A flow-adaptive ergodic coverage formulation using MMD that preserves guarantees over evolving domains and supports open-loop planning for robots in flows.
Canonical reference
Monocular visual-inertial odometry in low-textured environments with smooth gradients: A fully dense direct filtering approach,
Canonical reference. 80% of citing Pith papers cite this work as background.
23
Pith papers citing it
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80% of classified citations
citation-role summary
background 8
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representative citing papers
Timed reward machines extend reward machines with timing constraints, allowing model-free RL algorithms to learn policies that satisfy precise temporal requirements on standard benchmarks.
BEVCALIB performs LiDAR-camera calibration from raw data by fusing camera and LiDAR bird's-eye view features with a novel feature selector and reports state-of-the-art accuracy on KITTI and NuScenes.
Residual feature integration with a trainable target-side encoder provably prevents negative transfer, achieving convergence rates no worse than training from scratch under informative target distributions.
INSANE releases multiple MAV datasets with cross-environment trajectories, rich multi-IMU and camera suites, high-rate vibration data, and sub-centimeter RTK GNSS ground truth for localization research.
A LiDAR-inertial odometry pipeline supplies deterministic feasible sets as protection levels by linking ICP point-cloud noise to pose uncertainty via a closed-form relation and propagating it with an on-manifold ellipsoidal set-membership filter.
RigidFormer learns mesh-free rigid dynamics from point clouds using object-centric anchors, Anchor-Vertex Pooling, Anchor-based RoPE, and differentiable Kabsch alignment to enforce rigidity.
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
BiCICLe frames bimanual robot control as a multi-agent leader-follower problem with Arms' Debate and an LLM judge, achieving up to 71.1% success on 13 TWIN benchmark tasks without fine-tuning.
A framework uses standardized US license plate typography and geometry as passive fiducials for metric monocular distance, velocity, and time-to-collision estimation without machine learning training.
New knot classification benchmark and topology-aware supervision methods yield small specificity gains but confirm that appearance bias remains the dominant failure mode.
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
Bee hive mind from weighted voter imitation equals a single RL agent using a new multi-armed bandit rule called Maynard-Cross Learning.
A simulation-trained deep deformation model combined with online adaptive control enables zero-shot autonomous tissue retraction for ROI exposure in robotic surgery.
Systematic grasping strategies for paper-like materials are developed and tested with a soft gripper by exploiting environmental constraints to improve force control and success rates.
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
PRISM downsamples point clouds by stratifying on RGB color bins with a maximum capacity k per bin to preserve high chromatic diversity regions over homogeneous surfaces.
RSR-RSMARL is a robust safe MARL framework with V2V communication and CBF safety shields that supports zero-shot sim-to-real transfer and improves coordination on 1/10-scale vehicle hardware.
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.
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.
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.
citing papers explorer
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Asymptotically Optimal Ergodic Coverage on Generalized Motion Fields
A flow-adaptive ergodic coverage formulation using MMD that preserves guarantees over evolving domains and supports open-loop planning for robots in flows.
-
About Time: Model-free Reinforcement Learning with Timed Reward Machines
Timed reward machines extend reward machines with timing constraints, allowing model-free RL algorithms to learn policies that satisfy precise temporal requirements on standard benchmarks.
-
BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations
BEVCALIB performs LiDAR-camera calibration from raw data by fusing camera and LiDAR bird's-eye view features with a novel feature selector and reports state-of-the-art accuracy on KITTI and NuScenes.
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Residual Feature Integration is Sufficient to Prevent Negative Transfer
Residual feature integration with a trainable target-side encoder provably prevents negative transfer, achieving convergence rates no worse than training from scratch under informative target distributions.
-
INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators
INSANE releases multiple MAV datasets with cross-environment trajectories, rich multi-IMU and camera suites, high-rate vibration data, and sub-centimeter RTK GNSS ground truth for localization research.
-
Safety-Critical LiDAR-Inertial Odometry with On-Manifold Deterministic Protection Level
A LiDAR-inertial odometry pipeline supplies deterministic feasible sets as protection levels by linking ICP point-cloud noise to pose uncertainty via a closed-form relation and propagating it with an on-manifold ellipsoidal set-membership filter.
-
RigidFormer: Learning Rigid Dynamics using Transformers
RigidFormer learns mesh-free rigid dynamics from point clouds using object-centric anchors, Anchor-Vertex Pooling, Anchor-based RoPE, and differentiable Kabsch alignment to enforce rigidity.
-
Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization
Dr-BA delivers a separable optimization approach for direct radar bundle adjustment and cross-session localization using full spinning-radar intensity images, achieving state-of-the-art performance on over 200 km of on-road data.
-
Bimanual Robot Manipulation via Multi-Agent In-Context Learning
BiCICLe frames bimanual robot control as a multi-agent leader-follower problem with Arms' Debate and an LLM judge, achieving up to 71.1% success on 13 TWIN benchmark tasks without fine-tuning.
-
Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography
A framework uses standardized US license plate typography and geometry as passive fiducials for metric monocular distance, velocity, and time-to-collision estimation without machine learning training.
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Physical Knot Classification Beyond Accuracy: A Benchmark and Diagnostic Study
New knot classification benchmark and topology-aware supervision methods yield small specificity gains but confirm that appearance bias remains the dominant failure mode.
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Iteratively Learning Muscle Memory for Legged Robots to Master Adaptive and High Precision Locomotion
Integrates iterative learning control with a torque library to enable high-precision adaptive locomotion on bipedal and quadrupedal robots, reducing tracking errors by up to 85% and achieving over 30x faster control rates.
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The Hive Mind is a Single Reinforcement Learning Agent
Bee hive mind from weighted voter imitation equals a single RL agent using a new multi-armed bandit rule called Maynard-Cross Learning.
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Learning-Based Adaptive Control for Surgical Robotic Exposure Task on Deformable Tissues
A simulation-trained deep deformation model combined with online adaptive control enables zero-shot autonomous tissue retraction for ROI exposure in robotic surgery.
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Introducing Environmental Constraints to Grasping Strategies for Paper-Like Flexible Materials Using a Soft Gripper
Systematic grasping strategies for paper-like materials are developed and tested with a soft gripper by exploiting environmental constraints to improve force control and success rates.
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Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production
PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.
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PRISM: Color-Stratified Point Cloud Sampling
PRISM downsamples point clouds by stratifying on RGB color bins with a maximum capacity k per bin to preserve high chromatic diversity regions over homogeneous surfaces.
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Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware
RSR-RSMARL is a robust safe MARL framework with V2V communication and CBF safety shields that supports zero-shot sim-to-real transfer and improves coordination on 1/10-scale vehicle hardware.
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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.
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Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
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Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
AttDiCNN reaches 98.56%, 99.66%, and 99.08% accuracy on EDFX, HMC, and NCH sleep datasets via force-directed visibility graph EEG representations and a three-module attentive dilated CNN architecture.
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Foundations of Future Communication Systems: Innovations in Communication - A Report
The report assembles abstracts of invited talks, presentations, and posters from the FFCS conference on foundational limits and emerging paradigms in communication.