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REVIEW 2 major objections 4 minor 84 references

ClevrSkills: Compositional Language and Visual Reasoning in Robotics

T0 review · 2 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Even after pretraining, vision-language models fail to compose robot skills into new tasks.

desk verdict Useful benchmark artifact, but the central claim about compositional failure is undercut by unreliable L0 base skills; needs a conditional-success analysis. read the letter →

arxiv 2411.09052 v1 pith:AZDVPMZC submitted 2024-11-13 cs.RO cs.LG

Sanjay Haresh , Daniel Dijkman , Apratim Bhattacharyya , Roland Memisevic This is my paper · ORCID
classification cs.ROcs.LG
keywords compositionalgeneralizationvision-languagemodelsrobotmanipulationbenchmarksuiteimitationlearningmulti-modalpromptsManiSkill2pick-and-place
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether vision-language models that have learned basic robot manipulation skills can recombine those skills to solve new, higher-level tasks without being trained on them. To answer it, the authors build ClevrSkills, a simulation benchmark with 33 tasks arranged in three levels: simple motor skills (L0), intermediate compositions such as sorting and stacking (L1), and complex compositions such as balancing a scale or sorting by throwing (L2). They generate 330,000 oracle trajectories with language, visual, and multi-modal annotations, and they benchmark several open vision-language policies. The central finding is that even after pre-training on large task collections and after fine-tuning on the benchmark's own data, all tested models fail on the composed L1 and L2 tasks, with near-zero zero-shot success and low fine-tuned success. If correct, the result indicates that current vision-language-action models do not exhibit compositional generalization in embodied manipulation, and that progress will require more than scaling data or fine-tuning on the component skills.

What carries the argument

The load-bearing mechanism is the three-level task curriculum built on predicate-based task definitions. Physical predicates (such as EEAtPos, OnTop, Inside, Touch) specify target states, and logical predicates (Set, Sequence, Once) combine them into tasks, so every L1 and L2 task is explicitly a composition of L0-level skills and can be solved by scripted oracle 'solver' policies that recursively invoke lower-level solvers. This design makes it possible to train on L0, then test zero-shot on L1 and L2, and to know exactly which skill compositions are required. The accompanying dataset provides 330k trajectories, hierarchical language annotations, bounding boxes, key-step frames, and multi-modal prompts. The oracle trajectories both define the demonstration data and act as a ceiling for evaluating the learned policies.

What would settle it

Train a policy to near-perfect success (for example, above 95%) on every L0 skill separately, then evaluate it zero-shot on L1 and L2 tasks. If such a policy solves the composed tasks at high rates, the paper's conclusion that current models fail at compositional generalization would be overturned; if it still fails, the composition bottleneck would be confirmed. A simpler check would be to rerun the paper's evaluation while conditioning only on episodes where the model completed all L0 subskills, and see whether L1/L2 success rises.

Watch

Extended reading notes

Core claim

The paper's central claim is that state-of-the-art vision-language models, despite broad pre-training and additional fine-tuning, cannot compose previously learned low-level manipulation skills to perform novel high-level tasks in a closed-loop robotics setting. The evidence is the benchmark's L0->L1->L2 curriculum: models trained on L0 reach at best roughly 63% success on those simple skills, yet their zero-shot success on L1 and L2 tasks is essentially zero, and fine-tuning on L1 and L2 leaves most success rates in single digits. The paper also shows that multi-modal (text-plus-image) prompts are harder for several baselines than text-only prompts, suggesting part of the difficulty lies in grounding visual task specifications. The authors conclude that compositional generalization in robotics remains unsolved by current VLM-based policies.

Load-bearing premise

The conclusion assumes that near-zero success on L1 and L2 tasks reflects an inability to compose skills, not the models' incomplete mastery of the L0 skills themselves; since the best model reaches only about 63% success on L0, a composed task can fail simply because a base skill misfires.

