FactoryNet is the first universal pretraining corpus for industrial time-series data with a shared S-E-F-C schema that supports cross-embodiment transfer and competitive anomaly detection.
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Idd-x: A multi-view dataset for ego-relative important object localization and explanation in den se and unstructured traffic
31 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
A Galilean-equivariant filter jointly estimates INS navigation states and unknown GNSS time delays, preserving accuracy and consistency better than EKF in UAV flights and simulations with delays up to 500 ms.
Distance-r Independent Unlabeled Multi-Agent Pathfinding is PSPACE-complete, with reduction-based and configuration-generator algorithms that solve instances with hundreds of agents.
New framework for probabilistic safety shields in MDPs showing impossibility of strong classical guarantees and providing weaker but usable alternatives with offline and online constructions.
BehaviorBench reveals that self-play RL policies for autonomous driving overfit to their training traffic agents and do not generalize to other behaviors, motivating a hybrid rule-based plus learned planner.
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
LLM-Foraging uses off-the-shelf LLMs for decentralized tactical decisions in CPFA-based swarm foraging, collecting more resources than GA-tuned baselines across 36 varied configurations while showing greater consistency.
ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
The virtual object MPC framework enables stable shared teleoperation for transporting up to nine objects, cutting sliding distance by 72.45% and eliminating tip-overs compared to baseline.
ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
VRA is a new acceleration interface that grounds discrete-time joint commands in voltage-constrained electric actuator physics to ensure realizability.
VISOR applies VLMs to automate robot test oracles for correctness and quality assessment while reporting uncertainty, with evaluation on GPT and Gemini showing trade-offs in precision and recall but poor uncertainty calibration.
MAG-VLAQ fuses multi-modal ground and aerial data via ODE-conditioned vector-of-locally-aggregated-queries to nearly double recall@1 on aerial-ground place recognition benchmarks.
Ray-aware pointer memory with adaptive retain-or-replace updates enhances stability and accuracy in streaming 3D reconstruction.
Waypoint-based bi-level planning with curriculum RLVR improves multi-robot task success rates in dense-obstacle benchmarks over motion-agnostic and VLA baselines.
SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.
WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.
Introduces the Dual-Upper-Triangular Invariant Representation (DUTIR) as a coordinate-invariant local representation for motion and force trajectories with improved robustness to singularities and a supporting computational algorithm.
SynFlow creates a 34-times larger synthetic LiDAR scene flow dataset that lets models trained only on simulation match or beat supervised real-data baselines on multiple benchmarks.
An agentic LLM framework augmented with symbolic feedback and heuristic search over model space generates improved planning domains from natural language descriptions.
Test-time steering of pre-trained whole-body policies via sample-based planning lets legged robots generalize dynamic loco-manipulation to varied heavy objects and tasks without additional training or tuning.
RECAP enables a generalist VLA to self-improve via advantage-conditioned RL on mixed real-world data, more than doubling throughput and halving failure rates on hard manipulation tasks.
ORICF is a declarative, model-agnostic robotics framework with YAML specs and edge offloading that reduces robot compute utilization by up to 83% and energy by 66% in a ROS2 demo combining ASR, LLM, and CNN.
citing papers explorer
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FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
FactoryNet is the first universal pretraining corpus for industrial time-series data with a shared S-E-F-C schema that supports cross-embodiment transfer and competitive anomaly detection.
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Galilean State Estimation for Inertial Navigation Systems with Unknown Time Delay
A Galilean-equivariant filter jointly estimates INS navigation states and unknown GNSS time delays, preserving accuracy and consistency better than EKF in UAV flights and simulations with delays up to 500 ms.
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Distance-Constrained Unlabeled Multi-Agent Pathfinding
Distance-r Independent Unlabeled Multi-Agent Pathfinding is PSPACE-complete, with reduction-based and configuration-generator algorithms that solve instances with hundreds of agents.
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Shields to Guarantee Probabilistic Safety in MDPs
New framework for probabilistic safety shields in MDPs showing impossibility of strong classical guarantees and providing weaker but usable alternatives with offline and online constructions.
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Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
BehaviorBench reveals that self-play RL policies for autonomous driving overfit to their training traffic agents and do not generalize to other behaviors, motivating a hybrid rule-based plus learned planner.
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The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment
An AI-agent social platform generated mostly neutral content whose use in fine-tuning reduced model truthfulness comparably to human Reddit data, suggesting limited unique harm but flagging tail risks like secret leaks.
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LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging
LLM-Foraging uses off-the-shelf LLMs for decentralized tactical decisions in CPFA-based swarm foraging, collecting more resources than GA-tuned baselines across 36 varied configurations while showing greater consistency.
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ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
ESARBench is the first unified benchmark for MLLM-driven UAV agents that must explore, locate clues, and decide on victim positions in photorealistic simulated SAR environments.
