Teach and Grow: An Agent-Centered Architecture for General Robot Learning

English reading report: Method, equations, original figures, experiments and reproducibility.

Authors: Chang Nie, Zhe Liu, and Hesheng Wang

Affiliations: School of Automation and Intelligent Sensing, Shanghai Jiao Tong University; Shanghai Key Laboratory of Navigation and Location Based Services

Source: 2608.17209 ↗ · Catalog record

Reading: 66 / 558 · 5 original figures & tables · ~18 min ·

1. Paper overview

In one sentence: TGL grows explicit, verified robot skills under fixed foundation weights, trading repeated agent-and-tool interaction for local capability updates whose broad scaling benefit remains untested. e-identity e-problem e-cycle e-contract e-pilot e-scaling

At a glance	What to know
Research problem	Author claim The authors call the recurring collection, optimization and regression burden of repairing uncovered robot behavior the retraining tax. TGL asks whether a local failure can instead lead to an inspectable, independently validated capability addition. Its motivation is embodied coverage: semantic recognition alone does not establish collision-free motion, stable contact or successful execution. e-problem
Core mechanism	Source description A teaching–composition–execution–growth cycle joins semantic reasoning to robot-native executors, while separate stores preserve executable skills and contextual lessons. e-cycle e-state
A key reported result	Related-task fixed-executor library growth: Eight blocks: 4/6, interval [0.30, 0.90]; median acquisition 1.5 attempts. Evaluation successes; 95% Wilson interval; capped acquisition attempts. Two LIBERO-Object tasks; onboarding states 12–14, disjoint evaluation states 15–17; seed 0. Executor, runtime, success criterion and three-attempt budget are fixed; library scope is frozen before evaluation. Six blocks: 0/6, interval [0.00, 0.39]; median 2.5 attempts. Reported two-sided Fisher exact p=0.061. Adding acquisition and release blocks changes this executor's reachable behavior. The two-task, single-seed pilot has wide uncertainty and does not establish benchmark-wide superiority. e-pilot
Reading caution	Reader analysis The abstract and Section V assert state-of-the-art standard LIBERO performance, but the supplied PDF provides no corresponding suite-level scores, named baseline comparison or full benchmark protocol. Table I and Appendix I document controlled studies only; the superiority claim cannot be quantitatively audited. e-benchmark-gap

Core contributions

Source description
A teaching–composition–execution–growth cycle joins semantic reasoning to robot-native executors, while separate stores preserve executable skills and contextual lessons. e-cycle e-state
Source description
Skill Blocks retain subgoal strategy and verification contracts while rebuilding object bindings, geometry and motion from current observations. e-contract e-induction
Author claim
The proposed scaling resource is verified reusable experience, with power-law hypotheses for future error and teaching demand. No longitudinal fit is reported. e-scaling e-longitudinal

Four-stage Teach-and-Grow diagram: demonstrations yield shared changes and blocks; an agent composes and checks execution; verified blocks and experience feed the next task. — **Figure 2.** Teaching seeds the route; physical feedback and two persistent stores determine what the next task inherits. Original paper, p. 2 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

2. Motivation

2.1 The problem and the proposed response

Author claim

The authors call the recurring collection, optimization and regression burden of repairing uncovered robot behavior the retraining tax. TGL asks whether a local failure can instead lead to an inspectable, independently validated capability addition. Its motivation is embodied coverage: semantic recognition alone does not establish collision-free motion, stable contact or successful execution. e-problem

2.2 What this reading follows

A robot can understand a request yet still lack the grasp, path or recovery needed to carry it out. Teach-and-Grow Learning organizes that gap around a reusable Skill Block: a meaningful physical change with an applicability scope, current-scene grounding, an executor and an outcome test. A high-level agent composes blocks and changes its route after observing what actually happened. The supplied v1 paper supports this organization with small LIBERO studies of decomposition, persistence and library expansion. Read the architecture as a proposed general system, the controlled counts as narrow empirical evidence, and the lifetime scaling curves as hypotheses awaiting longitudinal tests. e-identity e-problem e-cycle e-contract e-pilot e-scaling

3. Research context

We place the paper in the collection through its world–action interface. The catalog labels and the reading’s assessment are shown separately.

Catalog dimension	Recorded classification
Major category	Foundational work
Architecture	Not applicable
Prediction paradigm	Not applicable
Quadrant	Not applicable

This table preserves the labels recorded at reading time. The current major category is Related resources. View the current classification.