Editorial extensions

If this is right

  • If the finding holds, evaluating compositional generalization in robotics requires controlling for base-skill mastery; raw task success on composed tasks cannot separate planning failures from execution failures.
  • Current VLM-based policies trained on large internet and robotics datasets will not generalize zero-shot to novel task specifications that recombine known skills.
  • Fine-tuning on the composed tasks themselves does not close the gap, so the failure is not merely a matter of seeing the task distribution; the models lack a general composition mechanism.
  • The benchmark's predicate structure lets future work measure progress per skill and per composition, rather than with a single end-to-end metric.
  • Multi-modal prompts add difficulty beyond text-only instructions, so visual grounding of task specifications is itself a bottleneck.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the best model only masters about 63% of L0 skills, part of the L1/L2 failure could be downstream of unreliable base skills rather than compositional reasoning; a policy with near-perfect L0 performance would be needed to isolate the composition deficit.
  • If the bottleneck is compositional, then collecting more demonstration data for component skills alone will not suffice; methods that explicitly train on predicate-level composition or symbolic planning with learned skill modules would be a natural testable next step.
  • The same benchmark design could be extended to test whether hierarchical policies that separately learn skills and a high-level selector outperform end-to-end video-conditioned policies.
  • A real-robot counterpart would be needed to see whether the failure persists outside simulation, since sim-to-real transfer may add new confounds.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper introduces ClevrSkills, a benchmark built on ManiSkill2 with 33 manipulation tasks organized into three levels of compositional difficulty (L0 base skills, L1 intermediate compositions, L2 complex compositions), together with a dataset of roughly 330k oracle-generated trajectories with multi-modal prompts, language annotations, bounding boxes, key steps, and dense rewards. The authors benchmark several open-source vision-language policies (JAT, Octo, RoboFlamingo, and their own StreamRoboLM) under zero-shot and fine-tuned conditions. The central claim is that current VLMs, even after pre-training on large amounts of data, fail at compositional generalization in robotics: they can partially acquire L0 skills but cannot compose them into novel L1/L2 tasks.

Significance. The benchmark infrastructure is a genuine contribution: a three-level curriculum explicitly designed around skill composition, a large dataset with rich annotations, oracle solvers with motion planning, and a public release commitment. The evaluation is clean in the sense that the oracle policies are scripted and the models are measured empirically, so there is no circular derivation or fitted-parameter concern. The per-task results and the comparison across four baselines are useful. However, the central negative claim—that observed L1/L2 failures demonstrate a compositional generalization deficit—is currently confounded by incomplete mastery of L0 skills, as detailed in the major comments. If that confound is addressed with a conditional analysis, the benchmark would be a valuable resource for the robotics and VLM communities.

major comments (2)
  1. [§5.3, Table 2, Figure 5] The central negative claim—that failures at L1/L2 reflect compositional generalization failure—is not supported by the current evidence because the prerequisite L0 skills are not reliably mastered. Table 2 shows that the best L0 success is 62.91% (StreamRoboLM-OPT, seen attributes) and 55.41% (StreamRoboLM-Llama3, unseen attributes), and Figure 5 shows particularly low per-task success on pick, place, push, rotate, and touch variants. Since §5.3 states that L1 and L2 tasks require roughly 9 and 11 successful L0-skill executions respectively, even a flawless compositional planner with per-primitive success probability p ≈ 0.63 would complete a 9-primitive L1 episode with probability approximately 0.63^9 ≈ 1.6%, which is the same regime as the reported near-zero L1 success. The observed L1/L2 failures are therefore quantitatively consistent with low-level skill unreliability alone, independent of any compositional deficit. The phrase 'reasonable performance on the L0 base skills' in §5.3 is not supported by the reported numbers. Please add a conditional analysis that reports L1/L2 success only on episodes in which the requisite L0 sub-skills were individually executed successfully, or otherwise control for L0 failure modes before attributing failure to composition.
  2. [§5.3, Table 3] The sentence 'only Octo [43] achieves non-zero success rate on any of the tasks' is contradicted by Table 3: in the L2 zero-shot columns, JAT reports 0.83% success and Octo reports 0.83%; in the L1 zero-shot columns, Octo reports 0.33%. Please correct the text to agree with the table, e.g., by stating that almost all zero-shot L1/L2 success rates are at or near zero, with Octo and JAT occasionally reporting small non-zero values.
minor comments (4)
  1. [Appendix B.1] The file name 'succes.npy' appears to be a typo; it should be 'success.npy'.
  2. [Appendix G] The text refers to 'RTT Connect', which should be 'RRT-Connect' (the rapidly-exploring random tree connect variant used by OMPL).
  3. [Appendix A.1, Task 4] Prompt (b) for the Place task reads 'Put object with tex:object 1 texture on object with tex:object2 texture' and appears to have missing braces around the placeholders; it should likely be 'Put object with {tex:object}1 texture on object with {tex:object}2 texture'.
  4. [Appendix A.3, Task 3] The description of Stack topple contains the typo 'compostion' instead of 'composition'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the benchmark's conclusions are empirical measurements, not derivations from fitted inputs.

