All papers

The paper analyzes physical recursive self-improvement in robotics, proposing a multi-level evaluation framework and reviewing existing adaptation methods.

The paper introduces a benchmark to measure vision-language-action model robustness against hardware, optical, and physical robot setup variations.

The paper presents a dataset and framework with dense progressive rewards to train language agents on long-horizon tasks.

The paper studies how demonstration quality weighting, filtering, and conditioning affect vision-language-action model pretraining and downstream performance.

The paper introduces action chunking for agentic robotics to execute pre-planned primitive routines without redundant model reasoning at each step.

This paper analyzes visual token utilization in high-resolution vision-language models to optimize token pruning across network layers.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

The paper outlines a vision for trace-guided automated program repair to address context bloat and reasoning limitations.

Summary pending (next check).

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

The paper develops a time-frequency analysis method to diagnose partial discharge acoustic signals in industrial inspection equipment.

The paper converts time-domain vibration signals into symmetrized dot pattern images to classify helical gear defects using a CNN.

The paper investigates token pruning strategies to accelerate Vision Transformer inference for object detection and instance segmentation.

The paper presents a cross-attention deep learning framework fusing real-time vital signs and motion sensor data for medical anomaly detection.

The paper introduces an inference-time framework that creates inspection plans to guide LLMs in detecting source code vulnerabilities.

The paper proposes a frequency-enhanced conditional VAE combined with graph neural networks for anomaly detection in industrial multivariate time series.

The paper presents an autonomous patrol robot combining mapping and computer vision to detect threats and manage security incidents.

Reviews clinical applications, biomechanical foundations, and methodological challenges of ultrasound and magnetic resonance elastography of the neck.

Designs and validates a miniaturized Wi-Fi HaLow wireless imaging transfer system for capsule endoscopy across biological tissues.

Presents a hierarchical framework converting single-agent policies into reusable object-oriented motion skills for multi-agent cooperative tasks.

Proposes a robot path planner that extracts navigable geometry directly from 2D Gaussian splatting maps via rasterization queries.

Decouples geometric path and execution timing in vision-language-action policies to improve adaptation from teleoperated demonstrations.

Proposes a quadrotor navigation policy estimating wind disturbances from proprioceptive history via temporal convolutions during cluttered flight.

Proposes a modular digital twin framework jointly modeling industrial processes, 5G wireless networks, and control logic for pre-deployment safety validation.

Presents a sparse traversability-aware graph representation from grid costmaps to accelerate global path planning for autonomous rovers.

Proposes a plug-and-play alignment framework enabling vision-language-action models to understand and execute negated robotic instructions.

Evaluates physical attention biases based on Euclidean and arc-length distances to improve learned simulators for deformable cable dynamics.

Combines teleoperation and kinesthetic demonstrations using object-mask-conditioned diffusion policies for contact-rich, long-horizon robotic dexterous manipulation.

Introduces a manipulation skill dataset and benchmark built on 50 activities to evaluate subtask execution in long-horizon robotic tasks.

Presents a 3D-native world action model unifying observations, actions, and scene dynamics in metric space for robot and human manipulation.

Introduces an execution-authorization interface that gates robot actions using deterministic evidence checks and budgeted re-observations under sensory uncertainty.

Combines real-time digital twins with human-in-the-loop demonstrations to help collaborative robots adapt online using reinforcement learning.

The paper presents instance-anchored interaction evidence to ground robot planning in human pointing and physical object manipulation videos.

The paper proposes RESETTLE, an execution recovery framework triggered by policy proposal disagreements using retrieval and corrective control.

The paper presents MiniWAM, a world action model predicting compact transition targets learned via inverse dynamics and feature reconstruction.

The paper introduces MAP2, combining curvature-based model predictive control with sparse Gaussian process residual learning for autonomous racing.

The paper presents a sim-to-real reinforcement learning pipeline using brief field trajectories and system identification for autonomous surface vehicles.

The paper analyzes action-prediction residuals in robot learning and proposes heteroscedastic Student-t modeling to improve regression policies.

The paper introduces a fixed-reference pose residual model to quantify cross-dataset transfer of human body pose cues for interaction anticipation.

The paper evaluates the locomotion performance and foot slip limitations of a commercial quadruped robot on sloped roof surfaces.

The paper reports field deployment findings and perception-locomotion challenges of quadrupedal robots tested in lunar regolith simulant environments.

The paper presents PLaW-VLA, a vision-language-action policy conditioning action generation on predicted latent world representations via structured attention.

The paper introduces LiteNWM, an efficient latent navigation world model that shares visual encodings to predict candidate trajectory representations.

The paper proposes a physics-informed differentiable distance learning framework using kinematics and residual networks for multi-arm collision checking.

The paper presents GLIO2, a GPU-parallel tightly-coupled LiDAR-Inertial-GNSS optimization system for robust real-time localization and mapping.

The paper introduces SCOPE, a module distilling score curvature to extract calibrated Gaussian trajectory uncertainty for real-time robot control.

The paper proposes a generative neural retargeting framework to map human manipulation demonstrations to dynamically feasible robot trajectories.

The paper proposes SpatialHarness, an online simulated scaffolding method providing complementary virtual views for fine robotic manipulation.

The paper introduces PanTact, a system for learning humanoid whole-body dexterous contact manipulation directly from wearable human demonstrations.

The paper introduces MeMoREval, a benchmark evaluating memory modalities and inference costs for embodied large language model agents.

The paper proposes graph-based heuristics combining bidirectional feasible-set generation with fixed-length searches for minimum constraint removal in motion planning.

The paper introduces an image-based 3D path-planning algorithm extending A* for navigating soft-growing robots in the spinal subarachnoid space.

The paper presents an electrothermally actuated fiber-based robot with distal segment heating for high-precision minimally invasive surgical manipulation.

The paper presents a prioritized motion-retargeting framework using an explicit priority hierarchy to transfer human motions to humanoid robots.

The paper introduces a real-robot benchmark to evaluate how reliably vision-language-action models translate spatial descriptions into physical actions.

Presents a soft pneumatic wearable support garment designed to prevent falls and assist mobility among older adults.