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arxiv: 2606.09476 · v1 · pith:UMWX7GHCnew · submitted 2026-06-08 · 💻 cs.RO

Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling

Carlos V\'elez Garc\'ia , Miguel Cazorla , Jorge Pomares This is my paper

Pith reviewed 2026-06-27 16:05 UTC · model grok-4.3

classification 💻 cs.RO
keywords goal-set hindsight relabelingGS-HERhindsight experience replayoffline goal-conditioned reinforcement learningrobot goal predicatesnuisance dimensionsqueryable goals
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The pith

Goal-Set Hindsight Relabeling lets one offline-trained checkpoint answer multiple goal predicates by taking a binary query on success variables at inference time.

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

Standard hindsight relabeling converts future states into exact goal states, which overconstrains offline robot learning when success depends on only a subset of state variables. GS-HER generalizes the relabeling step so that achieved states certify entire goal sets defined by a binary query that selects the relevant variables. The query becomes an inference-time input, leaving the underlying offline goal-conditioned reinforcement learning algorithm and its training procedure unchanged. This change improves performance on tasks where nuisance dimensions create bottlenecks for full-state goals and converts a single trained checkpoint into a reusable interface that can handle different goal predicates without retraining.

Core claim

GS-HER is a predicate-level generalization of HER in which achieved states certify query-defined goal sets rather than singleton goal states. A binary query specifies which variables define success, making the goal predicate an inference-time input while leaving the underlying offline GCRL algorithm unchanged. This improves performance when full-state goals are bottlenecked by nuisance dimensions and turns hindsight relabeling into a reusable goal interface: one checkpoint can answer multiple robot goal predicates without retraining.

What carries the argument

Goal-Set Hindsight Relabeling (GS-HER), which replaces exact-state goal relabeling with query-defined goal sets that achieved states can satisfy.

If this is right

  • Performance improves across OGBench tasks and five offline goal-conditioned learners when full-state goals are bottlenecked by nuisance dimensions.
  • Hindsight relabeling becomes a reusable goal interface.
  • One checkpoint can answer multiple robot goal predicates without retraining.
  • The underlying offline GCRL algorithm and its training procedure stay unchanged.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The separation of the success predicate from the learned policy could simplify switching between related tasks after deployment.
  • The same query mechanism might extend to other forms of experience relabeling that currently tie labels to full states.
  • It opens a path for policies to respond to partial or context-dependent success criteria without additional offline data.

Load-bearing premise

The binary query specifying success variables can be supplied at inference time without requiring any change to the underlying offline GCRL algorithm or its training procedure.

What would settle it

An experiment on the OGBench tasks with the five offline goal-conditioned learners in which GS-HER produces no performance gain over standard HER when nuisance dimensions are present, or in which applying the binary query requires retraining the model.

Figures

Figures reproduced from arXiv: 2606.09476 by Carlos V\'elez Garc\'ia, Jorge Pomares, Miguel Cazorla.

Figure 1
Figure 1. Figure 1: From many goal states to many goal predicates. HER-Full supports annotation-free relabeling but fixes success to full-state matching. HER-Task focuses learning through a fixed oracle projection ϕ, but fixes the task semantics before training. GS-HER avoids oracle task projections while conditioning on a query q, allowing the same model to recover full-state goals, task-focused goals, and compositional pred… view at source ↗
Figure 2
Figure 2. Figure 2: Full-state hindsight relabeling is bottlenecked by nuisance dimensions. OGBench success rate across base goal-conditioned learners and relabeling schemes. Gray circles denote HER-Full, black stars denote GS-HER Blockwise, and dashed ticks denote the oracle HER-Task projection. Segments connect HER-Full to GS-HER; green indicates improvement and red degra￾dation. Averaged over all manipulation backbone–task… view at source ↗
Figure 3
Figure 3. Figure 3: GS-HER learns task-aligned distance estimates. Along a successful cube-single-noisy-v0 rollout, GS-HER tracks the oracle task-distance estimate, while HER-Full remains saturated because exact-state goals still depend on nuisance variables beyond the official cube-position predicate. 4.3 One Model, Many Goal Predicates The official benchmark evaluates only one predicate per environment, but this understates… view at source ↗
Figure 4
Figure 4. Figure 4: shows that a single GS-HER checkpoint can answer a family of goal predicates by chang￾ing only the inference-time query. These include the official cube-position predicate, object-centric predicates such as cube yaw and cube pose, an airborne cube-position predicate, and robot-centric end-effector predicates. No model is retrained between predicates. All non-official goals are sam￾pled from a held-out vali… view at source ↗
Figure 5
Figure 5. Figure 5: compares this oracle-projection gain to the gain obtained by GS-HER. Each point is an OGBench task averaged over base learners. GS-HER gains are strongly aligned with the oracle-projection gain, indicating that query￾conditioned relabeling helps most in settings where full-state HER is bottlenecked by nui￾sance dimensions. Points near the diagonal show settings where GS-HER recovers the ben￾efit of the ora… view at source ↗
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
read the original abstract

Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends only on a subset of the state. We propose Goal-Set Hindsight Relabeling (GS-HER), a predicate-level generalization of HER in which achieved states certify query-defined goal sets rather than singleton goal states. A binary query specifies which variables define success, making the goal predicate an inference-time input while leaving the underlying offline GCRL algorithm unchanged. Across OGBench tasks and five offline goal-conditioned learners, GS-HER improves performance when full-state goals are bottlenecked by nuisance dimensions and turns hindsight relabeling into a reusable goal interface: one checkpoint can answer multiple robot goal predicates without retraining.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes Goal-Set Hindsight Relabeling (GS-HER) as a predicate-level generalization of hindsight experience replay (HER) for offline goal-conditioned reinforcement learning. Rather than relabeling achieved states to exact singleton goal states, GS-HER relabels them to goal sets defined by a binary query over success variables. This makes the goal predicate an inference-time input that leaves the underlying offline GCRL algorithm and training procedure unchanged. The method is evaluated on OGBench tasks across five offline goal-conditioned learners and is claimed to improve performance when full-state goals are bottlenecked by nuisance dimensions while enabling one trained checkpoint to answer multiple robot goal predicates without retraining.

Significance. If the central claims hold, GS-HER would provide a reusable goal interface that mitigates over-constraining in offline GCRL and increases the flexibility of trained checkpoints for multi-predicate robot tasks. The approach directly addresses a practical bottleneck in goal-conditioned learning when only a subset of state dimensions matters for success.

major comments (2)
  1. [Abstract] Abstract: the claim that 'the binary query specifying success variables can be supplied at inference time while leaving the underlying offline GCRL algorithm unchanged' is load-bearing for the reusable-interface result. Standard GCRL conditions policies and critics on full goal states; supporting arbitrary binary masks at inference requires either additional conditioning on the mask or a query-dependent goal encoding, both of which alter the input interface or training distribution relative to the baseline algorithm.
  2. [Abstract] Abstract: the reported 'consistent gains across OGBench and five learners' are stated without any quantitative results, error bars, or implementation details. This prevents verification that the gains are attributable to the goal-set formulation rather than other factors and undermines assessment of the central performance claim.
minor comments (1)
  1. The abstract is clear on the high-level motivation but would benefit from a single sentence distinguishing GS-HER from prior set-based or predicate-based relabeling methods.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on the manuscript. We address each major comment below with point-by-point responses.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'the binary query specifying success variables can be supplied at inference time while leaving the underlying offline GCRL algorithm unchanged' is load-bearing for the reusable-interface result. Standard GCRL conditions policies and critics on full goal states; supporting arbitrary binary masks at inference requires either additional conditioning on the mask or a query-dependent goal encoding, both of which alter the input interface or training distribution relative to the baseline algorithm.

    Authors: We agree that the original wording in the abstract overstates the case. GS-HER does introduce a query-dependent goal encoding to enable inference-time predicates, which modifies the input interface relative to standard GCRL baselines. The core training procedure of the underlying offline GCRL algorithm (actor-critic updates, etc.) remains unchanged, with the query used only for offline relabeling. We will revise the abstract to clarify this distinction and remove the claim that the algorithm is left entirely unchanged. revision: yes

  2. Referee: [Abstract] Abstract: the reported 'consistent gains across OGBench and five learners' are stated without any quantitative results, error bars, or implementation details. This prevents verification that the gains are attributable to the goal-set formulation rather than other factors and undermines assessment of the central performance claim.

    Authors: We acknowledge that the abstract presents the performance claim without supporting numbers. The full manuscript contains tables and figures with quantitative results, error bars, and implementation details for all five learners on OGBench. In the revision we will add concise quantitative highlights to the abstract where space permits, while ensuring they accurately reflect the full results. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper presents GS-HER as a direct conceptual generalization of hindsight experience replay to goal sets specified by binary queries at inference time. No equations, fitted parameters, or derivation steps are shown that reduce any claimed result to its own inputs by construction. The central assertion that the underlying offline GCRL algorithm and training procedure remain unchanged is a design statement about the method's interface, not a mathematical derivation or self-referential fit. The work is therefore self-contained as a proposed extension rather than a tautological renaming or self-citation load-bearing argument.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The proposal rests on standard assumptions of offline GCRL and HER; no free parameters, invented entities, or ad-hoc axioms are mentioned in the abstract.

axioms (1)
  • domain assumption Offline GCRL algorithms can be left unchanged while only the relabeling step is modified.
    Abstract states the underlying algorithm remains unchanged.

pith-pipeline@v0.9.1-grok · 5668 in / 1137 out tokens · 21518 ms · 2026-06-27T16:05:40.453906+00:00 · methodology

discussion (0)

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Reference graph

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