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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Wed, 19 Nov 2025 18:51:08 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"691e119c-13d0f" expires: Sun, 28 Dec 2025 06:08:41 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: EDE3:2B0FD4:754307:834832:6950C70E accept-ranges: bytes age: 0 date: Sun, 28 Dec 2025 05:58:41 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210094-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1766901521.898294,VS0,VE218 vary: Accept-Encoding x-fastly-request-id: b13af1f9009a7b81a2c86de0d27424e98264fdd8 content-length: 10413 Generative Trajectory Stitching through Diffusion Composition

Generative Trajectory Stitching through
Diffusion Composition

Yunhao Luo¹, Utkarsh A. Mishra¹, Yilun Du^†,2, Danfei Xu^†,1

¹ Georgia Tech ² Harvard University

^† Equal advising

NeurIPS 2025 (Spotlight)

arXiv Paper Code

Trajectory Stitching via Compositional Trajectory Generation. The proposed method, CompDiffuser, generates long-horizon plans by compositionally sampling a sequence of coherent short trajectory segments (while only being trained on short-horizon data).

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, training data quality, and show that CompDiffuser significantly outperforming existing methods.

Compositional Trajectory Sampling

Compositional Trajectory Denoising Process. We train CompDiffuser on the OGBench PointMaze Large Stitch Dataset, which only contains short trajectories that travel at most 4 blocks. However, at test time, the agent needs to travel up to 15 blocks to reach the given goal (purple star) from the given start (blue circle). The proposed method is able to compositionally sample 5 trajectories, as shown by different colors, to construct a goal-reaching trajectory of much longer horizon than those seen in the training dataset.

replay

CompDiffuser Autoregressive Sampling Method

Compositional Trajectory Planning with Autoregressive Sampling. We present an illustrative example of sampling three trajectories $\tau_{1:3}$ with CompDiffuser. Dashed lines represent cross trajectory information exchange between adjacent trajectories and black lines represent the denoising flow of each trajectory. In this autoregressive sampling scheme, denoising $\tau_k$ depends on the previous trajectory $\tau_{k-1}$, e.g., the denoising of $\tau_2$ depends on $\tau_1$ (as shown in the blue horizontal dashed arrows). Additionally, start state $q_s$ and goal state $q_g$ conditioning are applied to the trajectories in the two ends, $\tau_1$ and $\tau_3$, which enables goal-conditioned planning. Trajectories $\tau_{1:3}$ will be merged to form a longer plan $\tau_{\text{comp}}$ after the full diffusion denoising process.

Composing Different Numbers of Trajectories

Unseen Long Horizon Evaluation Task
Start (circle) & Goal (star)

# of Composed Trajectories $K~ $

replay

Compose Different Numbers of Trajectories at Inference. Our method is only trained on short trajectories segment with maximum length of 5 blocks, while is able to compositionally generate coherent long trajectory given the start (circle) and goal (star). When $K$ is smaller and barely sufficient to reach the goal (e.g., 8), the length of overlapping chunk between segments decreases to extend the travel distance of the overall compositional plan. While if given a larger $K$ (e.g., 11), some parts of the compositional plan might travel redundant distances (back and forth) to consume the extra length.

OGBench AntMaze Stitch Datasets

AntMaze Medium Stitch replay

Task 1

Task 2

Task 3
AntMaze Medium Stitch replay

Task 4

Task 5

Qualitative Results in OGBench AntMaze Medium Stitch Environments. We present the environment rollouts of CompDiffuser in 5 OGBench tasks. The blue circle indicates the start position, the pink circle indicates the goal position, and the yellow circle denotes the current subgoal waypoint for the agent.

AntMaze Large Stitch replay

Task 1

Task 2

Task 3
AntMaze Large Stitch replay

Task 4

Task 5

Qualitative Results in OGBench AntMaze Large Stitch Environment. The blue circle denotes the start position, the pink circle denotes the goal position, and the yellow circle denotes the current subgoal waypoint for the agent.

AntMaze Giant Stitch replay

Task 1

Task 2

Task 3
AntMaze Giant Stitch replay

Task 4

Task 5

Compositional Planning in High Dimension

Task:

replay

topdown view

agent view

Synthesized Plans in 15D and 29D on OGBench AntMaze Stitch Environments. Note that our method is only trained on short trajectory segments while generates the full goal-reaching trajectory (from bottom left to top right) by composing multiple trajectories (as shown by different colors in the topdown view).