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Towards Multi-Object Nonprehensile Transportation via Shared Teleoperation: A Framework Based on Virtual Object Model Predictive Control
The virtual object MPC framework enables stable shared teleoperation for transporting up to nine objects, cutting sliding distance by 72.45% and eliminating tip-overs compared to baseline.
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Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
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A global dataset of continuous urban dashcam driving
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
-
VRA: Grounding Discrete-Time Joint Acceleration in Voltage-Constrained Actuation
VRA is a new acceleration interface that grounds discrete-time joint commands in voltage-constrained electric actuator physics to ensure realizability.
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VISOR: A Vision-Language Model-based Test Oracle for Testing Robot
VISOR applies VLMs to automate robot test oracles for correctness and quality assessment while reporting uncertainty, with evaluation on GPT and Gemini showing trade-offs in precision and recall but poor uncertainty calibration.
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MAG-VLAQ: Multi-modal Aerial-Ground Query Aggregation for Cross-View Place Recognition
MAG-VLAQ fuses multi-modal ground and aerial data via ODE-conditioned vector-of-locally-aggregated-queries to nearly double recall@1 on aerial-ground place recognition benchmarks.
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Ray-Aware Pointer Memory with Adaptive Updates for Streaming 3D Reconstruction
Ray-aware pointer memory with adaptive retain-or-replace updates enhances stability and accuracy in streaming 3D reconstruction.
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Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems
Waypoint-based bi-level planning with curriculum RLVR improves multi-robot task success rates in dense-obstacle benchmarks over motion-agnostic and VLA baselines.
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SafetyALFRED: Evaluating Safety-Conscious Planning of Multimodal Large Language Models
SafetyALFRED shows multimodal LLMs recognize kitchen hazards accurately in QA tests but achieve low success rates when required to mitigate those hazards through embodied planning.
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WARPED: Wrist-Aligned Rendering for Robot Policy Learning from Egocentric Human Demonstrations
WARPED synthesizes realistic wrist-view observations from monocular egocentric human videos via foundation models, hand-object tracking, retargeting, and Gaussian Splatting to train visuomotor policies that match teleoperation success rates on five tabletop tasks with 5-8x less collection effort.
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A Coordinate-Invariant Local Representation of Motion and Force Trajectories for Identification and Generalization Across Coordinate Systems
Introduces the Dual-Upper-Triangular Invariant Representation (DUTIR) as a coordinate-invariant local representation for motion and force trajectories with improved robustness to singularities and a supporting computational algorithm.
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SynFlow: Scaling Up LiDAR Scene Flow Estimation with Synthetic Data
SynFlow creates a 34-times larger synthetic LiDAR scene flow dataset that lets models trained only on simulation match or beat supervised real-data baselines on multiple benchmarks.
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Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
An agentic LLM framework augmented with symbolic feedback and heuristic search over model space generates improved planning domains from natural language descriptions.
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Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation
Test-time steering of pre-trained whole-body policies via sample-based planning lets legged robots generalize dynamic loco-manipulation to varied heavy objects and tasks without additional training or tuning.
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$\pi^{*}_{0.6}$: a VLA That Learns From Experience
RECAP enables a generalist VLA to self-improve via advantage-conditioned RL on mixed real-world data, more than doubling throughput and halving failure rates on hard manipulation tasks.
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ORICF -- Open Robotics Inference and Control Framework
ORICF is a declarative, model-agnostic robotics framework with YAML specs and edge offloading that reduces robot compute utilization by up to 83% and energy by 66% in a ROS2 demo combining ASR, LLM, and CNN.
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What Will Happen Next: Large Models-Driven Deduction for Emergency Instances
WLDS applies large models with factual and logical calibration to produce diverse text-and-image deductions of emergency scenarios beyond what traditional fixed simulations can generate.
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E$^2$DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
E²DT couples a Decision Transformer with a k-Determinantal Point Process that scores trajectories on return-to-go quantiles, predictive uncertainty, and stage coverage to improve sample efficiency and policy quality in robotic manipulation.
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Reliability-Guided Depth Fusion for Glare-Resilient Navigation Costmaps
Reliability modeling of depth measurements enables glare-resilient occupancy grid costmaps for mobile robots.
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Robotic Nanoparticle Synthesis via Solution-based Processes
Screw-based motion planning extracted from single demonstrations enables robots to autonomously execute long-horizon nanoparticle synthesis protocols.
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Event-Triggered Adaptive Consensus for Multi-Robot Task Allocation
An event-triggered consensus framework for heterogeneous robot swarms reduces communication overhead while preserving high task completion rates and resilience to failures in simulations.
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The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
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DigiForest: Digital Analytics and Robotics for Sustainable Forestry
DigiForest integrates heterogeneous autonomous robots for data collection, automated tree trait extraction, a decision support system for growth forecasting, and autonomous harvesters for selective logging, with real-world tests in European forests.