full rationale

ClevrSkills is a benchmark paper: the central claim is that benchmarked VLMs fail compositional generalization, supported by success-rate tables from a fixed evaluation protocol. There is no fitted parameter, no predictive equation, and no derived quantity that is equivalent to its input by construction. The L0-to-L1/L2 hierarchy is a design assumption about what counts as composition, not a result derived from the data, so it cannot be circular. The only in-house citation (LRR for StreamRoboLM) is used for architectural adaptation, not as load-bearing evidence for the failure claim; the failure claim is directly measured on the released tasks. The reviewer-flagged confound that weak L0 skills (best 62.91% success) could explain near-zero L1/L2 zero-shot results is a measurement-validity concern about attribution, not a circularity of the kind where an output equals its input; accordingly it does not raise the circularity score. Because the paper's conclusions reduce to empirical success rates of externally defined baselines on a public benchmark, the derivation chain is self-contained and no circular step is exhibited.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The benchmark itself is a new instrument, but it rests on standard simulation tools and hand-crafted task definitions. No free parameters are fitted to data. The main assumptions are that the oracle policies are reliable and that the task hierarchy is a valid measure of compositional generalization.

assumptions (4)
  • domain assumption Oracle policies generate correct demonstrations and that filtered failed trajectories do not bias the dataset.
    The dataset is generated by scripted solvers; no independent verification of oracle correctness is provided beyond success criteria.
  • domain assumption The predicate decomposition of tasks accurately reflects compositional generalization.
    The benchmark's validity depends on the chosen task hierarchy being a meaningful measure of compositionality, as stated in Section 3.2 and 4.
  • domain assumption The success criteria are reasonable and consistent across tasks.
    Success is defined by hand-crafted predicates (position/rotation tolerances, contact conditions) whose strictness can affect model scores, as acknowledged in Section 5.3.
  • domain assumption ManiSkill2's simulated physics and rendering are adequate for evaluating manipulation policies.
    All results are in simulation; the paper states in Section 6 that a real-world counterpart is future work.

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Cite this review

Pith. "Pith review of ClevrSkills: Compositional Language and Visual Reasoning in Robotics." pith.science (2026). https://pith.science/paper/AZDVPMZC

@misc{pith2026241109052,
  author       = {Pith},
  title        = {Pith review of: ClevrSkills: Compositional Language and Visual Reasoning in Robotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AZDVPMZC}},
  note         = {Machine review of arXiv:2411.09052}
}
read the original abstract

Robotics tasks are highly compositional by nature. For example, to perform a high-level task like cleaning the table a robot must employ low-level capabilities of moving the effectors to the objects on the table, pick them up and then move them off the table one-by-one, while re-evaluating the consequently dynamic scenario in the process. Given that large vision language models (VLMs) have shown progress on many tasks that require high level, human-like reasoning, we ask the question: if the models are taught the requisite low-level capabilities, can they compose them in novel ways to achieve interesting high-level tasks like cleaning the table without having to be explicitly taught so? To this end, we present ClevrSkills - a benchmark suite for compositional reasoning in robotics. ClevrSkills is an environment suite developed on top of the ManiSkill2 simulator and an accompanying dataset. The dataset contains trajectories generated on a range of robotics tasks with language and visual annotations as well as multi-modal prompts as task specification. The suite includes a curriculum of tasks with three levels of compositional understanding, starting with simple tasks requiring basic motor skills. We benchmark multiple different VLM baselines on ClevrSkills and show that even after being pre-trained on large numbers of tasks, these models fail on compositional reasoning in robotics tasks.

Figures

Figures reproduced from arXiv: 2411.09052 by the authors.

