All papers

Evaluates LLM judges assessing AI-generated database specifications against trivial baselines using a leakage-resistant benchmark.

Empirically analyzes how refactoring commits correlate with software quality metrics like complexity, code smells, and vulnerabilities.

Empirically analyzes how refactoring commits correlate with software quality metrics like complexity, code smells, and vulnerabilities.

Introduces an execution-grounded benchmark to evaluate code generation for real-world web service and API calls across multiple models.

Evaluates coding agents on generating full application test suites from specifications using a benchmark of open-source projects and mutants.

Uses agentic reasoning over program concurrency semantics to steer thread scheduling and reproduce elusive concurrency bugs.

Proposes an agentic workflow that drafts and maintains Architecture-as-Code documentation with mandatory human review gates in the IDE.

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.

Presents an external revisable memory layer that tracks and retracts dynamic manipulation conditions for frozen vision-language-action policies.

Develops a simulation platform and policy framework for robot navigation combining visual observations with overheard ambient Wi-Fi signals.

Evaluates geometric verifiers that coordinate multi-robot path planning by translating trajectory conflicts into structured feedback for language models.

Presents a three-stage learning framework for whole-body reactive humanoid control from egocentric visual inputs to catch flying objects.

Combines iterative covariance steering and adaptive control to provide safe proximity navigation for spacecraft around small celestial bodies.

Presents an integrated software architecture combining stereovision, augmented reality, and tracking modules for real-time surgical robotic navigation.

Develops an online data-driven predictive control method using tactile feedback for secure and gentle robotic grasping of unfamiliar objects.

Introduces a two-agent memory architecture with an auditing mechanism to ground long-horizon episodic context into symbolic subgoals for robot policies.

Presents an automated solver for cubic spline interpolation to generate smooth, continuous waypoint trajectories for autonomous motion systems.

Proposes an action-space grounding method that projects tool affordances to transform pretrained vision-language models into robotic manipulation policies.

Proposes a counterfactual training method that mitigates head-task action bias in robot policies by isolating instruction- and action-relevant visual evidence.

Develops a temporal soft mixture-of-experts teacher-student distillation framework for versatile robot navigation across dynamic environments.

Introduces a token-allocation scheme combining sparse future references and dense execution targets within world action models.

Combines kinodynamic sampling-based motion planning with latent visual world models to solve long-horizon manipulation tasks.

Presents a lightweight vision-language-action policy trained with optical-flow future motion prediction to guide robot manipulation.

Proposes an adaptive whole-body control framework combining model predictive control, posture planning, and balance compensation for humanoids lifting unknown payloads.