3.1 Evidence-based assessment

Supports the recorded classification

Reader analysis

The foundational theory/planning classification and not-applicable WAM quadrant are supported: TGL organizes heterogeneous executors through explicit contracts and memory. It does not specify one shared world/action model, joint future/action generation or inverse dynamics. WAMs are optional executors or future students. The work is an architecture paper with small experiments, so its survey/resource tag should not imply a systematic survey. e-taxonomy e-contract e-state

This is the collection’s architectural analysis, not a new related-work survey. Benchmark comparisons and their protocols appear in Section 6.

4. Problem formulation

4.1 Inputs and outputs

Inputs	Outputs
Task language and sparse successful demonstrations Current agent-view and wrist evidence; RGB-D and robot state for grounding Registered Skill Blocks, Experience Memory, allowed tools and verifier/planner evidence	Executor-generated robot actions and checked semantic effects Revised task compositions, admitted Skill Blocks and structured experience records

4.2 Equations and their role

\theta_{n+1}=\theta_n=\theta,\qquad (\mathcal{B}_{n+1},\mathcal{M}_{n+1})=\mathcal{U}(\mathcal{B}_n,\mathcal{M}_n,\mathcal{D}_n,\Delta\mathcal{H}_n)

Equation (3): at task n, pretrained parameters θ remain fixed. The update operator U changes Skill Library B and Experience Memory M using teaching set D and useful execution lessons ΔH. This is external capability growth, distinct from training the proposed student's separate parameters ϕ. e-state

b_i:=\langle g_i,\mathcal{S}_i,\rho_i,\gamma_i,\Pi_i,v_i,\mathcal{R}_i\rangle

Equation (4): block i carries goal g, scope S, reusable strategy ρ, grounding function γ, executor set Π, outcome test v and bounded recoveries R. Grounding maps observations to current block variables; the outcome test returns pass, fail or inconclusive. e-contract

\mathcal{E}_{\mathrm{future}}(X)=\mathcal{E}_{\infty}+AX^{-\alpha},\qquad D_{\mathrm{teach}}(X)=D_{\infty}+BX^{-\beta}

Equation (1) is a hypothesis. X>0 is effective reusable experience, computed in Eq. (7) by summing preregistered experience weights ω between 0 and 1. E_future is expected related-future-task error; D_teach is teacher intervention time under a fixed success rule and budget cap. Nonnegative floors E_∞ and D_∞ and positive A, B, α, β are not fitted here. e-scaling e-longitudinal

5. Method in detail

5.1 Turn a demonstration into a scoped acquisition contract

Source description

Consider the alphabet-soup-can example. The teaching evidence first identifies a change worth naming: the requested object becomes stably held. In the general induction procedure, demonstrations are aligned by that effect even if their paths or timing differ. Shared object roles, relations and approach structure become the candidate strategy; coordinates and control commands remain runtime variables. Table III then supplies a deliberately narrow acquisition contract, paired with a relation-aware release block. A later scene must be sensed again, and successful motion must be followed by retention evidence. Candidates are tested on separated cases before admission, with scope narrowed when necessary. This is the source's general recipe. The reported image-decomposition experiment is deterministic, so its semantic-role accuracy cannot be treated as proof that a multimodal agent independently discovered and implemented every part of the contract. e-induction e-block e-decomposition

Skill Block fields surround a central block; a current-scene observe-ground-execute-verify sequence branches to pass, inconclusive and fail outcomes. — **Figure 3.** A stored semantic contract is instantiated through fresh geometry, action and verification. Original paper, p. 6 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

5.2 Let verified effects govern the next decision

Source description

At task time, the working block sequence is provisional. The agent chooses a relevant block and executor, but the executor supplies continuous physical control and returns observations plus outcome evidence. That separation matters in the bowl-on-plate trace: successful picking did not provide sufficient post-grasp evidence for placement, so the agent rebuilt the remaining route. In the drawer-opening trace, an inconclusive result instead prompted another observation. These are different responses to different evidence states. Pass, fail and inconclusive therefore have operational consequences, beyond merely annotating a finished episode. The reported online traces allow at most one evidence refresh and two replans without human in-episode edits. Those limits help specify the examples, while the selection of two successful traces leaves the frequency and average benefit of adaptive recomposition unresolved. e-contract e-runtime e-runtime-traces e-stack