Figure 1
Figure 1. The ClevrSkills environment suite includes support for multi-modal prompts as task [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Example task compositions in ClevrSkills. Higher level tasks in ClevrSkills are built on [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Left: The median length of an episode across task levels showing significant increase in episode length as we go from lower to higher levels of compositionality. Right: The mean number of solvers used by the oracle to complete a task across task levels. Each solver solves for a specific sub-task, showing higher levels have increasingly compositional tasks. subsets to achieve more complicated goals, such as balancing… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: The StreamRoboLM model in contrast to state of the art models, [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Per task success rate on L0 tasks. We use the open-source imple￾mentations for JAT, Octo and RoboFlamingo to evaluate the models on ClevrSkills. We ini￾tialize both the models from re￾leased checkpoints and fine-tune them on the ClevrSkills data on each task level sepa…
Figure 6
Figure 6. Figure 6: Left: Task-wise success rate of StreamRoboLM (opt) on L1 tasks. Right: Task-wise success rate of StreamRoboLM (opt) on L2 tasks. D Training Details For JAT, Octo and RoboFlamingo, we use the official open-source code bases to run the experiments on ClevrSkills data. We…
Figure 7
Figure 7. Figure 7: Language-only counterpart of multi-modal prompts achieved by adding simple descriptions [PITH_FULL_IMAGE:figures/full_fig_p026_7.png]
Figure 8
Figure 8. Figure 8: We plot the action trajectories for 100 randomly sampled episodes each for three different [PITH_FULL_IMAGE:figures/full_fig_p027_8.png]

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    Match the pose of the end effector in {ks:keystep_1}, {ks:keystep_2} followed by {ks:keystep_3}

    Match pose: • Prompts: (a) "Match the pose of the end effector in {ks:keystep_1}, {ks:keystep_2} followed by {ks:keystep_3}". • Description: The image placeholder {ks:keystep_1} is the goal image showing the pose of the robot that it needs to achieve. The number of goals can v...

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    • Description: Similar to 1, but with an additional constraint of avoiding objects sus- pended in air

    Move without hitting: • Prompts: (a) Match the pose of the end effector in {ks:keystep_1} without hitting any objects. • Description: Similar to 1, but with an additional constraint of avoiding objects sus- pended in air. The number of obstacles may vary from 1 to 5. • Success...

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    13 (b) Grab the {obj:object}

    Pick: • Prompts: (a) Pick up the {obj:object}. 13 (b) Grab the {obj:object}. (c) Lift the {obj:object}. (d) Pick up the object with {tex:object} texture. (e) Grab the object with {tex:object} texture. (f) Lift the object with {tex:object} texture. • Description: The robot is r...

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    (b) Put object with tex:object 1 texture on object with tex:object2 texture

    Place: • Prompts: (a) Put {obj:object} 1 on {obj:object}2. (b) Put object with tex:object 1 texture on object with tex:object2 texture. • Description: The agent starts off with an object ({obj:object}1) attached to the end- effector and is tasked to place it on the specified o...

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    (b) Push object with {tex:object} 1 texture towards object with {tex:object}2 texture

    Push: • Prompts: (a) Push {obj:object} 1 towards {obj:object}2. (b) Push object with {tex:object} 1 texture towards object with {tex:object}2 texture. • Description: The agent is tasked to push the specified object {obj:object}1 towards another object {obj:object}2. • Success ...

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    (b) Rotate object with {tex:object} 1 {angles} degrees {direction}

    Rotate: • Prompts: (a) Rotate {obj:object} 1 {angles} degrees {direction}. (b) Rotate object with {tex:object} 1 {angles} degrees {direction}. • Description: The placeholder {obj:object} 1 specifies the object to be rotated by {angles} in {direction}. The angles can take value...

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    (b) Hit {obj:object} 2 with {obj:object}1

    Throw: • Prompts: (a) Throw {obj:object} 1 to {obj:object}2. (b) Hit {obj:object} 2 with {obj:object}1. • Description: The agent is tasked to throw {obj:object}1 to {obj:object}2. {obj:object}1 is initialized at the end-effector so the robot does not need to first pick it up. ...

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    (b) Hit {obj:object} 2 with {obj:object}1 such that {obj:object}2 falls over

    Throw topple: • Prompts: (a) Throw {obj:object} 1 to {obj:object}2 such that {obj:object}2 falls over. (b) Hit {obj:object} 2 with {obj:object}1 such that {obj:object}2 falls over. • Description: Similar to 7, with the additional constraint that the target object must topple o...

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    • Description: The agent is tasked to gently touch a specified object without moving it

    Touch: • Prompts: (a) Touch {obj:object} 1. • Description: The agent is tasked to gently touch a specified object without moving it. The task is supposed to teach the agent to control the force with which it carries out the task. • Success Criteria: The specified object must b...

