
The following planners are available at the time of writing.
• Optimal planners : Fast Downward (FD) with the opt-lmcut heuristic [22], Sat-
Plan [15], Delﬁ [16], DecStar-optimal [24], and Monte Carlo tree search.
• Satisﬁcing planners : Fast Forward (FF), FF with axioms (FF-X) [21], Fast Downward
(FD) with the lama-cut heuristic [22], DecStar-satisﬁcing [24], Cerberus [23], Best First
Width Search (BFWS) [75], and regression planning.
• Learning-based planners : Relational policy learning [40], Planning with learned object
importance (PLOI)
[12] (and variants – see Sec. 6).
General assumptions.
To facilitate evaluation of all of these classes of planners, the ﬁrst edition ofour benchmark only considers fully observable tasks and discrete state and action spaces.
All goalstates are speciﬁed as conjunctions of literals.
While we make no distinction between deterministic or stochastic transitions, all current experiments assume a closed world , i.e., all possible liftedactions and effects are known apriori.
4.1 Robot planning domains: Case studies
The full T ASKOGRAPHY benchmark comprises 20 domains.
We discuss the four task categoriesfrom which all domains are constructed that we believe to be interesting to a broad robotics audience.
Domain 1.
Rearrangement( k ) :
Based on the recently proposed rearrangement challenge [76], thistask requires a robot randomly spawned to rearrange a set of k objects of interest into k corresponding receptacles.
The robot often needs to execute multiple other actions along the way, suchas opening/closing doors, navigating to goals, planning the sequence of objects to visit, etc.
Domain 2.
Courier ( n, k ) :
A robot that couriers objects is equipped with a knapsack of maximumpayload capacity of n units.
The robot needs to locate and courier k objects (of varying weightsw ∈ { 1 , 2 , 3 } units) to k distinct delivery points.
The knapsack can be used to stow and retrieveitems in random-access fashion; effectively embedding a combinatorial optimization problem intothe task.
Stow and retrieve actions increase branching, necessitating far deeper searches.
We also provide lifted variants of these tasks.
Here, goals are speciﬁed over desired object-receptacleclass relations (e.g., “put a cup on a table”) as opposed to over object instances (e.g., “put this cupon the table”).
These tasks introduce ambiguity in both the search of classical task-planners andlearning-based techniques, which must now distinguish object instances of relevant classes.
[Table]
c i
[Table]
Domain 3.
Lifted Rearrangement ( k ) :
A lifted version of the rearrangement domain where the goals [Table] are speciﬁed at an object category level, as opposed to an instance level.
Domain 4.
Lifted Courier ( n , k ) :
A lifted version of the courier domain where the goals are speciﬁedat an object category level, as opposed to an instance level.
To promote compatability with a range of planning systems [26, 77], we represent all tasks in PDDLformat [78, 79].
We also include mechanisms for translating tasks into alternative problem deﬁnitionlanguages that are essential for some of our supported planners [15].
4.2 Benchmarking classical and learned planners on T ASKOGRAPHY
We present the empirical results on the T ASKOGRAPHY benchmark across several classes of taskplanners in Table.
(Please consult supplementary material for a number of additional results).
Evaluation protocol.
We treat the evaluation of optimal planners separately to the remaining methods.
Optimal planners are not intended to be fast unlike satisﬁcing and learning-based variants.
Rather, they compute a solution of minimum length (not necessarily unique) to a given problem.
Optimal planners are hence allotted 10 minutes to solve each problem, while satisﬁcing and learningbased planners are allotted 30 seconds.
For learning-based methods, we evaluate results over 10random seeds for statistical signiﬁcance.
We report standard deviations in the supplementary material.
All domains comprise 40 training problems.
The domains tagged Tiny and Medium comprise55 and 182 test problems respectively, unless otherwise speciﬁed.
Optimal planners work only on the simplest of domains.
Despite the reasonable performance ofoptimal planners on the Rearrangment(1) domain, they are unable to efﬁciently scale with increasingtask complexity and fail to solve a single task on the Rearrangment (k) and Courier (n, k) domainsfor k > 2 .
In particular, the Rearrangement(1) domain is a superset of the grounded hierarchicalpath planning (HPP) task as described by Rosinol et al.
Because the HPP task does not considerstate changes to the scene graph (i.e., directly equating the 3DSG to the planning graph for search),efﬁcient shortest path planning is tractable.
However, increasingly complex robot tasks requiresmore than the mere ability to path plan in 3DSGs.
[Table]
Planning performance degrades with domain complexity, not scene complexity.
We observe an increasein the number of planning failures and timeouts as satisﬁcing planners are applied to larger Rearrangement(k)domains (Table 2).
Interestingly, larger scenes do not appear to directly correlate with task complexity, as the performance metrics remain largely consistent between thetiny and medium splits of the same domain (Table. 2).
Satisﬁcing planners fail in domains requiring long-horizon reasoning.