Populated acquisition contract for an alphabet-soup can, listing object-specific scope, current-scene grounding, executors, outcome test, recovery and excluded replay data. — **Table III.** The concrete learned block keeps a can-acquisition strategy and an explicit limit on where it applies. Original paper, p. 13 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

5.3 Separate demonstrated growth from a lifetime scaling law

Reader analysis

Reader analysis: the experiments form a useful sequence of increasingly demanding questions. Can teaching yield usable blocks? The separated-state study answers yes on its small sample. Can the result persist? Save-and-reload preserves those successes, although the old route already solves the same states. Can a larger library help a fixed executor elsewhere? The separate six-versus-eight-block pilot improves the observed count. None of these observations establishes a predictable lifetime curve. That requires checkpoints scored before future outcomes, held-out tasks and tests of retrieval, grounding and execution retention. The cost hypothesis adds another condition: local validation and linking must stay bounded as the library grows. If blocks interfere or compatibility checking expands globally, adding more stored capability need not reduce acquisition burden in the way the schematic curves suggest. e-persistence e-pilot e-retention e-longitudinal e-cost

Agent, persistent skill and experience stores, and a proposed fast policy above two schematic declining curves for future-task error and teaching demand. — **Figure 4.** The proposed lifetime resource is experience that remains usable, rather than the number of episodes stored. Original paper, p. 8 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

5.4 Training and inference

During training

Source description

Task acquisition updates explicit library and memory state without optimizing the fixed foundation stack. The paper supplies no task-time neural loss or optimizer. Later training of a separate fast student from verified trajectories is a proposed extension, not a demonstrated training stage. e-state e-scaling e-stack

During inference

Source description

Appendix I names gpt-5.6-sol, seed 0 and structured decisions for the online traces, with at most one evidence refresh and two replans and no human in-episode edits. Appendix J names Contact-GraspNet, MPLib and bounded controllers. These implement grasping and collision-aware motion beneath agent decisions; no joint future-video/action predictor is specified. e-stack e-taxonomy

5.5 Implementation flow

Abstract the taught change
The general method segments demonstrations at semantic effects, aligns equivalent roles and relations, and synthesizes shared strategy. Timing, pixels, poses and paths remain instance variables. Scope broadens only with supporting variation. The reported visual decomposition study uses a deterministic procedure, so it does not establish autonomous multimodal-agent induction. e-induction e-decomposition
Bind a contract to the scene
A block declares goal, applicability, strategy, grounding, compatible executors, outcome test and recovery. The populated alphabet-soup-can block is object-specific. It selects a current grasp, servos acquisition and plans a collision-aware transition; fresh retention evidence closes the subgoal. e-contract e-block
Execute and reconsider
The agent selects a block and executor at semantic boundaries. Continuous motion stays inside the executor. Pass permits continuation; inconclusive evidence can trigger observation; failure permits bounded repair, another executor, recomposition, teaching or stopping. The remaining route can change after each effect. e-runtime e-contract
Admit and remember
Separated validation cases test scope and matched old behavior before library admission. Experience records retain task, context, blocks, outcome, diagnosis, repair and evidence. Storage persistence is only one requirement: retrieval, grounding and behavioral retention also need testing. e-state e-induction e-retention

6. Experiments & results

Teach-and-Grow Learning (TGL) acquires executable, scoped Skill Blocks while leaving pretrained model weights fixed. An agent composes those blocks, checks physical effects and preserves lessons in memory. Small LIBERO studies support local acquisition and persistence; the paper's broader benchmark superiority and lifetime scaling predictions remain insufficiently quantified or untested.

Source and visual limitations

Reader analysis

Table I is the only empirical results table and also contains the fixed-executor library-size comparison used here as the ablation visual. The supplied PDF lacks a standard-LIBERO suite-score/baseline table and a matched feedback- or memory-removal experiment. The decomposition and failure diagnostics are numerical prose in Appendix I, not separate plots. Figures 4 and 5 are explicitly schematic or conceptual; they cannot supply measured scaling results. e-benchmark-gap e-pilot e-decomposition e-failure e-scaling e-cost

6.1 Read the original evidence

No results visual is included in this edition. The source-specific visual limitations are recorded above.

