{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/danli-deliberative-agent-for-following","title":"DANLI: Deliberative Agent for Following Natural Language Instructions","arxiv_id":"2210.12485","date":"2022-10-22","proceeding":null,"authors":["Yichi Zhang","Jianing Yang","Jiayi Pan","Shane Storks","Nikhil Devraj","Ziqiao Ma","Keunwoo Peter Yu","Yuwei Bao","Joyce Chai"],"abstract":"Recent years have seen an increasing amount of work on embodied AI agents that can perform tasks by following human language instructions. However, most of these agents are reactive, meaning that they simply learn and imitate behaviors encountered in the training data. These reactive agents are insufficient for long-horizon complex tasks. To address this limitation, we propose a neuro-symbolic deliberative agent that, while following language instructions, proactively applies reasoning and planning based on its neural and symbolic representations acquired from past experience (e.g., natural language and egocentric vision). We show that our deliberative agent achieves greater than 70% improvement over reactive baselines on the challenging TEACh benchmark. Moreover, the underlying reasoning and planning processes, together with our modular framework, offer impressive transparency and explainability to the behaviors of the agent. This enables an in-depth understanding of the agent's capabilities, which shed light on challenges and opportunities for future embodied agents for instruction following. The code is available at https://github.com/sled-group/DANLI.","url_abs":"https://arxiv.org/abs/2210.12485v1","url_pdf":"https://arxiv.org/pdf/2210.12485v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"danli-deliberative-agent-for-following","repo_url":"https://github.com/sled-group/danli","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"instruction-following","task_name":"Instruction Following"},{"task_slug":"vision-language-navigation","task_name":"Vision-Language Navigation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.12485","atlas_url":"https://app.syntology.ai/?focus=2210.12485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12485"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/sled-group/DANLI","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/alexpashevich/E.T","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"summary":{"ran":2,"ran_violates":1,"unverified":3},"by_repo_kind":{"official":{"samples":6,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"eb415cd40f9c474e","entry":"ithor_oid_to_object_class","repo":"sled-group/DANLI","repo_kind":"official","path":"ithor_assets/object_definitions.py","file_url":"https://github.com/sled-group/DANLI/blob/HEAD/ithor_assets/object_definitions.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"eb415cd40f9c474e"}},{"code_sha256_prefix":"9c0c9c4786089460","entry":"object_intid_to_string","repo":"sled-group/DANLI","repo_kind":"official","path":"ithor_assets/object_definitions.py","file_url":"https://github.com/sled-group/DANLI/blob/HEAD/ithor_assets/object_definitions.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9c0c9c4786089460"}},{"code_sha256_prefix":"f20433e6b6a47e71","entry":"process_alive","repo":"sled-group/DANLI","repo_kind":"official","path":"start_x.py","file_url":"https://github.com/sled-group/DANLI/blob/HEAD/start_x.py","link_basis":"harvester_set","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f20433e6b6a47e71"}},{"code_sha256_prefix":"527a96e1b70deb8a","entry":"find_devices","repo":"sled-group/DANLI","repo_kind":"official","path":"start_x.py","file_url":"https://github.com/sled-group/DANLI/blob/HEAD/start_x.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"527a96e1b70deb8a"}},{"code_sha256_prefix":"aa6655b550db6453","entry":"generate_xorg_conf","repo":"sled-group/DANLI","repo_kind":"official","path":"start_x.py","file_url":"https://github.com/sled-group/DANLI/blob/HEAD/start_x.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"aa6655b550db6453"}},{"code_sha256_prefix":"a62edde9a2236b2f","entry":"object_str_to_intid","repo":"sled-group/DANLI","repo_kind":"official","path":"ithor_assets/object_definitions.py","file_url":"https://github.com/sled-group/DANLI/blob/HEAD/ithor_assets/object_definitions.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a62edde9a2236b2f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}