Title: $\textit{Hyper-GoalNet}$: Goal-Conditioned Manipulation Policy Learning with HyperNetworks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Visuomotor Hypernetwork

Goal-Conditioned Policy
Abstract: Goal-conditioned policy learning for robotic manipulation presents significant challenges in maintaining performance across diverse objectives and environments. We introduce *Hyper-GoalNet*, a framework that generates task-specific policy network parameters from goal specifications using hypernetworks. Unlike conventional methods that simply condition fixed networks on goal-state pairs, our approach separates goal interpretation from state processing -- the former determines network parameters while the latter applies these parameters to current observations. To enhance representation quality for effective policy generation, we implement two complementary constraints on the latent space: (1) a forward dynamics model that promotes state transition predictability, and (2) a distance-based constraint ensuring monotonic progression toward goal states. We evaluate our method on a comprehensive suite of manipulation tasks with varying environmental randomization. 
Results demonstrate significant performance improvements over state-of-the-art methods, particularly in high-variability conditions. Real-world robotic experiments further validate our method's robustness to sensor noise and physical uncertainties. Our code and trained models will be made publicly available.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Hyper-GoalNet, a framework that uses hypernetworks to generate\ntask-specific policy network parameters from goal specifications for robotic manipulation.\nThe authors also propose latent space shaping techniques using forward dynamics modeling\nand distance constraints to enhance performance. Experiments are performed on 6\nRobosuite tasks, and comparisons are made with several baselines. Ablation studies on the\nuse of a different hypernetwork (HyperZero) and the effectiveness of the two new latent\nspace objectives are performed. The proposed approach is further tested on a real world\nrobot, leveraging four evaluation tasks.\n\nSTRENGTHS AND WEAKNESSES:\n*Originality*\n\nThe proposed latent space shaping technique is simple but principled and it shows benefits\nin the ablation studies. The use of hypernetworks in goal-conditioned behavioral cloning is\nnovel.\nHowever, it is not clear whether the first claimed contribution is actually a novelty. In the\nIntroduction, the authors say “we adapt optimization-inspired hypernetwork architectures for\ngenerating policy parameters conditioned on goal specifications, creating a framework that\ndynamically determines how current observations should be processed”, and again in\nsection 3 “To realize this insight, we leverage hypernetworks to dynamically generate\ntask-specific policy parameters [...] tailored to the specified goals”. While the related work\nsection briefly mentions recent RL approaches leveraging hypernetworks, saying they\ncannot be directly adapted to goal-conditioned behavioral cloning (lines 88-90), the ablation\nstudy in 4.3 compares Hyper-GoalNet to “Hyper-Zero [39], an alternative hypernetwork\narchitecture, under identical training conditions”. The adaptation seems therefore\nstraightforward.\n\n*Significance*\n\nThe use of hypernetworks in goal-conditioned behavioral cloning might be of interest to the\ncommunity, offering a well-motivated novel combination of existing techniques.\nHowever, while the related work section briefly mentions recent RL approaches leveraging\nhypernetworks, important references such as [1, 2] are missing. The relationship with\nprevious hypernetwork-based RL approaches is unclear. The authors should expand the\nrelated work section, explaining why such approaches cannot be directly adapted to\ngoal-conditioned behavioral cloning (lines 88-90), and how they differ from the proposed\nmethod. Otherwise, they should add comparisons to other hypernetwork-based approaches,\nto assess the superiority of the proposed approach. Some concerns regarding the presented\nresults for the chosen baselines raise doubts on the actual impact this work might have on\nthe community (see quality section for detailed comments and suggestions).\n\n*Quality*\nThe paper includes experiments across multiple manipulation tasks (long- vs short-horizon,\n3 difficulty levels) in simulation (Robosuite) and real robot experiments, with ablation studies.\nHowever, the results are not entirely convincing.\n\n●​ In line 222, the authors claim that one of the advantages of Hyper-GoalNet is its\nability to perform “natural goal completion detection”. However, they say they use\n“environment-provided terminal signals” for fair comparisons with the baselines (lines\n278-281). Then, in section 4.3, they claim again that a key advantage of their\napproach is that “it enables reliable autonomous detection of task completion based\non latent distances”. How is this verified?