Title: Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following
Venue: NeurIPS 2025
Accepted: yes
Keywords: Robotics

Representation Learning
Abstract: Effective task representations should facilitate compositionality, such
that after learning a variety of basic tasks, an agent can perform
compound tasks consisting of multiple steps simply by composing the
representations of the constituent steps together. While this is
conceptually simple and appealing, it is not clear how to automatically
learn representations that enable this sort of compositionality. We show
that learning to associate the representations of current and future
states with a temporal alignment loss can improve compositional
generalization, even in the absence of any explicit subtask planning or
reinforcement learning. We evaluate our approach across diverse robotic
manipulation tasks as well as in simulation, showing substantial
improvements for tasks specified with either language or goal images.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes temporal representation alignment (TRA), a training approach for a language-conditioned and goal-conditioned policy which uses contrastive losses to align future (image-based) goal representations with current (image-based) state representations and text-based task specification representations. Intuitively, this enables the policy to be trained on two separate trajectories where Trajectory A’s goal state is the initial state of Trajectory B, then be able to compose these trajectories together conditioned only on Trajectory B’s goal state (or text instructions).\n\nThe paper draws from BridgeDataV2 to train TRA and several representative baseline policies for real-world execution. Evaluation task categories carefully separate tasks by their difficulty of compositional generalization, from simple one-step tasks to concatenated tasks, order-independent concatenated tasks with different objects of the same type (e.g., placing several food items into a container), and tasks with ordering dependencies between subtasks (e.g., opening a drawer then taking an object out of it). In these real-world experiments, the paper demonstrates that TRA achieves top success rates in all studied task categories in both language-conditioned and goal-conditioned settings. While policies from prior work are not statistically significantly worse than TRA in many cases, TRA achieves the best statistically significant performance in more categories than any other approach. Performance gains over the best baseline are most pronounced in tasks requiring compositional generalization. TRA vastly outperforms traditional offline reinforcement learning approaches (which are typically thought necessary to “stitch” subtasks together), and does not significantly benefit from being combined with such approaches. Together, these suggest that TRA is a remarkably effective approach despite its relative simplicity.\n\nThe paper goes on to evaluate TRA in 7 different simulated environments from OGBench. Consistent with the above results, TRA achieves the significantly best success rate in most environments (5 of 7), including 4 of 5 environments that require stitching together subtasks. The paper then briefly discusses TRA’s failure cases.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength 1: Paper is well written and clear.** \n\nThis paper is well written. It clearly conveys its motivation, contributions, methods, experimental design, results, and conclusions. The paper clearly and intuitively contextualizes its contributions with prior work in language- and goal-based robotic manipulation, compositional generalization in sequential decision making problems, and representation learning for physical states and procedures. It is quite polished with limited typos, and its figures and notations are mostly very clear and illustrative. Extensive appendices provide lower-level supplementary details about the datasets, methods, implementation, and analyses for reproducibility. In terms of clarity, it is among the top tier of submissions.\n\n**Strength 2: Experiments soundly demonstrate effectiveness of TRA.**\n\nThe experimental results are motivated by clear and well-defined research questions. These questions are mostly effectively answered by experiments, demonstrating that TRA indeed improves zero-shot composition of tasks over prior methods, better captures rare skills in the dataset, and works even without explicit subtask planning or reinforcement learning. Performance improvements over prior work are sharp and significant. Experiments cover both language-conditioned and goal-conditioned execution, and simulated and physical settings. Evaluations carefully divide BridgeData according to difficulty in terms of compositional generalization. To the best of my knowledge, the baselines are sufficiently representative of approaches used in prior work. \n\n**Weakness 1: Figure 1 does not provide a clear impression of the task or method.**\n\nWhile this is a relatively minor concern, I found that Figure 1 left a lot of questions for me after reading Section 1 of the paper (where it’s referenced) for the first time. Specifically, I had the following questions (these do not require an answer, as I figured them out after reading the entire paper, but hopefully can help guide revisions of the figure):\n\n* What is the task that TRA, AWR, and BC are trying to perform? The caption claims this is a “language task”, but it clearly involves manipulation too.  \n* What are we trying to observe through the comparison with AWR and BC? What are AWR and BC doing wrong, and how does it relate to compositionality? I see that AWR only puts one food in the bowl, but does this show the entire execution of the robot? Further, it seems that BC is unable to find any objects. What I don’t understand from the figure and caption is how this demonstrates a lack of “compositionality.”  \n* What is the TRA method doing? How is the time-contrastive loss calculated, and what does that have to do with the sequence of frames? Providing an intuitive sentence or phrase in the caption to describe the method at the high level would help a lot.\n\nAlso, the acronym for advantage weighted regression (AWR) is only defined in the appendix \\- I would recommend defining this somewhere in the main body of the paper.\n\n**Weakness 2: Paper seems to conflate “rarely-seen” and “challenging” tasks.**\n\nOne key question for the experimental results was “How well does TRA capture skills that are less common within the dataset?” However, the discussion around this at L241-246 seems to focus on the more challenging tasks in terms of compositional generalization (from Sets C and D involving semantic generalization across objects and subtask dependencies), which are compositionally OOD. However, can we safely assume that subtasks from Sets C and D are less common in training than others? I can’t seem to find details to support this, and would recommend that the authors better clarify this in the revised version of the paper, and consider rephrasing this research question if needed.\n\nQUESTIONS:\nBased on my Weakness 2, I ask one question to facilitate the rebuttal: **Do compositionally-OOD tasks truly consist of less common subtasks?** Given clarification on this in the rebuttal, it’s possible I will increase my quality score up to 4\\. \n\nMy other weakness regarding Figure 1 cannot really be addressed without seeing a new version of the Figure, which the authors are welcome to discuss or present. That said, Figure 1 did not significantly impact my score (clarity score is already 4).\n\nGiven that the results are mixed in some cases (i.e., don’t consistently significantly outperform baselines), I don’t anticipate raising my significance score beyond 3 (which is a positive score). Given that the proposed approach is fairly simple, I don’t anticipate raising my originality score beyond 3 (which is a positive score). For these reasons, I don’t anticipate raising my overall score, which is already quite positive.\n\nLIMITATIONS:\nThe discussion of limitations of the approach in Section 5 is sufficiently thoughtful and provides areas for future work to improve upon. The discussion of failure cases in the main body of the paper is quite brief. I don't see much discussion about the broader society impact in the paper.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of compositional generalization in robotic skills, where elementary skills learned during training can be recombined to solve longer, more complex tasks at test time. The authors propose Temporal Representation Alignment (TRA), a method that achieves compositionality purely through learning aligned state representations via temporal alignment of state and goal/instruction embeddings, which are then used in a goal-conditioned policy. To this end, a contrastive loss is introduced to align representations from the same expert demonstration. The policy is trained via behavioral cloning on expert demonstrations using these temporally aligned goal embeddings. TRA is evaluated in both simulation and real-world robotics settings, and the results show improved performance compared to various goal- or language-conditioned baselines, particularly in compositional generalization to novel task sequences.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- addresses an important and challenging problem of compositional generalization in a novel way by introducing a temporal alignment loss over representations.\n- empirical evaluation: includes both simulation and real-world experiments, and demonstrates statistically significant performance improvements over a variety of baselines (even though the baselines don’t directly target compositionality!).\n- the paper is mostly well-written, and uses figures, algorithm boxes, and tables effectively to illustrate key concepts.\n\nWeaknesses:\n- some parts of the method description would benefit from more natural language explanation. For example, Eqs. 6 and 7 are presented without much intuition, and the roles of $\\psi$ and $\\phi$ (introduced in Eq. 5) are not clearly described in text until much later (Subsection 3.4).\n- no baseline  is directly targeting compositional generalization. While the method is compared to strong goal- and language-conditioned policies, it is not evaluated against approaches that specifically tackle compositionality, such as hierarchical policies or planning-based methods, e.g. the methods discussed in the related work section.