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    • Description: Similar to 9, but now the specified object must move from its original position without the agent ever grasping it or the object toppling over

    Touch push: 15 • Prompts: (a) Touch and push {obj:object} 1. • Description: Similar to 9, but now the specified object must move from its original position without the agent ever grasping it or the object toppling over. • Success Criteria: The specified object must move at lea...

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    • Description: Similar to 9, but now the object must topple over

    Touch topple: • Prompts: (a) Touch and topple {obj:object} 1, {obj:object}2. • Description: Similar to 9, but now the object must topple over. • Success Criteria: The specified object should be touched and toppled over i.e. there should be at least 45 degree change in the vert...

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    • Description: The agent is tasked to touch goal positions specified by green spheres suspended in air

    Trace: • Prompts: (a) Trace the sequence of goals by moving to the next green goal. • Description: The agent is tasked to touch goal positions specified by green spheres suspended in air. Once a goal is touched, it turns to yellow and another goal turns green. The agent can on...

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    (b) Put object with {tex:object} 1 texture on object with {tex:object}2 texture

    Simple manipulation: • Prompts: (a) Put {obj:object} 1 on {obj:object}1. (b) Put object with {tex:object} 1 texture on object with {tex:object}2 texture. • Description: The tasks combined pick and place skills. The agent is tasked to pick a specified object {obj:object}1 and p...

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    • Description: Given a specified object {obj:object} 1 and a set of goal states {ks:keystep1}, the agent is tasked to achieve the goal states for the specified object in order

    Follow order: • Prompts: (a) Follow the motion for {obj:object} 1: {ks:keystep1}. • Description: Given a specified object {obj:object} 1 and a set of goal states {ks:keystep1}, the agent is tasked to achieve the goal states for the specified object in order. There may be multi...

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    • Description: Similar to Task 2 in L1, with the additional constraint that the specified object must be returned to it’s original state

    Follow order and restore: • Prompts: (a) Follow the motion for {obj:object} 1: {ks:keystep1} and then restore. • Description: Similar to Task 2 in L1, with the additional constraint that the specified object must be returned to it’s original state. • Success Criteria: All the ...

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    The direction can take values of north, south, east and west

    Neighbour: • Prompts: (a) First put {obj:object} 1 in {obj:object} 2 and then put the object that was at its {direction} in the same {obj:object}2 • Description: The agent is tasked to pick and place {obj:object}1 in {obj:object}2 and then pick and place the neighbour of {obj:...

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    daxer", “blicker

    Novel Adjective: 17 • Prompts: (a) {obj:object}1 is {adjective} than {obj:object}2. Put the {adjective} {obj:object}3 on {obj:object}4. • Description: The task is similar to Task 1 in L1, however instead of directly specifying the object with an image, the object is specified ...

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    dax", “blicket

    Novel Noun: • Prompts: (a) {obj:object} 1 is {noun}1 and {obj:object}2 is {noun}2. Put {noun}1 on {noun}2. • Description: This is similar to Task 5 in L1, however instead of an adjective the object is specified by a random noun. The {noun} can take values “dax", “blicket", “wu...

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    This is a {noun}2 {obj:object}2

    Novel Adjective and Noun: • Prompts: (a) This is a {noun}1 {obj:object}1. This is a {noun}2 {obj:object}2. {obj:object}3 is {adjective} than {obj:object}4, {obj:object}5 is {adjective} than {obj:object}6. Put the {adjective} {noun}1 on {noun}2 • Description: This task is the c...

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    • Success Criteria: All the objects in the scene are placed at the positions specified in the scene image {ks:scene}

    Rearrange: 18 • Prompts: (a) Rearrange to {ks:scene} • Description: Here, the agent is tasked with rearranging the scene to object configura- tion shown in the scene image {ks:scene}. • Success Criteria: All the objects in the scene are placed at the positions specified in the...

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    Rearrange and restore: • Prompts: (a) Rearrange to {ks:scene} and then restore. • Description: This is similar to Task 8 in L1 with an additional constraint that after rearranging to the specified scene, the agent must bring the objects to their initial positions i.e. rearrang...

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    • Success Criteria: The specified object is rotated in the correct direction within 5 degrees of the specified angle

    Rotate and restore: • Prompts: (a) Rotate {obj:object} {angle} degrees {direction} and then restore • Description: This task is similar to Task 6 in L0 with an additional constraint that once the rotation is complete, the agent needs to restore the object to its starting posit...

