{"event":{"id":"evt-inner-monologue-20220712","dedupe_key":"closed_loop_embodied_language_planning:2022-07-12:inner-monologue-closes-language-model-planning-loops-with-environment-feedback","event_type":"closed_loop_embodied_language_planning","title":"Inner Monologue closes language-model planning loops with environment feedback","summary":"Researchers showed language-model planners incorporating success detection, scene descriptions and human interaction to replan in simulated and real robot tasks.","occurred_at":"2022-07-12T15:20:48.000Z","published_at":"2022-07-12T15:20:48.000Z","observed_at":"2026-10-02T15:12:29.000Z","reconstructed_at":"2026-10-02T15:12:29.000Z","ingest_type":"live_world_scan","locations":null,"status":"verified","confidence":0.96,"metadata":{"actors":["Google Research","collaborators"],"what_happened_at_time":"Language-model planning was repeatedly conditioned on feedback from the environment and people rather than operating only as an open-loop proposal mechanism.","evidence_at_time":"The July 12 arXiv preprint reports closed-loop evaluations in simulation, tabletop rearrangement and long-horizon kitchen manipulation.","affected_layers":["layer-models","layer-robotics","layer-agents","layer-research","layer-software"],"change_kind":"agent_precursor_maturation","q2_continuity":"Q2 SayCan and Gato established model-mediated action loops; Q3 evidence added explicit feedback-driven replanning.","retrospective_inference":"Agent-like embodied behavior was becoming more compositional and closed-loop without establishing general persistent autonomy.","uncertainty":"The work remained bounded by existing perception, skills and research environments."},"created_at":"2026-10-02T16:08:16.242Z","updated_at":"2026-10-02T16:08:16.242Z"},"artifacts":[{"id":"art-inner-monologue-20220712","canonical_url":"https://arxiv.org/abs/2207.05608","artifact_type":"paper_preprint","title":"Inner Monologue","summary":"The paper showed language-model planning that incorporates environment and human feedback for closed-loop replanning in simulated and real robotic tasks.","creator_entities":["Google Research and collaborators"],"released_at":"2022-07-12T15:20:48.000Z","content_hash":null,"metadata":{"historical_scope":"2022-Q3"},"created_at":"2026-10-02T16:08:16.234Z","updated_at":"2026-10-02T16:08:16.234Z","relation":"evidenced_by"}],"signals":[{"id":"sig-q3-embodied-closed-loop-agency","statement":"LLM-mediated robotics progressed toward closed-loop replanning and executable program synthesis over robot APIs, strengthening embodied agent-like capability without establishing general persistent autonomy.","signal_type":"embodied_agent_capability_maturation","direction":"increasing","confidence":0.95,"epistemic_status":"supported_inference","mapping_mode":"retrospective","reconstructed_at":"2026-10-02T15:12:29.000Z","created_at":"2026-10-02T16:08:16.251Z","updated_at":"2026-10-02T16:08:16.251Z"}]}