In the Courier(n, k) domains, satisﬁcing planners tend to produce shorter length solutions by leveraging the knapsack’scapacity to stow objects on the way to various delivery points.
However, the planners often display shortsighted behaviours by stowing objects early in the search, depleting knapsack slots thatcould potentially help further along the task.
This yields dead-end conﬁgurations and excessivebacktracking, and thus, an increase in timeouts is observed.
Planners that do not exploit forward heuristics fail due to large branching factors.
Due to thelarge branching factor of our domains, common strategies such as Monte-Carlo Tree Search (MCTS)and MC Regression Planning are unable to solve any task within a reasonable time constraint.
Forinstance, a Rearrangement(10) task has an average branching factor of 6.5 for MCTS.
Since a rewardis only obtained at the end (typical planners take 200 steps to get there), MCTS degenerates to a slowbreadth-ﬁrst search.
Learning based planners that prune the state spaceexcel on all domains.
We also evaluate two prevalent learning-to-plan methods based on generalized relational planning [40] and planning with learned abstractions [12].
While the relational policy stuggles to generalize in our domains, PLOI demonstrates an impressiveability to detect and prune contextually irrelvant parts ofthe 3DSGs.
However, it also requires a signiﬁcant number of replanning steps (see ﬁgure to the right) as it oftenretains objects within a graph without ensuring that allproperties and ancestors required to access the object arealso preserved.
Discussion.
Our evaluation of existing performant planners on the T ASKOGRAPHY benchmarkconsistently reveals two important trends across all domains.
• Pruning a 3DSG is essential for real-time performance, more so on challenging domains. • While learning-based planners excel across all domains, they require a large number of
replanning steps.
These imply that efﬁcient utilization of 3DSGs in real-time robotic task planning requires bothadapting 3DSGs to better suit existing planners, and enabling performant (learning-based) plannersto better exploit 3DSG hierarchy.
The remainder of our work addresses these issues.
SCRUB : Principled sparsiﬁcation of 3DSGs for efﬁcient planning
[Table]
As discussed above, learning-based planners leverage awealth of prior knowledge acquired during a trainingphase to signiﬁcantly prune extraneous scene graph entities.
We argue that, if equipped with the right sparsiﬁcation machinery, classical planners can compete with, oroutperform learning methods.
We develop SCRUB , a principled 3DSG sparsiﬁcation scheme that prunes a 3DSG
G (w.r.t.
planning problem Π G = (cid:104)O , P , A , T , C , I , G(cid:105) )by removing vertices and edges extraneous to the task,resulting in a sparsiﬁed 3DSG ˆ G (and planning problemˆΠ ˆ G = (cid:104)
ˆ O , ˆ P , ˆ A , ˆ T , ˆ C , ˆ I , G(cid:105) )
Deﬁnition 1.
A valid 3DSG sparsiﬁcation of G for aplanning problem Π G to ˆ G (and corresponding planningproblem ˆΠ ˆ G ) is a computable function SCRUB (Π G ) =
ˆΠ ˆ G such that, a plan p solves Π g
iff it solves ˆΠ ˆ G .
A satisﬁcing plan for Π G may thus be obtained by simplysolving the (much easier to solve) sparsiﬁed problem ˆΠ ˆ G .
Savings in planning time depend on thecomplexity of the sparsiﬁed subgraph ˆ G .
SCRUB presents a simple strategy which is guaranteed tobe minimal for grounded planning problems and satisﬁcing for lifted planning problems.
For exposition, we consider grounded planning problems; see appendix for how SCRUB is adaptedto lifted planning problems or stochastic transitions.
SCRUB begins with an initially empty sufﬁcientobject set
ˆ O .
Satisfying the goal minimally requires all ground objects in the goal to be included inthe sufﬁcient object set ˆ O (else goal objects are unreachable).
In addition, the robot itself must bepart of the sufﬁcient set.
Let p be the set of all binary predicates which include any of these objects.
And let g be the set of all objects contained in p .
In general, this will be a superset of the objectswe started with.
We iteratively repeat this process, each time adding the new objects in g to oursufﬁcient set ˆ O .
The process terminates either when either the set g has no new objects (indicating convergence), oruntil all the objects in the scene graph are visited at least once (indicating the input graph alreadydeﬁnes a minimal object set).
We initialize the nodes of ˆ G with objects in ˆ O , and copy over all edges( u, v ) ∈ G for which both u, v ∈
ˆ O .
SCRUB terminates in time linear in the nuber of the predicatesor nodes (whichever is larger).
Proposition 1.
SCRUB is complete and results in a minimal scene subgraph for all grounded planning problems over the scenegraph domain.
(Please refer to supp.
material for proof)
5.1 Impact of SCRUB on modern task planners
In this section, we investigate the effect that a 3DSG reduction scheme like SCRUB may have onthe performance of modern task planners.
We experiment with the four domains shown in ﬁg. 4 andevaluate the impact of scrub on planning performance and on domain structure.