Stitching Extremely Low Quality Data

Task:

replay

Example Demonstrations

Our Rollout

Example Demonstrations in OGBench AntMaze Explore Dataset and Qualitative Environment Rollout of CompDiffuser.

Compositional Planning in Complex Dynamics

Synthesized Plan: Top-down View

Synthesized Plan: Zoom-in View

Qualitative Compositional Plan in OGBench AntSoccer Stitch Dataset. We generate four trajectories compositionally, as highlighted by the color of the circle beneath the ant (color order: blue -> orange -> green -> red; the circle is just for visualization purpose). This enables our planner to complete new tasks: the ant moves to the soccer ball and then dribbles the ball to the goal location (indicated by pink circle). We use inpainting to enable the goal conditioning in our planner (the inpainted goal state is shown in the last frame of the video). See the Dataset types Section in the OGBench website for dataset examples.

OGBench AntSoccer Stitch Environments

AntSoccer Arena Stitch replay

Task 1

Task 2

Task 3
AntSoccer Arena Stitch replay

Task 4

Task 5

Environment Rollout of CompDiffuser in AntSoccer Arena Environments. In this task, the ant needs to move the ball to the goal position as shown by the pink circle. The initial position of the ant and the ball is shown by the blue and yellow circle respectively. The model is trained on two different types of trajectories: 1) the ant moves without dribbling the ball; 2) the ant moves while dribbling balling. At test time, our method stitches these two types of trajectories to achieve the goal by first moving to the ball from the far side and dribble the ball to the goal position.

AntSoccer Medium Stitch replay

Task 1

Task 2

Task 3
AntSoccer Medium Stitch replay

Task 4

Task 5

Environment Rollout of CompDiffuser in AntSoccer Medium Environments. In this task, the ant needs to move the ball to the goal position as shown by the pink circle. The initial position of the ant and the ball is shown by the blue and yellow circle respectively. The model is trained on two different types of trajectories: 1) the ant moves without dribbling the ball; 2) the ant moves while dribbling balling. At test time, our method stitches these two types of trajectories to achieve the goal by first moving to the ball from the far side and dribble the ball to the goal position.

OGBench HumanoidMaze Stitch Datasets

HumanoidMaze Medium Stitch replay

Task 1

Task 2

Task 3
HumanoidMaze Medium Stitch replay

Task 4

Task 5

Environment Rollout of CompDiffuser in Humanoid Medium Environment. The humanoid agent is marked by a green circle and the current subgoal is indicated by a yellow circle. Compared to the ant agent, we observe that the humanoid agent is less responsive in tracking the yellow subgoal given by the planner. This is probably due to the locomotion complexity of a humanoid agent and might be mitigated by leveraging more robust and specialized inverse dynamics model.

HumanoidMaze Large Stitch replay

Task 1

Task 2

Task 3
HumanoidMaze Large Stitch replay

Task 4

Task 5

Environment Rollout of CompDiffuser in Humanoid Large Environment. The humanoid agent is marked by a green circle and the current subgoal is indicated by a yellow circle.

HumanoidMaze Giant Stitch replay

Task 1

Task 2

Task 3
HumanoidMaze Giant Stitch replay

Task 4

Task 5

Environment Rollout of CompDiffuser in Humanoid Giant Environment. The humanoid agent is marked by a green circle and the current subgoal is indicated by a yellow circle. Due to the complexity of the inverse dynamics modeling, the humanoid may occasionally lose track of the given subgoal. A replanning will be triggered when the distance between the agent's position and the subgoal exceeds a threshold, a

Conclusion

We introduce CompDiffuser, a generative trajectory stitching method that leverages the compositionality of diffusion models. We introduce a noise-conditioned score function formulation that helps in performing autoregressive sampling of multiple short-horizon trajectory diffusion models and eventually stitching them to form a longer-horizon goal-conditioned trajectory. Our method demonstrates effective trajectory stitching capabilities as evident from the extensive experiments on tasks of various difficulty, including different environment sizes, planning state dimensions, trajectory types, and training data quality.

BibTeX

@article{luo2025generative,
      title={Generative Trajectory Stitching through Diffusion Composition},
      author={Luo, Yunhao and Mishra, Utkarsh A and Du, Yilun and Xu, Danfei},
      journal={arXiv preprint arXiv:2503.05153},
      year={2025}
    }

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