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arxiv: 2410.06347 · v2 · submitted 2024-10-08 · 💻 cs.RO · cs.AI

Goal-Conditioned Decision Transformer for Multi-Goal Offline Reinforcement Learning

Pawe{\l} Gajewski , Dominik \.Zurek , Marcin Pietro\'n , Kamil Faber This is my paper

Pith reviewed 2026-05-23 19:17 UTC · model grok-4.3

classification 💻 cs.RO cs.AI
keywords goal-conditioned reinforcement learningoffline RLdecision transformermulti-goal roboticsFranka Pandasparse rewardssequence modeling
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The pith

A goal-conditioned Decision Transformer learns multi-goal robotics policies from offline data alone.

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

The paper adapts the Decision Transformer to handle goal-conditioned policies in an offline reinforcement learning setting for robotics. It adds goal states directly into the input sequence so the model can address many different tasks from the same pre-collected dataset without further environment interaction. The method is tested on a newly released offline dataset collected on the Franka Emika Panda arm and is shown to exceed online reinforcement learning baselines on harder tasks while remaining stable under sparse rewards and small amounts of expert data. A reader would care because the approach removes the usual requirement for costly online trial-and-error when a robot must achieve multiple goals.

Core claim

By explicitly incorporating goal states into the sequence modeling framework, the Goal-Conditioned Decision Transformer solves varying tasks using only pre-collected data. On the Franka Emika Panda offline dataset the approach outperforms state-of-the-art online baselines in complex tasks and maintains robustness in sparse-reward settings even with limited expert demonstrations.

What carries the argument

The Goal-Conditioned Decision Transformer, which inserts goal states into the transformer sequence to produce actions conditioned on different target goals.

If this is right

  • Multi-goal robotic policies can be obtained without any online environment interaction after the initial data collection.
  • The same trained model handles both dense and sparse reward conditions across multiple target goals.
  • Performance remains high even when the offline dataset contains only limited expert trajectories.
  • The method extends standard Decision Transformer training by adding an explicit goal token to the sequence.

Where Pith is reading between the lines

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

  • Similar goal-conditioning could be added to other sequence-based offline RL algorithms beyond the Decision Transformer.
  • If the dataset coverage assumption holds, the approach may transfer to additional robot platforms once comparable offline multi-goal datasets are collected.
  • The offline multi-goal setting reduces the usual sample-efficiency barrier that prevents transformer models from being used directly on physical robots.

Load-bearing premise

The new offline dataset for the Franka Emika Panda contains enough coverage of different goals and task distributions for the learned policy to generalize.

What would settle it

Evaluating the trained policy on a set of goals that are sparsely or never visited in the offline dataset and observing consistent failure to reach them would falsify the generalization claim.

Figures

Figures reproduced from arXiv: 2410.06347 by Dominik \.Zurek, Kamil Faber, Marcin Pietro\'n, Pawe{\l} Gajewski.

Figure 1
Figure 1. Figure 1: Multi-goal robotic environments used for evaluation. From the left: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: A plot of the influence of the data size vs. success rate [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: A plot of the influence of the expert percentages vs. success rate [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

Reinforcement learning (RL) in robotics faces significant hurdles regarding sample efficiency and generalization across varying goals. While Offline RL mitigates the need for costly online interactions, its integration with goal-conditioned policies and transformer-based architectures remains underexplored. We introduce a Goal-Conditioned Decision Transformer adapted for offline multi-goal robotics. By explicitly incorporating goal states into the sequence modeling framework, our approach efficiently solves varying tasks using only pre-collected data. We validate this method on a newly released offline dataset for the Franka Emika Panda platform. Experimental results demonstrate that our approach outperforms state-of-the-art online baselines in complex tasks and maintains robustness in sparse-reward settings, even with limited expert demonstrations.

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 / 0 minor

Summary. The paper introduces a Goal-Conditioned Decision Transformer (GCDT) that extends the Decision Transformer architecture by explicitly conditioning on goal states within the sequence modeling framework for offline multi-goal reinforcement learning. It claims to solve varying robotic tasks using only pre-collected data, with validation on a newly released offline dataset for the Franka Emika Panda platform, reporting outperformance over state-of-the-art online baselines in complex tasks and robustness in sparse-reward settings even with limited expert demonstrations.

Significance. If the empirical claims hold with adequate controls, the work would provide a practical demonstration of combining transformer-based sequence modeling with goal conditioning in offline RL for robotics, potentially improving generalization across goals without online interaction. The release of a new Panda dataset could be a useful contribution if it demonstrates sufficient diversity.

major comments (2)
  1. [Dataset Description / Experiments] Dataset section: The central claim of robustness in sparse-reward multi-goal settings with limited expert demonstrations requires that the newly released Franka Emika Panda offline dataset supplies adequate goal variation and task distribution coverage. No quantitative metrics (e.g., number of unique goals, trajectories per goal, or state-space coverage statistics) are referenced to establish this precondition for generalization.
  2. [Experiments] Experimental results: The abstract and results claim outperformance over online baselines and robustness, yet the provided description supplies no quantitative metrics, baseline implementation details, number of evaluation runs, statistical tests, or ablation studies on the goal-conditioning component, making it impossible to verify whether the data supports the stated claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important areas for strengthening the presentation of our dataset and experimental results. We address each major comment below and will revise the manuscript to include the requested details.

read point-by-point responses

  1. Referee: [Dataset Description / Experiments] Dataset section: The central claim of robustness in sparse-reward multi-goal settings with limited expert demonstrations requires that the newly released Franka Emika Panda offline dataset supplies adequate goal variation and task distribution coverage. No quantitative metrics (e.g., number of unique goals, trajectories per goal, or state-space coverage statistics) are referenced to establish this precondition for generalization.

    Authors: We agree that quantitative metrics are necessary to substantiate claims about dataset diversity and coverage. In the revised manuscript, we will expand the dataset section with a table and accompanying text reporting the number of unique goals, trajectories per goal, total trajectories, and state-space coverage statistics (e.g., via histograms or variance measures across dimensions). This will directly support the robustness claims in sparse-reward multi-goal settings. revision: yes

  2. Referee: [Experiments] Experimental results: The abstract and results claim outperformance over online baselines and robustness, yet the provided description supplies no quantitative metrics, baseline implementation details, number of evaluation runs, statistical tests, or ablation studies on the goal-conditioning component, making it impossible to verify whether the data supports the stated claims.

    Authors: We acknowledge the absence of these details in the current version. The revised experiments section will include: concrete performance metrics with numerical values and comparisons, full baseline implementation details (hyperparameters, code references if available), number of evaluation runs with seeds, statistical significance tests (e.g., mean ± std over runs), and an ablation study on the goal-conditioning component. These additions will enable verification of the outperformance and robustness claims. revision: yes

Circularity Check

0 steps flagged

No derivation chain present; empirical method validated on external dataset

full rationale

The paper introduces a Goal-Conditioned Decision Transformer as an empirical architecture for offline multi-goal RL and reports experimental outperformance on a newly released Franka Emika Panda dataset. No equations, derivations, or parameter-fitting steps are described in the abstract or framing that could reduce to self-definition or fitted-input predictions. The central claims rest on external dataset properties and baseline comparisons rather than any internal reduction by construction. This matches the default expectation of no significant circularity for an empirical robotics paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no explicit free parameters, axioms, or invented entities; all modeling choices remain implicit.

pith-pipeline@v0.9.0 · 5652 in / 985 out tokens · 33146 ms · 2026-05-23T19:17:55.724494+00:00 · methodology

discussion (0)

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