Title: Generative Trajectory Stitching through Diffusion Composition
Venue: NeurIPS 2025
Accepted: yes
Keywords: Trajectory Stitching

Diffusion Models

Compositionality

Planning
Abstract: Effective trajectory stitching for long-horizon planning is a significant challenge in robotic decision-making. While diffusion models have shown promise in planning, they are limited to solving tasks similar to those seen in their training data. We propose CompDiffuser, a novel generative approach that can solve new tasks by learning to compositionally stitch together shorter trajectory chunks from previously seen tasks. Our key insight is modeling the trajectory distribution by subdividing it into overlapping chunks and learning their conditional relationships through a single bidirectional diffusion model. This allows information to propagate between segments during generation, ensuring physically consistent connections. We conduct experiments on benchmark tasks of various difficulties, covering different environment sizes, agent state dimension, trajectory types, training data quality, and show that CompDiffuser significantly outperforms existing methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors present CompDiffuser a method for planning trajectories in a sampling driven generative manner based on diffusion. Long and potentially complex trajectories are decomposed into several shorter shorter trajectory segments to solve the overall motion planning problem. The conditional relationships between consecutive trajectory chunks are then learned using a bidirectional diffusion model. The proposed method enables both parallel generation of trajectory chunks and causal autoregressive generation and is shown to significantly outperform several relevant baselines.\nThe authors present an experimental evaluation of their approach both against multiple baselines and for several scenarios in simulation. Experimental simulation settings include 2d maze navigation problems and higher dimensional maze navigation settings with ant and humanoid embodiments where the method is shown to significantly outperform existing baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper is clearly written and provides a good overview of the proposed method. \n- The supplementary material is exemplary and extensive and covers important aspects such as the failure mode analysis.\n- The paper tackles a crucial current shortfall of diffusion based methods in motion planning and appears to make significant advances over baseline state of the art methods.\n\nWeaknesses:\n\n- The current experimental evaluation is still only based on relatively limited simulation problems. It would be interesting to see examples of challenging planning problems where the method still fails and a real world implementation in the robotics context would also be interesting to see in future work.\n\nQUESTIONS:\n- Classical motion planning techniques in robotics such as probabilistic roadmaps/rapidly exploring random trees construct graphs or trees with nodes and connecting trajectory segments between sets of states. Could the proposed stitching techniques potentially be extended to construct not just a single multi-segment path, but a complete graph of segments to solve motion planning problems between sets of source/target locations as well?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a novel approach to planning for long-horizon tasks, called CompDiffuser. This method involves breaking down trajectories into shorter segments with overlapping parts, and then training diffusion models to learn the distribution of consecutive states conditioned on the start and end states of neighboring segments.\n\nAt test time, given a starting state and a goal state, CompDiffuser generates a plan by combining these segments in an autoregressive or parallel manner, resulting in a physically plausible plan. The plan is then executed using a learned inverse dynamic policy.\n\nThe paper provides empirical results, primarily focusing on navigation tasks with various embodiments (such as pointmass, ant, and humanoid) and different mazes. These results are compared to other state-of-the-art methods in goal-conditioned offline RL and diffusion-based approaches\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n\n- The paper is well-structured, clearly motivated, and presents a straightforward methodology.\n- The proposed CompDiffuser algorithm demonstrates good performance in stitching trajectories, outperforming baselines in most domains, particularly in longer horizon tasks (e.g., maze giant). Visualizations confirm that the generated plans are physically plausible.\n- The paper provides comprehensive ablation studies on various aspects, including chunk size, state dimensionality, parallel vs. autoregressive sampling, and replanning procedures.\n\n\nWeaknesses:\n\n- A significant weakness of the paper is the lack of detailed implementation specifics, such as: Architecture details, the way they condition on overlapping parts, Network capacity (total number of parameters needed), any data normalization techniques, any weighting used in the diffusion loss? \n- The usage of the generative skill chaining baseline is unclear; more details, equations, or explanations would be helpful to understand its application in this context.