6.2 Results and evaluation conditions

Task & protocol	Reported result	Comparison & interpretation
Related-task fixed-executor library growth Two LIBERO-Object tasks; onboarding states 12–14, disjoint evaluation states 15–17; seed 0. Executor, runtime, success criterion and three-attempt budget are fixed; library scope is frozen before evaluation.	Eight blocks: 4/6, interval [0.30, 0.90]; median acquisition 1.5 attempts. Evaluation successes; 95% Wilson interval; capped acquisition attempts	Six blocks: 0/6, interval [0.00, 0.39]; median 2.5 attempts. Reported two-sided Fisher exact p=0.061. Adding acquisition and release blocks changes this executor's reachable behavior. The two-task, single-seed pilot has wide uncertainty and does not establish benchmark-wide superiority. e-pilot
Task-specific induction and save-and-reload Three teacher trajectories from states 0–2; separated evaluation states 3–5; farther scope checks on states 6–8.	Learned two-block composition: 3/3 before persistence and 3/3 after save-and-reload. Successful evaluation initial states	Existing six-block route also achieves 3/3 on states 3–5. This adds a persistent alternative rather than improving the already-successful baseline. The learned route stops at missing effects on farther states; this is scope enforcement, not successful extrapolation. e-persistence
Visual demonstration decomposition Ten successful demonstrations, five per related LIBERO-Object condiment-transfer task; sampled agent-view and wrist images. Production excludes reward, success labels, hidden object state, actions and reference boundaries; annotations are used afterward.	40 predicted/reference stages; accuracy 1.000; F1 0.100 exact, 0.633 within one sampled frame, 0.900 within two; 20/20 acquisition/release effects confirmed. Ordered role/type accuracy; boundary F1; observable effect confirmations	Independent annotations; no competing decomposition method reported. Correct semantic roles coexist with weak exact boundary localization. Effect confirmation does not measure executed novel-task success. e-decomposition
Feedback-driven task execution Two representative successful traces from a frozen LIBERO-GOAL archive.	Bowl-on-plate replans after insufficient post-grasp evidence; drawer opening requests a fresh observation. Observed route changes	No matched no-feedback success-rate comparison is supplied. The traces illustrate feedback use but cannot estimate its average causal benefit. e-runtime-traces
Failure localization Separate eight-attempt acquisition cohort.	0/8 successes: two failures before motion planning, four involving path consistency or calibration, two involving gripper closure. Task successes and failure categories	No paired repaired-cohort result is supplied. Diagnostic information identifies physical bottlenecks; diagnosis alone does not demonstrate recovery. e-failure

6.3 Ablations and diagnostic examples

Read component removals and qualitative examples within their stated evaluation conditions.

Controlled-study summary with rows for task-specific persistence, feedback-driven traces and a fixed-executor comparison of six versus eight blocks. — **Table I.** The library-size comparison supplies the paper's clearest controlled test of local capability growth. Original paper, p. 7 ↗
Excerpt from the authors’ paper; cropped without altering the figure or table.

7. Analysis & limitations

7.1 What the evidence leaves open

Reader analysis

The abstract and Section V assert state-of-the-art standard LIBERO performance, but the supplied PDF provides no corresponding suite-level scores, named baseline comparison or full benchmark protocol. Table I and Appendix I document controlled studies only; the superiority claim cannot be quantitatively audited. e-benchmark-gap

Reader analysis

Experiments use one embodiment. The representative successful traces and compact pilots do not establish broad transfer, physical deployment reliability or long-term retention. Explicit storage does not prevent retrieval or grounding failures. e-limitations e-pilot e-runtime-traces e-retention

Source description

The authors identify sequential agent reasoning, tool use and observation time as a practical cost. Faster models and distilled execution are expectations, without measured latency improvements here. e-limitations e-scaling

Reader analysis

The scaling law and near-additive acquisition-cost model remain hypotheses. The latter requires bounded local teaching/grounding/validation/linking costs and at-most-linear cumulative retrieval; all-to-all compatibility checks could make linking quadratic. e-cost e-longitudinal

7.2 Questions for discussion

Does library growth preserve retrieval and execution of old skills as similar blocks accumulate?
How much benefit comes from the added executors versus adaptive agent composition?
Can a preregistered reusable-experience score predict unseen checkpoints better than raw episode count?

