Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning

• URL: http://arxiv.org/abs/2604.16850v1
• Date: Sat, 18 Apr 2026 05:51:46 GMT
• Title: Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning
• Abstract summary: 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.
• Score: 8.16664434473795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early-iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories. We validate on real-robot whiteboard erasing and peg-in-hole tasks using a teleoperation setup with a compliance-controlled follower and a 3D-printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x-10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg-in-hole task at both seen and unseen positions, with I2RLC-trained policies exhibiting lower contact forces than those trained on IRLC-refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact-rich IL.

Related papers

• Improving Robotic Imitation Learning via Trajectory Standardization [7.969825103657315]
  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.
  arXiv  Detail & Related papers  (2026-06-22T06:46:26Z)
• TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies [58.40033352838586]
  Existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations.<n>We observe that the magnitude of each predicted action already governs how fast the robot moves.<n>We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition.
  arXiv  Detail & Related papers  (2026-06-04T17:59:40Z)
• Simple but Stable, Fast and Safe: Achieve End-to-end Control by High-Fidelity Differentiable Simulation [14.322775509774246]
  Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications.<n>We propose a novel end-to-end policy that directly maps depth images to low-level bodyrate commands by reinforcement learning.<n>Results demonstrate that the proposed approach achieves the highest success rate and the lowest jerk among state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-04-12T09:38:00Z)
• FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control [55.38832429564216]
  Reinforcement learning (RL) is a core approach for robot control when expert demonstrations are unavailable.<n>We present FlashSAC, a fast and stable off-policy RL algorithm built on Soft Actor-Critic.<n>Across over 60 tasks in 10 simulators, FlashSAC consistently outperforms PPO and strong off-policy baselines in both final performance and training efficiency.
  arXiv  Detail & Related papers  (2026-04-06T09:03:41Z)
• Vector Field Augmented Differentiable Policy Learning for Vision-Based Drone Racing [8.010590009724636]
  Differentiable-physics-based policy learning has recently demonstrated high sample efficiency and remarkable performance across various tasks.<n>We propose DiffRacing, a novel vector field-augmented differentiable policy learning framework.<n>We show that DiffRacing achieves superior sample efficiency, faster convergence, and robust flight performance.
  arXiv  Detail & Related papers  (2026-03-09T06:49:41Z)
• SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning [52.29534291796025]
  Reinforcement learning is a promising approach that adapts policies for faster execution without additional demonstration.<n>We propose SpeedAug, an RL-based policy acceleration framework that efficiently adapts pre-trained policies for faster task execution.
  arXiv  Detail & Related papers  (2025-11-24T04:25:47Z)
• Human-in-the-loop Online Rejection Sampling for Robotic Manipulation [55.99788088622936]
  Hi-ORS stabilizes value estimation by filtering out negatively rewarded samples during online fine-tuning.<n>Hi-ORS fine-tunes a pi-base policy to master contact-rich manipulation in just 1.5 hours of real-world training.
  arXiv  Detail & Related papers  (2025-10-30T11:53:08Z)
• SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies [20.52085846080824]
  offline Imitation Learning (IL) methods are effective at acquiring complex robotic manipulation skills.<n>Existing IL-trained policies are confined to executing the task at the same speed as shown in demonstration data.<n>We introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor policies.
  arXiv  Detail & Related papers  (2025-06-13T16:58:20Z)
• Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation [58.95799126311524]
  Humans can accomplish contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces.<n>Existing visual imitation learning approaches rely on action chunking to model complex behaviors.<n>We introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality.
  arXiv  Detail & Related papers  (2025-03-04T18:58:21Z)
• 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)

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.