Improving Robotic Imitation Learning via Trajectory Standardization

• URL: http://arxiv.org/abs/2606.22907v1
• Date: Mon, 22 Jun 2026 06:46:26 GMT
• Title: Improving Robotic Imitation Learning via Trajectory Standardization
• Abstract summary: A common preprocessing strategy is time-uniform downsampling to shorten sequences, but it cannot effectively remove speed-induced non-uniformity or redundant pauses.<n>We propose Information-Standardized Trajectory Resampling (ISR), an offline preprocessing method for effective imitation learning.<n>ISR improves task success rates by about 25%, remains robust across datasets collected from different operators, and reduces both dataset size and training cost.
• Score: 7.969825103657315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imitation learning for robotic manipulation relies on large sets of human demonstration trajectories, which are often noisy and temporally irregular due to variable operator speed, intermittent pauses, and inconsistent action density. A common preprocessing strategy is time-uniform downsampling to shorten sequences, but it cannot effectively remove speed-induced non-uniformity or redundant pauses. This mismatch degrades data quality and hinders policy learning. To address this issue, we propose Information-Standardized Trajectory Resampling (ISR), an offline preprocessing method for effective imitation learning. ISR resamples each trajectory by enforcing approximately equal information distance between adjacent points. Specifically, we map trajectories onto an information-modulated Riemannian manifold and perform geodesic-equidistant parameterization. We construct an information-intensity field from velocity and acceleration norms: the velocity term removes small-motion redundancy, while the acceleration term preserves high-curvature and fine-manipulation phases. We evaluate ISR on three real-world manipulation tasks with mainstream imitation learning policies. Compared with the baseline time-uniform 3x downsampling, ISR improves task success rates by about 25%, remains robust across datasets collected from different operators, and reduces both dataset size and training cost. The code and videos are publicly available at https://d-robotics-ai-lab.github.io/isr.page.

Related papers

• Temporal Policy: History-Initialized Action Generation for Robotic Learning from Demonstration [0.01268579273097071]
  Generative models excel at capturing multimodal behaviors for robotic Learning from Demonstration (LfD), but often suffer from high inference cost.<n>This paper introduces Temporal Policy, a generative framework based on interpolants that formulates action generation as a temporally coupled transport problem.<n>We validate Temporal Policy across visuomotor simulation benchmarks and on a physical Barrett WAM 2x 7DoF platform.
  arXiv  Detail & Related papers  (2026-07-31T14:47:02Z)
• Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning [8.16664434473795]
  We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC)<n>We propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories.<n>Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error.
  arXiv  Detail & Related papers  (2026-04-18T05:51:46Z)
• MemDLM: Memory-Enhanced DLM Training [56.40248490616793]
  Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models.<n>They suffer from a notable train-inference mismatch.<n>We propose MemDLM (Memory-Enhanced DLM), which embeds a simulated denoising process into training.
  arXiv  Detail & Related papers  (2026-03-23T17:39:56Z)
• Ada3Drift: Adaptive Training-Time Drifting for One-Step 3D Visuomotor Robotic Manipulation [53.750389076941396]
  Diffusion-based visuomotor policies capture multimodal action distributions through iterative denoising, but their high inference latency limits real-time robotic control.<n>We propose Ada3Drift, which learns a training-time drifting field that attracts predicted actions toward expert demonstration modes.<n>Ada3Drift achieves state-of-the-art performance while requiring $10times$ fewer function evaluations than diffusion-based alternatives.
  arXiv  Detail & Related papers  (2026-03-12T14:36:53Z)
• ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning [18.435889278351297]
  ESPADA is a semantically aware framework that segments demonstrations using a VLM-LLM pipeline with 3D gripper-object relations.<n>To scale from a single annotated episode to the full dataset, ESPADA propagates segment labels via Dynamic Time Warping.<n> ESPADA achieves approximately a 2x speed-up while maintaining success rates, narrowing the gap between human demonstrations and efficient robot control.
  arXiv  Detail & Related papers  (2025-12-08T10:08:33Z)
• Obstacle Avoidance using Dynamic Movement Primitives and Reinforcement Learning [36.09105994195904]
  This work proposes an alternative approach that quickly generates smooth, near-optimal collision-free 3D Cartesian trajectories from a single artificial demonstration.<n>The demonstration is encoded as a Dynamic Movement Primitive (DMP) and iteratively reshaped using policy-based reinforcement learning.<n>The approach is validated in simulation and real-robot experiments, outperforming a RRT-Connect baseline in terms of computation and execution time.
  arXiv  Detail & Related papers  (2025-10-10T10:51:42Z)
• RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility [9.200793414310182]
  We introduce RHYTHM (Reasoning with Hierarchical Temporal Tokenization for Human Mobility), a unified framework for predicting human mobility.<n>We use large language models (LLMs) as general-purpose predictors and reasoners.<n> RHYTHM achieves a 2.4% in overall accuracy, a 5.0% increase on weekends, and a 24.6% reduction in training time.
  arXiv  Detail & Related papers  (2025-09-27T04:55:56Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [57.278726604424556]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers.<n>Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy.<n>We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns [77.34726150561087]
  We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers. Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
  arXiv  Detail & Related papers  (2022-05-19T08:37:56Z)
• Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
  In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA) It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
  arXiv  Detail & Related papers  (2020-06-24T04:45:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.