\n\n●​ The discussion of the quantitative results in Tables 1-2-4 is too superficial, simply\nsaying that the proposed approach outperforms the others. However, the baseline\nimplementations seem weakened, with GCBC, Play-LMP, and HyperZero obtaining\n0.0 success rates on all tasks and for all difficulty levels. The authors should say how\nthe baseline methods were re-implemented, how the hyperparameters were chosen,\ncheck for technical errors in their implementation, and carefully justify any disruptive\nresults like the ones that are currently reported. The training curves in Figure 5 seem\nto suggest there are issues in HyperZero’s training, which might be the cause of its\npoor performance.\n\n●​ Success rates are computed over 50 rollouts, but error bars are absent in all\npresented results. This is particularly problematic in Tables 6-8, where some of the\nbaseline approaches have success rates that are closer to the ones obtained by\nHyper-GoalNet.\n\n*Clarity*\n\nOverall, the paper is hard to follow. There are inconsistencies in terminology, missing\ninformation, and the organization can be improved.\n\n●​ The section on “Hypernetwork training” is not very clear. What is ℓ in equation 5?\n\n●​ In section 4.3, the authors compare against HyperZero, which is an alternative\nhypernetwork architecture, never introduced before. The authors should consider\nadding a brief description of such architecture where the other baselines are\ndescribed, in section 4.2.\n\n●​ In all experiments, it is not clear what the architecture of the policy network is and\nwhether it is the same for the baselines. Could performance differences might also\nbe due to the architectural differences?\n\n●​ In lines 313-315, the authors say: “Our training strategy also matters – unfreezing the\nR3M visual encoder [32] only after 20 epochs ensures stable parameter generation.”\nThis aspect was never mentioned before, and the overall training procedure is\nunclear, and the code is unavailable at this time. The authors should consider adding\nthis information in the Methods section.\n\n●​ In line 314, the authors refer to epochs, while in Figure 3 they report time steps on\nthe x axis. Are the two terms referring to the same thing? Try to be consistent with\nthe terminology.\n\n●​ In Table 9, the authors report average inference time per step, and claim the superior\nefficiency of their method. What about training times? No information about the\nmemory and total execution time needed to reproduce the experiments is provided.\n\n●​ Overall, results are not reproducible with the information provided. Details about the\narchitectures of the policy network and the hypernetwork are missing. The training\nprocedure is not clear, and the authors do not specify how hyperparameters were\nchosen.\n\n[1] Beck, J., Jackson, M. T., Vuorio, R., & Whiteson, S. (2023, March). Hypernetworks in\nmeta-reinforcement learning. In Conference on Robot Learning (pp. 1478-1487). PMLR.\n\n[2] Y. Huang, K. Xie, H. Bharadhwaj and F. Shkurti, \"Continual Model-Based Reinforcement\nLearning with Hypernetworks,\" 2021 IEEE International Conference on Robotics and\nAutomation (ICRA), Xi'an, China, 2021, pp. 799-805, doi: 10.1109/ICRA48506.2021.9560793.\n\nQUESTIONS:\n1.​ Why do GCBC, Play-LMP, and HyperZero achieve 0% success across all tasks and\ndifficulty levels? Please provide: (a) implementation details for each baseline, (b)\nverification that the original papers' results can be reproduced, (c) hyperparameter\nsearch procedures used, and (d) any debugging steps taken. If technical errors were\nfound and fixed, I would reconsider the quality score. Without proper baseline\nvalidation, the claimed improvements cannot be trusted.\n\n2.​ How does your approach differ from Beck et al. [1]'s hypernetwork-based meta-RL\nmethod? Your claim that \"hypernetwork-based RL approaches cannot be directly\nadapted to goal-conditioned behavioral cloning\" (lines 88-90) is contradicted by your\nown straightforward adaptation of HyperZero. Please clearly explain: (a) what makes\nyour adaptation novel compared to [1], and (b) why a comparison with [1] is not\nincluded. Adding this comparison or clearly establishing novelty would address my\noriginality concerns.\n\n3.​ Can you provide complete architectural and training details for reproducibility?\nSpecifically: (a) exact policy network architecture used for all methods, (b) complete\nhypernetwork architecture beyond Eq. 4, (c) the loss function ℓ in Eq. 5, (d) training\nprocedure including the R3M unfreezing strategy, (e) hyperparameter selection\nprocess, and (f) training time/memory requirements. Providing these details would\nimprove the clarity score and enable verification of results.\n\n4.