8. Reproducibility audit

8.1 Requirements and known gaps

Reader analysis

Reproduction needs the frozen LIBERO-GOAL trace archive, exact LIBERO-Object task identifiers and initial states, teacher trajectories, six/eight-block libraries, camera calibration, verifier thresholds and controller/planner configurations. Appendix I supplies splits and budgets but omits complete task names, prompts, software versions, hardware, compute and timing details. e-stack e-block e-decomposition e-persistence e-pilot

Reader analysis

A proposed longitudinal check should freeze tools, agent and inference budget, score reusable experience before future outcomes, and retain exhausted teaching budgets at their cap. Fit early checkpoints and predict held-out larger checkpoints, while separately testing storage, retrieval, grounding and execution retention. e-longitudinal e-retention

8.2 Proposed reproduction checks

The following checks are proposals motivated by the paper. They have not been run as part of this reading.

Check 1: Isolate which library addition improves the fixed executor

Reader-proposed check; not performed here. Recover the exact pilot tasks and frozen libraries, then compare four arms: the original six blocks, acquisition added alone, release added alone, and both added. Keep agent/executor versions, scope, runtime, seeds, success rules and the three-attempt cap identical, with onboarding and evaluation states disjoint. Extend to additional preregistered seeds and related held-out tasks, and retain exhausted acquisitions at the cap. Measure task success, acquisition attempts, verifier outcomes and matched old-task performance. A reproducible gain confined to the combined arm would support complementary composition; disappearing gains on new states or degraded old-task retrieval would limit the claim of reusable local growth. e-pilot e-block e-retention

Check 2: Test whether reusable experience predicts future acquisition

Reader-proposed check; not performed here. Run sequential acquisition with a frozen foundation agent, tool stack, inference budget and future-task distribution. Preregister the experience-weighting rule using only checkpoint-available evidence, alongside raw episode count as a competing resource. At each checkpoint, measure future error and teacher intervention time until the success rule or budget cap; include capped failures. Fit the proposed floor-plus-power-law curves on early checkpoints and compare held-out predictions against preregistered alternatives. Also record retrieval errors, retention and total acquisition cost. Poor extrapolation, no advantage over episode count, or growing interference would challenge the claimed scaling resource even if a few added blocks improve immediate success. e-scaling e-longitudinal e-retention e-cost

8.3 Reading coverage

Visual audit: All 17 supplied PDF pages were rendered and visually inspected, including the title/author/version block, Figures 1–6, Tables I–III, method equations and all appendices. Page 17 was inspected for every reported split, statistic, trace budget, agent name and stack component; pages 14–15 support the proposed scaling and cost checks. All five final original crops were separately viewed at their final dimensions and retain the relevant labels, axes and table rows without surrounding body prose. Only the supplied PDF was covered: no separate supplements, trace archives, code or executed reproductions were inspected.

PDF pages inspected for this edition: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17. Appendix coverage: reviewed.

Original figures and tables remain the work of the source’s authors. Extractions preserve their scientific content; any HTML wrapper layout is disclosed with each figure. The surrounding reading notes are our own.

Text reading scope & known omissions

Title, author affiliations and abstract (p. 1)
I. Introduction (pp. 1–3)
II. Related Work (pp. 3–4)
III. Teach-and-Grow Learning (pp. 4–5)
IV. General Agentic Robot Architecture (pp. 5–6)
V. Experiments and Results (pp. 6–7)
VI. Discussion and VII. Conclusion (pp. 7–8)
References (pp. 9–10)
Appendix A: Why End-to-End Scaling Remains Costly (p. 10)
Appendix B: Teach-and-Grow Learning versus Existing Learning Forms (pp. 11–12)
Appendix C: From Zero-Shot Agent Control to Teach and Grow (pp. 11–12)
Appendix D: Skill Blocks and Atomic Skills (pp. 12–13)
Appendix E: Detailed Agent-Led Skill Induction (pp. 12–13)
Appendix F: Agentic Execution, Memory, and Failure Learning (p. 13)
Appendix G: Scaling Laws, Cost, and the Teach-and-Grow Hypothesis (pp. 14–15)
Appendix H: A Whole-System View of the Robot Learning Ecosystem (pp. 15–17)
Appendix I: Experimental Details and Appendix J: Current System (p. 17)

Outside the original text pass

Text extraction does not reconstruct figure images; inspect the retained PDF for figures and equation/table layout.
Separate supplemental material availability has not been fully verified.
Separate supplemental material availability has not been fully verified; no separate supplement was supplied.
The extraction did not reconstruct figure images; this gap was addressed by visually inspecting all 17 PDF pages, all six figures and all three tables.
Code, raw demonstrations, frozen trace archives and executable libraries were not supplied or inspected; no experiments were reproduced.
Only the supplied arXiv:2608.17209v1 PDF dated 17 August 2026 was reviewed. Its title and three authors match the catalog; no revision or edition discrepancy was observed, and other versions were not compared.

