arXiv AI
Jun 16
Training and Evaluating Diffusion Policies with Long Context Lengths
arXiv:2606. 16447v1 Announce Type: cross Abstract: Imitation learning has enabled highly-dexterous robotic manipulation from RGB observations.
Asynchronous Multimodal Diffusion Policy Composition via Latency-Aware Guidance Fusion
arXiv:2607. 17257v1 Announce Type: cross Abstract: Diffusion policies have shown strong potential for robotic imitation learning, and recent extensions incorporate additional modalities to improve manipulation performance.
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arXiv AI
Jun 9
Latent Diffusion Policy: Shaping Latent Spaces for Diffusion-Based Robotic Manipulation
arXiv:2606. 08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process.
arXiv Machine Learning
Jun 10
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
arXiv:2606. 10025v1 Announce Type: cross Abstract: We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution.
arXiv Machine Learning
Jul 23
Diffusion ReRoll: Revisable Denoising for Robotic Sequential Prediction
arXiv:2607. 19919v1 Announce Type: cross Abstract: We propose Diffusion ReRoll, a diffusion-based framework for robotic sequential prediction that enables revisable denoising over horizons.
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 2
From Noise to Control: Parameterized Diffusion Policies
arXiv:2606. 00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embedded in a learned behavior manifold.
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 AI
Jun 16
Sample from What You See: Visuomotor Policy Learning via Diffusion Bridge with Observation-Embedded Stochastic Differential Equation
arXiv:2512. 07212v3 Announce Type: replace Abstract: Imitation learning with diffusion models has advanced robotic control by capturing the multi-modal action distributions.
arXiv Machine Learning
Jul 10
SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment
arXiv:2511. 08583v2 Announce Type: replace-cross Abstract: Developing efficient and accurate visuomotor policies poses a central challenge in robotic imitation learning.
arXiv AI
Jul 29
A context-adaptive policy framework for robust and reactive robotic manipulation via uncertainty-aware imitation learning
arXiv:2410. 24035v2 Announce Type: replace-cross Abstract: Generating robust and reactive manipulation strategies that can adapt to changing context information is a challenging task in robotics.
arXiv Machine Learning
Jun 10
MODIP: Efficient Model-Based Optimization for Diffusion Policies
arXiv:2606. 10825v1 Announce Type: new Abstract: Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC).
arXiv AI
6d ago
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
arXiv:2605. 12236v2 Announce Type: replace-cross Abstract: Fine-tuning pre-trained robot policies with reinforcement learning (RL) often inherits the bottlenecks introduced by pre-training with behavioral cloning (BC), which produces narrow action distributions that lack the coverage necessary for downstream exploration.