SCRUB enables classical planners to obtain performance at least as good as state-of-theart planners .
In Fig.
4, we see that SCRUB drastically reduces planning time for FF ,
FD-lama-first , and BFWS to a few milliseconds on Rearrangement(10), and upper-boundstimes at 5 seconds on Courier(10, 10).
We see this enables BFWS, FD, and FF to outperform PLOI(lower plan lengths for similar plan times).
The grounded domains each have 182 test problems, andthe lifted domains each have 70 test problems.
SCRUB greatly reduces the number of operators and states .
To asses the impact of SCRUB , wecompute statistics (number of operators, number of state variables, and average number of branchesper state) in Fig.
We see that SCRUB prunes more than two-thirds of the operators and statevariables for grounded planning problems, and about a third in the case of lifted planning problems.
[Table]
SCRUB enables optimal planners to run on lifted domains .
Table 3 reports results of running the satisﬁcing and optimalvariants of FD with and without SCRUB , on the Lifted Rearrangement(5) domain.
While FD (optimal) did not convergeeven with a timeout of 24 hours , FD (optimal) + scrub solvedabout 72% of the tasks under a 30-second timeout, taking 2seconds per task on average.
SEEK : A procedure for efﬁcient learning-based planning
While SCRUB results in a 3DSG reduction that is guaranteed to ﬁnd a satisﬁcing plan—if oneexists—its conservative approach hurts performance in challenging lifted planning problems asshown in Fig.
For such problems, learning-based graph-pruning strategies like PLOI [12] outperform classical planners over SCRUBBED 3DSGs.
However, as can be seen in Sec.
4.2, even
PLOI [12] incurs a signiﬁcant number of replanning iterations.
[Table]
We posit that several replanning iterations may be avoided by exploiting the 3DSG hierarchy.
Pruning strategies like PLOI ﬁrst score all objects, and retain a minimal set by thresholding.
A simplethreshold does little to ensure that all retained objects are reachable from the root of the scene graph.
To alleviate this issue, we propose SEEK : a procedure that ensures we obtain a connected graph,with the objective of reducing the number of replanning steps needed.
SEEK requires as input the 3DSG, the planning problem Π , and an object scoring mechanism f θ .
This scoring mechanism is typically a graph neural network (akin to [12]) that, given the currentstate, scores each object with an importance value in [0 , 1] .
We ﬁrst run the scorer and only retainobjects above a threshold score t .
We follow an identical approach to PLOI [12] and at each stepgeometrically decay the threshold by γ , such that at iteration i , the threshold is t i = γ
i t i − 1 , witht 0 , γ ∈ [0 , 1) .
For each retained object o , we recursively traverse up the 3DSG, adding all ancestorsof o to the sufﬁcient object set.
This procedure ensures that all objects are reachable from theirrespective room nodes.
While SEEK , unlike SCRUB , is not guaranteed to be satisﬁcing, it results infar fewer replanning steps without affecting computation time.
SEEK reduces replanning steps by an order of magnitude .
To assess the impact of the SEEK procedure onplanning performance, we evaluate performance with respect to other learning-based planners on T ASKOGRAPHYin Table 4.
As a baseline, we evaluate a random pruning strategy that uniformly randomly retains or prunesevery object.
Even for this naive strategy, SEEK offerssigniﬁcant performance improvement.
We also evaluate
PLOI [12] and our adaptation dubbed hierarchical , whichtrains multiple graph neural networks, one for each levelof the 3DSG hierarchy.
For each variant, SEEK offers aconsistent performance improvement by decreasing thenumber of replanning steps required as seen in Fig.
SEEK is thus a conceptually simple strategy for use withlearning-based planners.
SCRUB on grounded domains, SEEK on lifted domains : In general, we note that SCRUB is more performant on grounded domains (due to minimality properties)and SEEK is more performant on lifted domains (where
SCRUB typically retains all instances of important object categories, but SEEK is more effective dueto its opportunistic retention of instances (Fig. 7)).
Concluding remarks
Limitations.
T ASKOGRAPHY currently supports only a fraction of the diverse types of planningproblems possible on 3DSGs.
Geared towards identifying the most promising avenues in learningbased planning, the ﬁrst release of this benchmark focuses exclusively on ofﬂine task planning infully observable and deterministic domains.
Furthermore, low-level motion planning is excludedfrom our benchmark.
Robots operating in the real world will need to reason under partial observability, sensor noise, and resource constraints.
Outlook.
T ASKOGRAPHY , in conjunction with SCRUB and SEEK aid the robot learning communityby (a) providing guidelines and implementations for practitioners choosing a task planner, (b) serving as a benchmark for upcoming learning-based planners, and (c) guiding the design of futuristicspatial representations for robotic task planning.
We believe T ASKOGRAPHY is a ﬁrst step towardsaddressing several of the grand challenges along the road to developing general planning capabilitiesfor autonomous intelligent robots.