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Learning Robust Visuomotor Policies via Dynamics Alignment

arXiv:2510.27114v2 Announce Type: replace-cross Abstract: Generative BC policies model expert action distributions well, but they do not explicitly reason about what scene a candidate action would pro

Learning to Assist: Collaborative VLAs for Implicit Human-Robot Collaboration

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DGX agent

arXiv:2606.12475v2 Announce Type: replace Abstract: Human-robot collaboration (HRC) combines the complementary strengths of humans and robots to improve task efficiency. However, many existing collabo

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14 Oct 2026

Learning to See through Action: Predictive Curiosity for Robots

arXiv:2610.14302v1 Announce Type: new Abstract: Humans and animals do not learn vision from a fixed set of data: they move, interact, and learn from the visual consequences of their actions. Motivated

Learning Transferable World-Action Models from Task-Paired Human Videos

arXiv:2610.14351v1 Announce Type: new Abstract: World-action models inherit broad visual and physical priors from video pre-training. However, standard within-video next-frame prediction allows them t

Learning When to Transfer: Reinforcement Learning for Carbon-Footprint Modeling in New Manufacturing Domains

arXiv:2610.18730v1 Announce Type: new Abstract: Manufacturers often need to characterize a new material, process, or product before enough operating data are available to train reliable predictive mod

LERR-Nav: Learning to Coordinate Exploration, Recovery, and Reminiscing for Object Navigation

arXiv:2610.15362v1 Announce Type: new Abstract: Object Navigation in unseen environments remains a fundamental challenge in embodied AI, as it requires agents to generalize visual and spatial reasonin

Less is More: Label-Guided Efficient Summarization of Procedural and Instructional Videos

arXiv:2601.12243v3 Announce Type: replace Abstract: Video summarization transforms long videos into concise representations that are easier to document and analyze, especially in high-stakes domains s

LightVAE: Towards Compact and Efficient Video Autoencoders

arXiv:2610.14726v1 Announce Type: new Abstract: Latent diffusion models enable high-quality video generation, but video VAE encoding and decoding remain computationally expensive. Existing methods use

LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration

arXiv:2610.14612v1 Announce Type: new Abstract: Chiplet technology is a key approach for extending Moore's Law. However, its design process is constrained by two primary challenges: the scarcity of ea

LLM Judges Have Dark Current: Stratified Null and Utility Tests for LLM Judge Reliability

arXiv:2606.15610v2 Announce Type: replace-cross Abstract: Passing an exact-clone null gate does not entail passing a matched target-selection utility gate. We call a non-tie preference between exact d

LLM Value Functions for Credit Assignment in Agentic RL

arXiv:2610.15840v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language model (LLM) agents remains challenging in long-horizon tasks with sparse outcome rewards, where assigning

Load-Aware Quadruped Navigation with Online Contact-based Geometry Estimation

arXiv:2610.14596v1 Announce Type: new Abstract: Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots. Compared with works that focused on load-aw

Locomotion with Online Awareness of Dynamics for Precision Footholds

arXiv:2610.17930v1 Announce Type: new Abstract: Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small. We present LOAD, a perceptive locomo

LongForcing: Scaling Temporal Manifold Matching for Long-Horizon World Rollout

arXiv:2610.18289v1 Announce Type: new Abstract: Autoregressive video diffusion models have emerged as a foundation for long-horizon world rollout. However, when extended far beyond their training wind

Look Before You Move: Agent-Interface-Aligned Supervision for Open-World Mobile Manipulation

arXiv:2610.15572v1 Announce Type: new Abstract: Open-world mobile manipulation requires a robot to find an object, approach it, and move it to a target receptacle from a language instruction. Training

LoOPSD: On-Policy Self-Distillation with Looped Teachers

arXiv:2610.18162v1 Announce Type: new Abstract: On-policy self-distillation improves a model using guidance from the model itself, but it still requires a teacher that provides more useful predictions

LoopWAM: Closed-Loop Generalist Policy Improvement from Deployment Rollouts

arXiv:2610.18873v1 Announce Type: cross Abstract: Scaling expert demonstrations alone does not enable generalist robot policies to improve from deployment. Real-world rollouts contain successes, subop

MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation

arXiv:2610.17014v1 Announce Type: cross Abstract: Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions. Generation of functional connec

Make {pi}0.5 Learn to Call Astra for Reliable Robot Manipulation

arXiv:2610.14117v1 Announce Type: new Abstract: GPT-6 Astra provides robust embodied reasoning, but frequent external queries increase inference time and token costs. We equip a frozen pi_{0.5} policy

Making Tensor Decoupling More Robust and Less Parametric

arXiv:2610.16937v1 Announce Type: new Abstract: Tensor decoupling algorithms recover compact representations of multivariate functions by combining linear factor matrices with univariate internal nonl

MARS: Systematic Multi-Level Acceleration for World Action Models

arXiv:2610.17141v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising alternative for embodied intelligence by jointly predicting future world states and robot actions

MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation

arXiv:2610.17009v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown strong potential for aligning generative models with human intent, but adapting RL to masked generative models (MG

Measuring Learned Equivariance: An Information Theoretic Approach

arXiv:2610.17269v1 Announce Type: new Abstract: Learning symmetries through data augmentation offers an alternative to architectural equivariance, but does not guarantee that a network respects those

Memory Without Curation: Learning Multi-Stream Latent Memory for Robot Policies

arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks. However, directly conditioning policies on all past