The visual audit above records the subsequent illustrated pass.

8.4 Traceable evidence

e-identityPDF p. 1, title, author line, affiliation footnote and arXiv marginInspect

The observed title is Teach and Grow: An Agent-Centered Architecture for General Robot Learning. Authors are Chang Nie, Zhe Liu, and Hesheng Wang. The margin identifies arXiv:2608.17209v1 [cs.RO], 17 August 2026. All authors share the School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, and Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai 200240, China. Identifier, title and authors agree with the supplied catalog.

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e-problemPDF p. 1, Section I; p. 10, Appendix A.C–EInspect

The paper motivates local capability updates by the cost of collecting embodied evidence, optimizing a monolithic policy and checking old behavior after a local coverage failure. It acknowledges that adapters and continual-learning methods can reduce this burden.

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e-cyclePDF p. 2, Figure 2 and Section IInspect

Demonstrations reveal shared semantic changes; the agent composes, executes a block and checks its effect. Verified capability enters the Skill Library, while outcomes, failures and repairs enter Experience Memory; both carry into later tasks.

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e-statePDF p. 4, Section III.A, Eqs. (2)–(3), and Section III.C, Eq. (6)Inspect

Foundation parameters remain fixed while teaching and execution lessons update library and memory. History retains evidence. Memory records task, context, blocks, outcome, diagnosis, repair and evidence. A later student has separate parameters.

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e-contractPDF p. 4, Section III.B, Eqs. (4)–(5); p. 6, Figure 3Inspect

A Skill Block includes goal, scope, strategy, grounding, executors, verifier and recovery. The working route can change. Figure 3 distinguishes retained contract fields from recomputed scene geometry and separates pass, inconclusive and fail outcomes.

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e-inductionPDF p. 5, Section IV.A–B, Eqs. (8)–(9); pp. 12–13, Appendix E.A–FInspect

The general induction procedure normalizes source evidence, segments and aligns semantic effects, separates invariants from current-scene variables, narrows scope as needed and tests candidates on separated cases before normal retrieval. Appendix E.F includes preservation of matched old behavior.

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e-runtimePDF p. 5, Section IV.C, Eq. (10); p. 6, Section IV.DInspect

The selected executor runs a block and returns a new observation plus executor/verifier evidence. The agent reasons at semantic transitions, with reobservation, executor substitution, route changes, bounded recovery, teaching and stopping available.

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e-blockPDF p. 13, Table III, all rows; p. 12, Appendix D.CInspect

The acquisition block induced from three teacher trajectories applies specifically to an alphabet-soup can. It grounds current identity, frame, confidence, obstacles, reachability, grasp and motion parameters, verifies retention and displacement, and excludes demonstration-coordinate/action replay. The release block recomputes opening frame, release band and held-object offset.

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e-stackPDF p. 17, Appendix I.A and Appendix JInspect

Online traces name gpt-5.6-sol, seed 0, structured decisions, at most one evidence refresh and two replans, with no human in-episode edits. Inputs include task, agent-view/wrist evidence, registered blocks, allowed routes and verifier/planner evidence. The current stack names Contact-GraspNet, MPLib, RGB-D, robot state and bounded controllers. Distillation is described as the proposed next step.

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e-decompositionPDF p. 6, Section V.a; p. 17, Appendix I.BInspect

The reported visual decomposition is deterministic. Ten successful demonstrations span two condiment-transfer tasks, five each; production uses sampled camera images without rewards, success labels, hidden state, actions or reference boundaries. Against independent annotations: 40 predicted and 40 reference stages, role/type accuracy 1.000, boundary F1 0.100 exact, 0.633 within one sampled frame and 0.900 within two. Ten acquisition and ten release effects are confirmed.

