Pith Number

pith:I62ZPU4K

pith:2022:I62ZPU4KQYYRUN6V32AXFAWQA6

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ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Animesh Garg, Ankit Goyal, Arsalan Mousavian, Danfei Xu, Dieter Fox, Ishika Singh, Jesse Thomason, Jonathan Tremblay, Valts Blukis

Structuring LLM prompts as executable programs lets robots generate valid task plans across any environment and capabilities.

arxiv:2209.11302 v1 · 2022-09-22 · cs.RO · cs.AI · cs.CL · cs.LG

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Claims

C1strongest claim

We present a programmatic LLM prompt structure that enables plan generation functional across situated environments, robot capabilities, and tasks.

C2weakest assumption

That prompting the LLM with program-like specifications of available actions and objects plus example programs will reliably prevent generation of actions impossible in the robot's current context.

C3one line summary

ProgPrompt generates situated robot task plans by prompting LLMs with program-like specifications of actions, objects, and executable examples, achieving state-of-the-art success in VirtualHome tasks and physical robot deployment.

References

39 extracted · 39 resolved · 1 Pith anchors

[1] Inner Monologue: Embodied Reasoning through Planning with Language Models 2022 · arXiv:2207.05608

[2] Language models as zero-shot planners: Extracting actionable knowledge for embodied agents 2022

[3] Socratic models: Composing zero-shot multimodal reasoning with language, 2022

[4] Do as i can, not as i say: Grounding language in robotic affordances, 2022

[5] Strips: A new approach to the application of theorem proving to problem solving, 1971

Formal links

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Cited by

21 papers in Pith

When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution

How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning

TaskGround: Structured Executable Task Inference for Full-Scene Household Reasoning

SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

Reasoning with Language Model is Planning with World Model

Receipt and verification

First computed	2026-05-17T23:38:15.145015Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

47b597d38a86311a37d5de817282d007bc9fa16c7dd855ee9382d36916c1353c

Aliases

arxiv: 2209.11302 · arxiv_version: 2209.11302v1 · doi: 10.48550/arxiv.2209.11302 · pith_short_12: I62ZPU4KQYYR · pith_short_16: I62ZPU4KQYYRUN6V · pith_short_8: I62ZPU4K

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/I62ZPU4KQYYRUN6V32AXFAWQA6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 47b597d38a86311a37d5de817282d007bc9fa16c7dd855ee9382d36916c1353c

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2022-09-22T20:29:49Z",
    "title_canon_sha256": "00e7c202978b484a0722730fa304d289f75814a81f0b7ccc7d0575915e88902c"
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