​ How do you verify the claimed \"natural goal completion detection\" capability? You\nclaim this as a key advantage (lines 222, 313) but use environment-provided terminal\nsignals in experiments. Please show experiments where: (a) your method\nautonomously detects task completion using latent distances, (b) comparison with\nenvironment ground truth, and (c) success/failure rates using autonomous vs.\nenvironment detection. Demonstrating this capability would strengthen the\nsignificance of your contribution.\n\n5.​ What causes HyperZero's training instability (Figure 5) and how did you attempt to\naddress it? The loss curves suggest HyperZero never properly converges. Did you\ntry: (a) different learning rates or optimization strategies, (b) architectural\nmodifications, (c) different initialization schemes? Understanding whether this is an\ninherent limitation of HyperZero or an implementation issue is crucial for fairly\nevaluating your approach's advantages.\n\nAddressing questions 1 and 3 satisfactorily would improve Quality and Clarity scores.\nResolving question 2 would improve Originality. Demonstrating question 4 would enhance\nSignificance. A thorough response to all questions could potentially change the overall\nrecommendation from Reject to Borderline Accept/Accept.\n\nLIMITATIONS:\nThe authors discuss some of the major limitations of their approach.They might add a\ndiscussion on failure modes when goal-specifications lie outside the training distribution or\nwhen latent distances mispredict completion (if this feature was actually used), which could\nlead to unsafe behaviors.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Hyper-GoalNet, a novel framework for goal-conditioned manipulation policy learning that uses hypernetworks to dynamically generate task-specific policy parameters based on goal specifications. Unlike conventional approaches that concatenate goal and state information as input to fixed-parameter networks, this method treats goals as specifications that determine how current observations should be processed. The approach is evaluated on multiple manipulation tasks in simulation and real-robot experiments.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The core idea of using hypernetworks to generate goal-specific policy parameters is innovative and well-motivated.\n- The paper includes extensive experiments across multiple manipulation tasks with varying difficulty levels, thorough ablation studies, and real-robot validation.\n- The paper is well-written and easy to follow.\n\n**Weaknesses:**\n\n- The method is evaluated exclusively in single-view camera configurations. The authors do not demonstrate how their approach scales to multi-view camera setups, which are standard in most robot learning systems. Given that the method incorporates a latent shaping module, scaling to multiple cameras may introduce additional complexity and learning challenges.\n- The experimental baselines appear outdated and insufficient for comprehensive evaluation. For instance, the authors use C-BeT [1] but omit its more recent variant VQ-BeT [2]. Additionally, diffusion policies [3, 4], which have gained significant adoption in recent robot learning research, are not included in the comparison. While the authors' method employs behavior cloning loss, making direct comparison with diffusion-based approaches potentially unfair, they should clarify how their method could extend to other policies and justify the exclusion of diffusion-based baselines to strengthen their evaluation.\n- The real robot experimental results raise concerns, as all baseline methods exhibit surprisingly poor performance. Given that these appear to be standard robot learning tasks, the authors should clarify what specific aspects make these tasks challenging and explain the underlying factors contributing to the baselines' poor performance.\n- I am also concerned about the scaling ability of the method. With the number and complexity of tasks increasing, relying on the current observation and specific goals to predict the parameters of the policy could be challenging.\n---\n[1] Cui, Zichen Jeff, et al. \"From play to policy: Conditional behavior generation from uncurated robot data.\" ICLR'23.\n\n[2] Lee, Seungjae, et al. \"Behavior generation with latent actions.\" ICML'24.\n\n[3] Chi, Cheng, et al. \"Diffusion policy: Visuomotor policy learning via action diffusion.\" RSS'23.\n\n[4] Reuss, Moritz, et al. \"Goal-conditioned imitation learning using score-based diffusion policies.\" RSS'23.\n\nQUESTIONS:\nIf I understand it right, the method only predicts the action of the current timestep, so why don’t the authors apply some techniques such as action chunking?