\n\nQUESTIONS:\n1. Compositionality-specific baselines: None of the baselines used are designed to explicitly address compositional generalization. Could the authors include comparisons to hierarchical methods or planning-based approaches, as discussed in the related work?\n2. Clarification on rarely-seen skills: In the paragraph titled \"Does TRA help rarely-seen skills within the dataset?\", the actual frequency of the skills is not mentioned. How does the analysis support the claim about rarely-seen skills?\n3. Task set clarifications: Are all tasks from set A in-distribution training tasks? What is the difference between task set B and C? Why is the use of different objects with the same type referred to as \"semantic generalization\"?\n\nMinor Comments\n\n- Table formatting errors: In Table 1, some rows highlight 0.00 success rates (e.g., row 4 for AWR, row 6 for Octo) as statistically significant best results. These seem to be mistakes, please double-check.\n- Table references: Tables are referred to only by number in several places, which is confusing. For example, l. 211: \"1 shows\" → should be \"Table 1 shows\"\"; l. 611: \"in 5\" → should be \"in Table 5\".\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Temporal Representation Alignment (TRA), a novel approach for learning useful representations for solving goal/instruction-directed robotics tasks. The fundamental problem addressed by this paper is how to allow agents learning from a dataset of demonstrations to compose solutions to in-distribution tasks to solve longer-horizon out-of-distribution tasks. For example, if the dataset contains solutions to the tasks $s \\rightarrow w$ and $w \\rightarrow g$, how should an agent learn a representation that enables it to compose these solutions to solve the task $s \\rightarrow g$?\n\nThe proposed approach treats this purely as a representation-learning problem, and solves it by ensuring that the representations of states $s$, $w$, and $g$ are aligned according to the temporal structure of the demonstration trajectories. By mapping states, goals, and natural-language instructions into a shared embedding space that captures this temporal alignment, the agent can naturally “stitch together” demonstrated behaviours to perform composite tasks.\n\nThe authors demonstrate the performance of TRA on a diverse set of long-horizon robotics tasks with positive results, particularly in cases where solving tasks requires the sort of behaviour composition that TRA is well-suited to.\n\nSTRENGTHS AND WEAKNESSES:\n**Originality & Significance**\n\nThis paper addresses the important problem of allowing robotic agents to compose behaviours from large demonstration datasets to solve complex, long-horizon tasks that exceed the temporal scope of any single trajectory in their training data. By tackling this “trajectory stitching” problem directly and treating it as part of the representation learning problem, the proposed approach enables performance improvements over existing approaches across a wide range of challenging long-horizon tasks.\n\nThe proposed approach is also positioned well for widespread adoption. It is conceptually simple, seems straightforward to implement, and requires no additional information beyond what is already included in common behaviour-cloning datasets. Furthermore, its applicability is broadened by the fact that it can condition behaviour based on either raw goals or natural-language instructions.\n\nOverall, I believe the ideas presented in this paper represent a significant step forward for goal-directed learning from demonstrations – and perhaps representation learning for sequential decision problems more broadly – and should prove a valuable tool for the community.\n\n---\n\n**Quality**\n\nThe quality of this submission is very high.\n\nThe evaluation is both thorough and focused. Centred around four specific research questions, it demonstrates the performance of the proposed approach across a diverse set of challenging real-world tasks, and includes comparisons to a wide range of relevant baselines.\n\nIt was particularly refreshing to see the honesty with which the authors presented their results. For instance, Table 1 highlights the best-performing methods *up to statistical significance*, rather than simply highlighting the absolute highest performance (which, in many cases, would have made the proposed approach look even better), and Section 4.5 explicitly discusses the proposed approach’s failure modes.\n\nAlongside the thorough empirical evaluation, the method is also well-principled and supported by convincing theoretical work. I appreciated the care the authors took to introduce, motivate, and explain each element of their representation-learning objective.\n\n---\n\n**Clarity**\n\nFor the most part, the paper is written very clearly.\n\nThe authors explained the intuition behind the proposed approach well, and made good use of examples to help motivate it. The paper contains enough detail for me to understand the proposed method well. Taken together with the implementation details included in the appendices, I am confident that the authors have provided enough information to allow an expert reader to reproduce their results.