\n\n- The trajectory merging approach appears to assume linear interpolation between produced trajectories. However, this might not be suitable for cases where two states are separated by obstacles (e.g., walls). Clarification on this aspect is necessary.\n\n- Although the method seems generalizable, the empirical investigation primarily focuses on navigation tasks and planning with xy positions, leaving questions about its applicability to other domains, such as manipulation or locomotion. Could you provide results in cube domains in OGbench for example (single and double) ? \n- Why in most of the results except in antmaze, the authors ignore the qvel part of the states, how bad the prediction would be if we predict the full state instead only the positions ? \n- the method seems to rely on high quality data, expert demonstration. when trained on navigate data, the method performance decreases\n\nQUESTIONS:\nsee comments above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis article introduces a method for generative planning using stitching. Their method denoises short segments conditioned on the noisy states of their neighbors, to form overall long coherent trajectories. They introduce two flavors of this method, one of which only conditions on the previous noise level, and another which takes the current noise level for one neighbor. The paper presents extensive evaluation on maze-type benchmarks as bundled in the OGBench benchmark and others, and demonstrate an advantage over various baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrenghts:\n- The paper is nice to read and the method appears elegant and fits the problem well\n- There is an extensive number of baselines and experiments presented in the paper\n- Effective learning based only on chunks that generalizes to longer horizons is an important task\nWeaknesses:\n- While the baselines are plentiful and there are a lot of experiments, it is not made entirely clear how appropriate the baselines actually are, and while there are a lot of experiments, there is not a lot of variation in task type. I will address each of these concern in a bit more detail. \nBaselines:\n- In appendix B.3, they compare against \"monolithic\", which is much faster, they say because it is a much smaller architecture. This seems like a strange comparison: it is not apples to apples, and therefore not very useful. As far as I see it, their parralel \"chunked\" attention is effectively monolithic attention with masking of all but three chunks, so, it should be possible for their method to be at least as fast or faster in like for like scenarios. This would indicate the overall model for the monolithic case is smaller, making the comparison invalid. \n- Continuing on this same topic, the parameter count / model size is not really well presented, but seems like an important thing to report since they give such strong performance against baselines. This raises the question whether the authors simply train much bigger networks than their competitors and this is why they get such a big advantage. It is more useful to the reader if they can compare like for like. In Table 12 there are some hyperparameters reported, but it is not possible to understand overall parameter count from that, and it is not possible to understand how it compares to DD.\n- In the checklist, authors state that their experiments are reproducible since they \"describe it in Appendix E and they release their code on acceptance\". In appendix E, the authors only describe the setup partially (only for `pointmaze giant`), and refer the reader to \"refer to our codebase for details\", which is not provided, making it impossible to judge any of these details. The authors should have an appendix with experimental settings for each experiment, and list total number of hyperparameters for each model, as well as how it compares to baselines. \n- Something that is not made clear in the paper is whether baselines are trained on snippets as well, and then required to produce full trajectories at inference time? If this is the case that also seems like a comparison of limited use, since those methods were not developed to perform well on these tasks. Can the authors comment?\nExperiments:\n- The experimental evaluation is limited to maze-like RL benchmarks, especially given the baselines such as GSC, it would be ineresting to see performance on such robot manipulation tasks as well.\n\nQUESTIONS:\nIn the algorithm, it states that \"clean trajectories\" are sampled from the dataset, while in the experiment description it is stated that the experment is done on benchmarks with short segments of trajectories. Are they full trajectories or short segments? If they are short segments, how are these connected together with their neighbors, as is required for the training algorithm? I.e., to train the diffusion model, the two neighbors on either side are required inputs to the model.