arXiv AI
Jul 17
VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents
arXiv:2606. 19729v2 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
VOiLA: Vectorized Online Planning with Learned Diffusion Model for POMDP Agents
arXiv:2606. 19729v1 Announce Type: cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
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arXiv AI
Sep 2
Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs
arXiv:2609.01351v1 Announce Type: cross Abstract: Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimens...
arXiv AI
Jun 16
PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty
arXiv:2606. 15654v1 Announce Type: cross Abstract: Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive.
arXiv AI
Jun 4
Vectorized Online POMDP Planning
arXiv:2510. 27191v5 Announce Type: replace-cross Abstract: Planning under partial observability is an essential capability of autonomous robots.
arXiv Computer Vision
Aug 21
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
arXiv Machine Learning
Aug 28
Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
The paper introduces Planning Diffusion Policy Optimization (PDPO), an offline‑to‑online reinforcement‑learning framework that employs a diffusion policy to produce short‑horizon action chunks for robot crowd navigation. PDPO is pretrained on collision‑avoidance demonstrations and fine‑tuned online with PPO, generating five‑step action sequences applied in a receding‑horizon manner. The authors also identify a benchmark artifact where agents can leave the valid domain without explicit boundary constraints, and they mitigate this by treating boundary violations as collisions, leading to improved success rates over strong baselines.
Hugging Face Trending Papers
Aug 20
RoMAN-Flow: Taming Autoregressive Normalizing Flows for Offline Reinforcement Learning in Robotic Manipulation
Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusion- and flow-matching robot policies lack tractable likelihoods, limiting their use in likelihood-based offline RL post-training.
arXiv Machine Learning
Jul 17
DriftWorld: Fast World Modeling through Drifting
arXiv:2607. 15065v1 Announce Type: cross Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly.
arXiv Machine Learning
Aug 20
Reinforced Planning with Latent World Models
Reinforced Planning with Latent World Models (RP1) is a novel method that learns to evaluate imagined outcomes via a critic and to improve multi‑step plans through an optimizer trained offline on world‑model roll‑outs. It is the first approach to fully learn plan improvement and can be attached to any pretrained latent world model. In experiments on visual navigation, arm reaching, and robotic manipulation, RP1 outperforms hand‑designed search algorithms, achieving near‑perfect success while using far fewer roll‑outs and running up to 67× faster than the strongest alternative.
arXiv AI
Jun 24
LaGO: Latent Action Guidance for Online Reinforcement Learning
arXiv:2606. 24669v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for planning and sequential decision-making, but prior work often relies on using them as direct controllers, which requires precise action generation and can be unreliable in practice.
arXiv AI
Jun 16
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.
arXiv AI
Sep 1
Learning Action Models with Conditional and Quantified Effects via Uncertainty-Guided Exploration
The paper introduces OHCAM, an online method for learning action models that include conditional and quantified effects from limited interactions. It maintains a belief over possible models and actively chooses actions that maximize disagreement among hypotheses to reduce uncertainty, while handling noisy observations. Starting with simple hypotheses, OHCAM expands complexity only when necessary, achieving sample‑efficient learning that outperforms baselines on benchmark domains and is validated on a Kinova Gen3 robot.