\n\nThere are, however, a couple of potential issues with mathematical notation, which I have discussed in detail in the “Paper Formatting” section.\n\nQUESTIONS:\nI found the discussion around Equation 4 – of how the proposed approach allows an agent to compose information from demonstration trajectories $s \\rightarrow \\ldots \\rightarrow w$ and $w \\rightarrow \\ldots \\rightarrow g$ to capture the higher-level behaviour $s \\rightarrow \\ldots \\rightarrow g$ – to be particularly useful. This kind of sequential composition is a key idea motivating many existing hierarchical reinforcement learning methods. For instance, HIRO [1] learns a high-level policy that proposes a sequence of subgoals that a low-level policy navigates between, effectively stitching shorter segments together to solve a higher-level task.\n\n**How does the proposed approach compare with hierarchical methods (like HIRO) that attempt to address this problem of sequentially composing (or, “stitching together”) behaviours?**\n\nPlease note that I raise this not to diminish this paper’s contributions; on the contrary, I believe such discussion would further highlight the originality and significance of the proposed approach and better set it apart from related work in hierarchical reinforcement learning.\n\n---\n\nWhile the authors target robotics problems with continuous state and action spaces, I suspect many of the ideas proposed in this paper might be more generally applicable.\n\n**Do the authors foresee any challenges when adapting the proposed approach to work in domains with discrete state or action spaces?**\n**If so, do they have any initial ideas about how these issues might be addressed?**\n\n---\n\nThe reported success rates compare models trained on identical amounts of data, but it would be useful to know how the learned representations change as the amount of training data increases. If the proposed approach can learn useful representations with less (or more, as the case may be) data than alternative approaches, that would be useful to know.\n\n**Have the authors investigated the sample-efficiency of the proposed method?**\n\n---\n\n**References**\n\n[1] Nachum, O., Gu, S.S., Lee, H. and Levine, S., 2018. Data-efficient hierarchical reinforcement learning. Advances in neural information processing systems, 31.\n\nLIMITATIONS:\nThe authors are candid about the limitations of the proposed approach, including a thoughtful discussion of these limitations and highlighting promising avenues for future work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Temporal Representation Alignment (TRA), an auxiliary contrastive objective that aligns representations of current states, future goals, and language instructions in a shared embedding space. By adding symmetric NCE losses on state–future‐state pairs and goal–language pairs to a standard behavior‐cloning policy, TRA enables zero‐shot “stitching” of known subtasks into novel, long‐horizon manipulation sequences. The authors demonstrate over 40 % relative gains on compositionally‐out‐of‐distribution tasks in a real WidowX250 setup (5 Hz execution) and show consistent improvements across seven environments in the OGBench benchmark—outperforming both imitation‐only and offline‐RL baselines on stitching tasks . A theoretical bound (Theorem 1) further relates in‐distribution to compositional error under successor‐feature assumptions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Minimal, Orthogonal Extension: TRA simply augments a standard goal- and language-conditioned BC policy with two lightweight contrastive losses, requiring no additional planner or value network.\n\n2. Strong Compositional Gains: Real-world experiments show a > 40 % boost on out-of-distribution “stitch” tasks, and OGBench results confirm superior performance on environments demanding composition.\n\n3. Unified Language + Vision: The same auxiliary losses work for both goal-image and natural-language instructions, demonstrating modality-agnostic compositionality.\n\n4. Theoretical Backing: Theorem 1 and Corollary 1.1 provide clear guarantees that temporal alignment reduces compositional generalization error under mild assumptions.\n\nWeaknesses:\n\n1. Limited Physical-Robot Diversity: All hardware tests use a single 5 Hz WidowX250 arm. It remains unclear whether TRA scales to faster control loops (e.g., 20 Hz Panda) or different kinematics.\n\n2. Some lacking ablation: Key choices—geometric horizon γ in the future‐state sampling, temperature and weighting of the contrastive losses—lack ablation.\n\n3. Failure Modes Under Multimodality: As noted, TRA struggles with inherently multimodal tasks (e.g., when multiple valid grasps exist), but no mitigation strategies are proposed .\n\nQUESTIONS:\n1. How does varying the geometric sampling parameter γ affect compositional success? \n\n2. Can you provide t-SNE or clustering metrics showing that subtasks and their compositions occupy distinct, composable regions in the learned latent space?\n\n3. Have you evaluated TRA on a different manipulator (e.g., a Panda arm) or at higher control rates?\n\n4. How does TRA handle paraphrased or out-of-distribution instructions?\n\nLIMITATIONS:\nPlease see the weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-02"}