MemoryVLN: From Ego-Aware Memory Representation to Cross-Context Memory Reuse in Vision-Language Navigation

arXiv:2610.18478v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have shown strong potential for embodied vision-language navigation (VLN), yet their performance degrades on long-h

MERIT: Multimodal Evidence Reasoning over Image Tables

arXiv:2610.17937v1 Announce Type: new Abstract: Table reasoning with language models typically assumes access to clean textual tables. In practice, tables are often serialized into text, which can cor

Mesh-Free Convolution: Learned Spectral Attenuation and Transport

arXiv:2610.15931v1 Announce Type: new Abstract: Learning solution operators for partial differential equations (PDEs) requires models that can represent spatial structure across grids and scales. This

MicroCLIP: Connecting Text and Microscopy Images via Contrastive Language--Image Pretraining

arXiv:2610.17866v1 Announce Type: new Abstract: Understanding fluorescence microscopy images is fundamental to automating biological research. However, existing methods typically learn predefined conc

MIRROR: Reflecting Hidden Symmetries from Offline Robot Interaction Data

arXiv:2610.17422v1 Announce Type: new Abstract: Symmetry-aware robot policy learning requires coupled observation-action transformations. For pixel observations, even simple physical symmetries can in

Mixing Regularization for Average-Reward Q-Learning and Q-Planning

arXiv:2610.14988v1 Announce Type: new Abstract: The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlying

Mixture of GFlowNets

arXiv:2610.17007v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn a policy that samples compositional objects in proportion to an unnormalized reward R(x). This makes them use

ML Engineering Agents - A Data Management Perspective

arXiv:2610.16919v1 Announce Type: cross Abstract: Machine learning engineering (MLE) agents are emerging as a new paradigm for automating end-to-end ML pipeline development. Using large language model

Mobility Helps Learning: Unsupervised Model Adaptation for Object Recognition via Movement

arXiv:2610.15846v1 Announce Type: new Abstract: Movable agents, such as autonomous vehicles and robots, should be able to autonomously adapt their pre-trained object recognition models to new environm

Model-Based Online Decision Making via Generative Trajectory Planning

arXiv:2610.18677v1 Announce Type: new Abstract: Online decision-making faces a tension between efficient inference and long-horizon reasoning. Value-based methods rely on short-horizon bootstrapping t

More Domains, No Gains? FLO: Fast-Slow Reciprocal Learning for Multi-Domain Graph Pre-training

arXiv:2610.14748v1 Announce Type: new Abstract: Inspired by the great success of foundation models in the development of artificial general intelligence, Graph Foundation Models (GFMs) have recently a

MOSAIC: Mode- and Support-Aware Inference-Time Control of Robot Diffusion Models

arXiv:2610.17001v1 Announce Type: new Abstract: Generative policies such as diffusion models provide expressive data-driven priors for robot behavior, yet the situations a robot faces at execution tim

Near-Optimal Regret Bounds for Monotone Stochastic Optimization under Semi-Bandit Feedback

arXiv:2610.17320v1 Announce Type: new Abstract: Stochastic optimization models sequential decision-making under uncertainty, typically assuming known input distributions. When these distributions are

NetRL: Powering Network Decisions via NetGRPO-Driven Reinforcement Learning

arXiv:2610.16226v1 Announce Type: cross Abstract: Network decision-making tasks, including adaptive bitrate streaming, load balancing, and congestion control, require policies to process heterogeneous

Neural Evidence and Behavioural Welfare Economics: When Can Neuroeconomics Inform Policy?

arXiv:2511.19548v2 Announce Type: replace Abstract: Neuroeconomics appears to offer welfare economics an evidential supplement, since neural data may help distinguish choices that express genuine pref

Not All Features Are Created Equal: A Mechanistic Study of Vision-Language-Action Models

arXiv:2603.19233v2 Announce Type: replace Abstract: A fine-tuned Vision-Language-Action (VLA) policy can easily place the alphabet soup in the basket on command, just as it saw during fine-tuning. How

Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies

arXiv:2610.15690v1 Announce Type: cross Abstract: We establish uniform score bounds and Lipschitz continuity for truncated Gaussian, Cauchy, Boltzmann, and softmax policies under explicitly stated par

OATH: Object-Agnostic Tracking for Dexterous Hands, Enabling Rapid Physics-based Retargeting

arXiv:2610.14379v1 Announce Type: new Abstract: Retargeting human demonstrations into dynamically feasible dexterous hand trajectories requires resolving complex contact physics, yet current methods e

ObsSpec: Accelerating Agentic Reinforcement Learning with Observation Speculation

arXiv:2610.15246v1 Announce Type: new Abstract: Tool-using agents alternate between model generation and tool execution, waiting for each tool call to complete before starting the next generation. Thi

OIL: An Object Interaction Layer for Humanoid Whole-Body Control

arXiv:2610.14033v1 Announce Type: new Abstract: Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint o

Omni-Modal LLMs Know More Than They Say

arXiv:2610.16135v1 Announce Type: cross Abstract: Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language

OmniRSI: Taming Latent Diffusion for Unified Multimodal Remote Sensing Image Generation

arXiv:2610.15414v1 Announce Type: new Abstract: Multimodal remote sensing image generation emerges as a promising paradigm for recovering complete and complementary observations from heterogeneous and

On Making SE(2)-Invariant Networks Optimal

arXiv:2610.17424v1 Announce Type: new Abstract: Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks ar

On the Dynamics of Delayed Generalization in Linear Models

arXiv:2602.08302v2 Announce Type: replace Abstract: Delayed generalization, where training accuracy becomes nearly perfect substantially before test accuracy, has often been studied in the context of