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBriefly summarize the paper and its contributions. This is not the place to critique the paper; the authors should generally agree with a well-written summary. This is also not the place to paste the abstract—please provide the summary in your own understanding after reading.\n\nThis paper considers the problem of building compositionally generalizable diffusion-based planners, i.e. models that can diffuse long sequences of actions (and/or states), without being bound to a fixed plan length. While prior work on diffusion-based planners (e.g. Diffuser or Decision Diffuser) did allow for generating multiple fixed-size plans in sequence, they struggle to generalize to sequences of much longer sequences than ones used in training. One of the challenges is ensuring a smooth and physically feasible transition between denoised sub-plans.\n\nThis work proposes CompDiffuser, which addresses this by denoising several sub-plans at the same time, with each sub-plan being conditioned on its adjacent sub-plans. In addition, the sub-plans are chosen to be overlapping. This enables the model to sample sub-plans that can actually be stitched together more easily.\n\nConcretely, the trajectories are factorized as:  \n$$\np_\\theta(\\tau \\mid q_s, q_g) \\;\\propto\\;\np_1\\bigl(\\tau_1 \\mid q_s, \\tau_2\\bigr)\\,\np_K\\bigl(\\tau_K \\mid \\tau_{K-1}, q_g\\bigr)\\,\n\\prod_{k=2}^{K-1}\np_k\\bigl(\\tau_k \\mid \\tau_{k-1}, \\tau_{k+1}\\bigr).\n$$\n\nWhere $q_s$ and $q_g$ are the start and goal states, respectively.\n\nThese conditional distributions are modeled using a joint diffusion model. At training time, the noise prediction network (in the DDPM sense) for sub-trajectory $\\tau_k$ at noise level $t$ (denoted $\\tau_k^t$) takes as input $t, \\tau_k^t, \\tau_{k-1}^t$ and $\\tau_{k+1}^t$, i.e. it is conditioned on the adjacent sub-trajectories at the same noise level. There is also a loss term for conditioning the first and last plans on the start and goal states, respectively. The fact that adjacent chunks influence each other during sampling aims to enable the model to make the sub-plans compatible “by construction”, allowing them to be easily stitched together at the end of sampling.\n\nThe authors propose two alternatives for sampling:\n\n- **Parallel sampling**: the noise predictor at $\\tau_k^t$ conditions on $\\tau_{k-1}^t$ and $\\tau_{k+1}^t$, as in training. This means that the samples at noise level $t-1$ only depend on samples at noise level $t$, meaning the denoising steps can be run in parallel.  \n- **Autoregressive sampling**: the noise predictor at $\\tau_k^t$ conditions on $\\tau_{k-1}^{t-1}$ and $\\tau_{k+1}^t$ (i.e. the sample for the previous trajectory is fed in at noise level $t-1$). Now one needs to do each denoising step sequentially (across time) at each noise level. The authors claim this enables stronger coordination across sub-plans, and find that autoregressive sampling has better empirical performance.\n\nExperiments focus on validating the empirical performance of CompDiffuser and studying how its performance varies with respect to state dimensionality, number of composed trajectories, sampling schemes and replanning.\n\nEvaluation covers PointMaze stitching datasets and OGBench locomotion tasks (AntMaze, HumanoidMaze, AntSoccer), comparing CompDiffuser against diffusion planners (Decision Diffuser, GSC), sequence-modeling methods (RvS, DT), and offline RL algorithms (GCBC, GCIVL, GCIQL, QRL, CRL, HIQL). The training data for these environments is constrained to shorter trajectories, as opposed to whole plans, producing a setting where models need to compositionally generalize in order to solve the task. CompDiffuser consistently outperforms these baselines on PointMaze and high-dimensional stitching tasks, demonstrating its ability to maintain feasibility and goal reachability under varied conditions.\n\nAblations show autoregressive sampling yields higher success than parallel sampling; replanning offers robustness gains primarily in the most complex (Giant) mazes; and performance remains stable across segment counts and inverse-dynamics configurations but degrades as planning dimensionality increases.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**  \n- Clear problem motivation: the authors clearly outline their goal, which is to address length generalization in decision making diffusion models.  \n- Experimental results indicate successful completion of research goal and extensively study relevant variables in the method’s implementation and performance. In particular, they demonstrate that CompDiffuser does indeed show much better length generalization than its full-sequence diffusion counterparts.  \n- Thorough and high-quality exposition that makes it very clear what the proposed algorithmic approach is, and relevant design choices (e.g. sampling approach).\n\n**Weaknesses:**  \n- Overall I consider this to be a good paper. On the algorithmic side, the three main novel points in their approach are: (1) subdividing large trajectories into chunks when denoising; (2) choosing the chunks to be overlapping; and (3) conditioning each chunk on its previous and following chunk. While ablations consider several aspects of the proposed setup (e.g. sensitivity to replanning, sampling approach, etc), I believe it would be constructive to include ablations of at least points (2) and (3).  \n  - Namely, these ablations would be: (2) testing a version of CompDiffuser where the chunks don’t overlap, and (3) testing a version of CompDiffuser that conditions only on the previous trajectory, and not on both adjacent ones. While I can see reasons why design decisions (2) and (3) should be beneficial, it’s not clear to me that e.g. the method would completely collapse or be mis-specified without them. These ablations would help understand the value of each algorithmic design decision made by the authors.  \n  - Also, ablation (3) above would bear some resemblance to Diffusion Forcing (Chen et al. 2023). It seems to me the paper would be strengthened by including a comparison between CompDiffuser and Diffusion Forcing in maze planning settings, since this is another recent diffusion decision making model which had good results relating to length generalization (e.g. infinite rollouts in Minecraft).\n\nQUESTIONS:\n- I think one interesting statistic about planning methods is the size of the largest problem they can solve reliably. In the setting of PointMaze environments, this could be measured along the lines of “how large does the maze have to be for the average success rate to drop below 95%”. Do the authors have a sense of, in the setting of Table 1, for how large a horizon does CompDiffuser’s performance start degrading, if any? We see that from the Large maze to the Giant maze in OGBench there is a significant drop; I wonder at what problem size the degradation starts.  \n- On a similar vein, do the authors have a sense of how they would build a CompDiffuser to achieve 100% accuracy in the Giant maze? Is it a matter of simply collecting more data? Or is it potentially a fundamental limitation of the method due to accumulating errors throughout the autoregressive sampling process at longer horizons?  \n- A more general instance of the question above: what are the main variables with respect to which the generalization ability of the method scales? (Potential variables could be amount of data, ratio of training trajectory length to evaluation trajectory length, computation budget for denoising during the forward pass, replanning budget, etc).  \n- In appendix, the authors mention that CompDiffuser’s autoregressive performance is robust to whether one starts from the starting position (hence working “forward” to the goal position) or from the goal position (hence walking “backward” to the starting position). However, the locomotion problem here is in a sense very symmetric, in the sense that a valid plan from point A to point B can be inverted to obtain a valid plan from B to A. Do the authors expect the method to be robust to sampling direction in more general settings, even e.g. for problems that are more naturally solved by “working back from the goal position”?  \n  - For an example of such an environment, consider a graph with a root node $s$, which is always a starting node, a set $I$ of intermediate nodes, and a set $T$ of target nodes. Each target node is connected to a single intermediate node. Intermediate nodes can be connected among themselves arbitrarily. $s$ is connected all intermediate nodes. Given a target position $t$, one immediately knows the intermediate node it is connected to (because it is unique), and from there one can easily construct a path from $s$ to $t$. However, starting from $s$, there are $|I|$ possibilities for the first move. If $I$ is very large, it seems intuitive to me that solving the problem “backward” should be significantly easier given finite memory capacity compared to solving it “forward”. Please let me know if I’m missing something with this example!  \n- Do the authors expect there to be any major methodological hurdles in generalizing CompDiffuser to settings requiring processing of e.g. visual inputs? (or, more generally, non-ground-truth-state observations, POMDPs, etc).  \n- Recent work by Chen et al. (2024) introduced Diffusion Forcing, and had strong long rollout results extending to complex environments such as Minecraft. Do you see any complementarity in your approaches to e.g. enable stronger temporal generalization of decision making diffusion models in complex control settings? What would an ultimate, ideal solution for this more general problem in complex environments look like in your view, and what are the main unsolved problems for making it happen?  \n- Do the authors see a natural extension of their method to non-goal-directed settings, instead driven by e.g. reward functions or other success metrics?  \n- On a similar vein, would one expect CompDiffuser to inherit similar global guidance properties from full-trajectory diffusion methods like Diffuser or Decision Diffuser? My intuition is it would, since sub-plans condition on each other during sampling.\n\nLIMITATIONS:\nThe authors address limitations in the conclusion.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}