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e-persistencePDF p. 7, Table I, task-specific learning row; p. 17, Appendix I.CInspect

Teaching states 0–2 yield two blocks. On states 3–5 they achieve 3/3, as does the old six-block route. Following admission to an eight-block library and save/reload, they again achieve 3/3. On states 6–8, the learned route stops at the missing semantic effect.

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e-pilotPDF p. 7, Table I, fixed-executor library pilot row; p. 17, Appendix I.DInspect

The two-task pilot fixes executor, runtime, seed 0, success criteria and three-attempt budget, changing only addition of acquisition/release blocks. Onboarding states are 12–14; disjoint evaluation states are 15–17. Six/eight blocks yield 0/6 and 4/6, Wilson 95% intervals [0.00,0.39] and [0.30,0.90], and reported two-sided Fisher p=0.061. Capped median acquisition attempts are 2.5 and 1.5.

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e-runtime-tracesPDF p. 6, Section V.b; p. 7, Table I, feedback row; p. 17, Appendix I.AInspect

Two representative successful LIBERO-GOAL archive traces illustrate route rebuilding for bowl-on-plate after insufficient post-grasp evidence and reobservation after inconclusive drawer-opening evidence. No matched no-feedback result is given.

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e-failurePDF p. 17, Appendix I.EInspect

A separate eight-attempt acquisition cohort has 0/8 task successes. Two failures occur before motion planning, four involve path consistency or calibration, and two involve gripper closure.

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e-retentionPDF p. 13, Appendix F.B–CInspect

Structured memory links diagnoses to evidence and permits human correction. Retention is explicitly divided into storage, retrieval, grounding and behavioral retention; the paper warns that persistence alone does not guarantee maintained behavior.

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e-scalingPDF p. 3, Eq. (1); p. 4, Section III.D, Eq. (7); p. 5, continuation of Section III.D; p. 8, Figure 4 and Section VI.D–EInspect

The proposed error and teaching-demand power laws use effective experience X, a sum of weights in [0,1] fixed before evaluation using reliability, coverage, retrieval, grounding and compatibility. Future outcomes are excluded from the score. Teaching demand is intervention time until a fixed success rule or cap. Figure 4 has separate schematic vertical scales; fast-policy distillation and lifetime scaling are proposed.

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e-longitudinalPDF pp. 14–15, Appendix G.D–EInspect

The proposed longitudinal study freezes agent, tools, inference budget, success rule, scoring rule and future-task distribution. It tests old tasks, new compositions, new instances and new behaviors. Capped failures remain in analysis, early fits predict larger held-out checkpoints, and alternative curves and matched systems are proposed. No longitudinal dataset, fitted exponents or empirical curve is supplied.

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e-costPDF p. 14, Appendix G.C, Eqs. (15)–(16); p. 15, Figure 5Inspect

The cost model counts matched semantic additions, with TGL paying infrastructure plus per-block teaching, grounding, validation and linking and cumulative retrieval. At-most-linear growth assumes bounded local costs and O(K) cumulative retrieval. All-to-all compatibility can make linking quadratic. Figure 5 labels the regimes conceptual.

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e-taxonomyPDF p. 12, Table II; pp. 15–17, Appendix H.B–E, including Figure 6 on p. 16; p. 17, Appendix JInspect

TGL is described as an organizing semantic-contract layer for learned policies, classical tools and memory. WAMs may implement blocks, mature routes or future students. World models may screen candidates. No shared learned world/action prediction architecture or inverse-dynamics mechanism is specified; dashed distillation and fleet-sharing routes are future extensions.

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e-limitationsPDF p. 8, Section VI.F; pp. 11–12, Appendix C.C; p. 12, Appendix E.CInspect

Sequential model calls, observation and tool use incur time costs. Faster agents, caching and future fast students are suggested. Appendix E.C states that the present study uses one embodiment and identifies cross-embodiment reuse as a future test.

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e-benchmark-gapPDF p. 1, abstract; p. 6, Section V opening; p. 7, Table I; p. 17, Appendix I.A–EInspect

The abstract and Section V assert state-of-the-art standard LIBERO performance. The supplied results table and experimental appendix instead report controlled decomposition, persistence, trace, library-pilot and failure-localization studies; no suite-level standard benchmark numbers or named-baseline score table accompany the assertion.

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8.5 Primary sources

Teach and Grow: An Agent-Centered Architecture for General Robot Learning ↗

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