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    • Success Criteria: All the objects with the specified texture are rotated by {angle} degrees in the correct {direction}

    Rotate symmetry: 19 • Prompts: (a) Rotate objects with {tex:object} texture {angle} degrees {direction} (b) Rotate identically textured objects {angle} degrees {direction} • Description: The task is similar to Task 6 in L0, however the object is specified using texture so the ...

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    • Success Criteria: The objects in the scene are stacked in the correct order

    Stack: • Prompts: (a) Stack {obj:object} 1 on {obj:object}2, and {obj:object}3 on {obj:object}1 (b) Stack object with {tex:object}1 texture on object with {tex:object}2 texture, object with {tex:object}3 texture on object with {tex:object}1 texture (c) Stack objects as in {ks:...

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    • Success Criteria: The objects in the scene are stacked in the reverse order of what is specified

    Stack reversed: • Prompts: (a) Stack {obj:object}1, {obj:object}2, {obj:object}3, {obj:object}4 in the reversed order • Description: The task is similar to Task 12 in L1 however the agent is tasked to stack the specified objects in the reverse order. • Success Criteria: The ob...

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    • Success Criteria: All the objects are placed in the areas which have same texture as the objects

    Sort: • Prompts: (a) Place the objects in the identically textured areas • Description: In this task, the agent is required to place objects in the areas with similar texture as the object. • Success Criteria: All the objects are placed in the areas which have same texture as ...

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    The only way to achieve the result is to move one of the object away and then place the other object in its place and then repeating the same with the initial object

    Swap: • Prompts: (a) Swap positions of {obj:object} 1 and {obj:object}2 • Description: The agent is tasked to swap the positions of two objects as specified in the prompt. The only way to achieve the result is to move one of the object away and then place the other object in i...

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    The weight of the objects is proportional to their size

    Balance: • Prompts: (a) Place all the objects on the scale while keeping it in balance • Description: The agent is tasked to balance a weight scale by placing the objects with appropriate weights on either side. The weight of the objects is proportional to their size. We initi...

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    The agent here is required to sort the objects according to their texture however, instead of just placing the objects on an area, it is tasked to stack them on top of each other

    Sort Stack: • Prompts: (a) Stack identically textured objects (b) Place identically textured objects on top of each other • Description: The task is a composition of Task 12 and Task 14 in L1. The agent here is required to sort the objects according to their texture however, i...

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    • Success Criteria: The objects are first stacked as specified in the prompt and then are toppled such that all the objects end up on the ground

    Stack topple: 21 • Prompts: (a) Stack {obj:object}1 on {obj:object}2, and {obj:object}3 on {obj:object}1 and then topple the stack (b) Stack object with {tex:object}1 texture on object with {tex:object}2 texture, object with {tex:object}3 texture on object with {tex:object}1 t...

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    • Success Criteria: The positions of the two objects are swapped without the objects being grasped

    Swap with push: • Prompts: (a) Swap positions of {obj:object} 1 and {obj:object}2 by pushing • Description: The task is similar to Task 15 in L1 but instead of swapping by pick and place skills, the agent is tasked to do the same by pushing the objects. • Success Criteria: The...

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    • Success Criteria: The positions of the two objects are swapped and the objects are rotated by {angles} degrees in {direction} with respect to their initial pose

    Swap and rotate: • Prompts: (a) Swap positions of {obj:object} 1 and {obj:object} 2 but rotate them by {angle} degrees {direction} • Description: The task is similar to Task 15 in L1 with an additional constraint that the objects must be rotated by {angles} degrees in {directi...

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    thrown” in the areas instead of “placed

    Throw sort (sort by throwing): 22 • Prompts: (a) Place the objects in the identically textured areas by throwing • Description: The task is similar to Task 14 in L1 but instead of sorting by picking and placing, the agent needs to throw the objects into specified areas. To for...

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    URL https://www.amazon.de/ Thinking-Fast-Slow-Daniel-Kahneman/dp/0374275637/ref=wl_it_dp_o_pdT1_ nS_nC?ie=UTF8&colid=151193SNGKJT9&coliid=I3OCESLZCVDFL7

    ISBN 9780374275631 0374275637. URL https://www.amazon.de/ Thinking-Fast-Slow-Daniel-Kahneman/dp/0374275637/ref=wl_it_dp_o_pdT1_ nS_nC?ie=UTF8&colid=151193SNGKJT9&coliid=I3OCESLZCVDFL7

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.