arXiv AI
Jun 11
Noise-Guided Transport for Imitation Learning
arXiv:2509. 26294v2 Announce Type: replace-cross Abstract: We consider imitation learning in the low-data regime, where only a limited number of expert demonstrations are available.
FOCUS: First-Order Control Using Optimal Transport Supervision for Imitation from Observations
FOCUS is a new imitation‑learning method that learns from state‑only demonstrations without temporal alignment. It uses local optimal‑transport alignment to match short agent rollouts with sampled expert segments, creating differentiable imitation rewards. A learned critic then bootstraps value beyond the rollout horizon, improving sample efficiency and training stability on contact‑rich locomotion tasks.
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arXiv AI
Oct 1
SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets
SynIL is a new framework for offline imitation learning that automatically assesses the quality of demonstration data without requiring labels. It uses motor synergy—a low‑dimensional coordinated movement pattern linked to proficiency—to generate dense, transition‑level reward signals through self‑supervised reward regression. Experiments on D4RL locomotion and Robomimic manipulation datasets show that synergy‑derived rewards align well with true rewards and that SynIL outperforms Behavior Cloning and rivals or surpasses offline reinforcement learning in sparse‑reward scenarios.
arXiv AI
Jun 2
Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
arXiv:2606. 01098v1 Announce Type: cross Abstract: Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control.
arXiv Computer Vision
Sep 21
DexPIE: Stable Dexterous Policy Improvement from Real-World Experience
DexPIE is a post‑training framework that improves dexterous manipulation policies using real‑world experience. It introduces a dexterous‑hand‑adapted intervention system and multi‑stage DAgger‑style data collection to enhance exploration, aligns training and inference to reduce distribution shift, and conditions the policy on a continuous optimality indicator for fine‑grained data quality use. In three real‑world tasks, DexPIE boosts success rates by 37.3% over a demonstration‑based baseline, outperforming all other methods and showing stronger robustness.
arXiv Machine Learning
Jun 2
Training-Free Imitation Learning with Closed-Form Diffusion Policies
arXiv:2606. 01238v1 Announce Type: cross Abstract: While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy deployment loop.
arXiv Machine Learning
Jun 16
DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.
arXiv AI
Jun 9
Difference-Aware Retrieval Policies for Imitation Learning
arXiv:2606. 09758v1 Announce Type: cross Abstract: Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment.
arXiv Machine Learning
Jun 2
Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
arXiv:2606. 01151v1 Announce Type: new Abstract: Behavior cloning with high-capacity generative policies achieves strong imitation performance, but is often limited by demonstration coverage and distribution shift.
arXiv AI
Jul 10
Feedback Manipulation Regularization: Enabling Offline Agent Alignment for Imitation Learning
arXiv:2607. 07859v1 Announce Type: new Abstract: Reinforcement learning (RL) research has increasingly shifted focus towards alignment, ensuring agents learn behaviors adhering to human values.
arXiv AI
Oct 15
Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
arXiv:2610.17709v1 Announce Type: cross Abstract: Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the data...
arXiv Machine Learning
Jul 16
Pretraining in Actor-Critic Reinforcement Learning for Locomotion
arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.
arXiv Machine Learning
Jul 31
REFINE-DP: Diffusion Policy Fine-tuning for Humanoid Loco-manipulation via Reinforcement Learning
arXiv:2603. 13707v3 Announce Type: replace-cross Abstract: Humanoid loco-manipulation requires coordinated task-space motion planning with stable loco-manipulation command tracking under complex robot-environment dynamics and long-horizon tasks.