\n\nLIMITATIONS:\nThe authors have discussed the limitations of the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel network architecture for goal-conditioned policy learning, where the goal is specified via an image. The central contribution is the use of a hypernetwork that generates the policy network’s weights conditioned on the goal image. The approach is evaluated through behavioral cloning on a set of manipulation tasks, originally introduced in the MimicGen benchmark. Additionally, the authors report results on a real robot, further demonstrating the feasibility of their method.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- The paper is clearly written and easy to follow. Figures are well-designed and contribute to the reader’s understanding. I love figure 1 & 2, both are very well designed and executed.\n- The proposed architecture, involving a hypernetwork for goal-conditioning, appears to be novel in the context of visual goal-conditioned policies and robot manipulation. While I am not deeply familiar with all related work in this subdomain, the architectural choice seems original and well-motivated.\n- The experimental design strikes a good balance between simplicity and realism. The chosen tasks are tractable but still non-trivial, providing a reasonable testbed for evaluation.\n- The inclusion of real robot experiments is a strong point and adds practical credibility to the method, though more details would be welcome (see suggestions below).\n\n\n## Weaknesses\n\n1. **Necessity of Goal Conditioning**  \n   It is unclear whether the tasks presented in the paper truly require goal conditioning. In many of the simulated scenarios, the current observation already appears to encode sufficient information to determine the correct action. Goal images are typically most useful when the task involves ambiguity or requires disambiguating between multiple possible outcomes. As such, the relevance of the goal-conditioned architecture in these settings is somewhat questionable.\n\n2. **Venue Fit**  \n   While the work is technically sound and includes real-world experiments, its primary contributions lie at the intersection of robotics and representation learning. As such, it might be more naturally aligned with a conference like CoRL. That said, the paper could still be of interest to the NeurIPS community, particularly those working on goal-conditioned policies and representation learning for control.\n\nQUESTIONS:\n- Can the authors clarify in which of the presented tasks the goal image provides information not already present in the current state? In the absence of ambiguity, what is the motivation for using a goal-conditioned architecture?\n\n\n- Could the authors provide videos of the real robot experiments to support their results and enable a better qualitative assessment of the learned policies?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work, the authors introduce a novel framework to learn goal-conditioned policies by separating the goal conditioning from the state parameters. Specifically, the approach uses hypernetworks to generate target policy parameters based on the goals. This is achieved by shaping the latent space to maintain the predictability of future states through a learned dynamics model and preserving physical relationships through distance constraints so that there is a monotonic progression towards goals. Through experimental validation, the authors show that their hypernetwork-based framework outperforms baseline goal-conditioned methods that use fixed network parameters. Similarly, the authors conduct ablation studies to compare the hypernetwork architecture and latent space shaping while also testing their approach on a real robot.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper discusses a novel concept in detail with both theoretical and experimental justifications for each of the design choices for the Hyper-GoalNet framework, including the choice of the hypernetwork architecture as well as the latent space shaping approach. The experiment setup includes a robotics benchmark, ablation studies and evaluations on a real robot, not to mention the additional benchmarks in the appendix. The paper is also well-written and easy to understand. Lastly, the results show significant improvements over the baselines, highlighting the success of the approach. The paper could discuss the limitations in greater detail for the final discussion section instead of only describing it in the appendix. In addition, the experiment task variety or difficulty should be adjusted (particularly for the hypernetwork architecture ablation study) in order to have a more detailed comparison, as a 0% success rate across all tasks for Hyperzero.\n\nQUESTIONS:\nWhy do many of the goal-conditioned methods have a 0.00 success rate across all three difficulty levels? Is there a more simplified benchmark where they have at least some successes? The same idea applies to the ablation study with Hyperzero, as it seems to have achieved 0 successes overall.\n\nLIMITATIONS:\nYes, the authors adequately address the limitations, though it is mainly in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}