All papers

The paper presents an algorithm using predefined 3D maneuvers to change aircraft squadron formations from longitudinal to latitudinal without collisions.

The paper introduces a MATLAB toolkit for actuator sizing, motion dynamics verification, and specification analysis of serial robotic arms during early design.

The paper proposes a hierarchical framework that combines 3D scene graphs with reusable option policies for repeated robot navigation tasks.

The paper presents a contact-aware optimization framework to learn dexterous robot hand manipulation directly from a single human ego-centric video.

The paper presents a tactile tracking method to estimate in-gripper 3D object rotation using two vision-based tactile sensors.

The paper adds learnable registers and flow-matching loss to World Action Models to suppress visual spatial outliers and improve robot action conditioning.

This paper introduces a benchmark suite for testing embodied reasoning agents on goal planning, continuous observation tracking, and failure recovery.

This paper establishes a theoretical minimum disjunctive basis of thirteen tasks characterizing solvable multi-robot computational classes in the plane.

This paper introduces an occupancy-native world-action model that jointly generates future scene occupancy and vehicle actions using flow matching.

This paper analyzes how implicit human instructions create communication gaps and execution failures in vision-language-action robotic models.

This paper introduces a simulation framework with reactive human motion models to train whole-body assistive robots for physical mobility support.

This paper introduces a safe online reinforcement learning framework with value calibration and safety filtering for autonomous driving policies.

This paper presents a continuous trajectory safety metric combining physical priors with learning to evaluate latent collision risks in driving.

This paper proposes a rigid-body trajectory forecasting framework to select feasible contact states for proactive dynamic grasping.

This paper presents a sliding-window factor-graph estimation framework fusing multiple IMUs and continuous contact detection for legged robot odometry.

This paper introduces an information-based framework using covariance analysis to optimize sensor geometry in nuclear magnetic resonance tracking systems.

This paper introduces an agentic system that generates robot hardware designs and learns executable task behaviors through iterative execution feedback.

This paper proposes an action-conditioned world model framework designed to safely simulate interactions for reinforcement learning on vision-language-action policies.

This paper introduces a perceptive locomotion framework that infers dynamic changes and payload variations to navigate narrow-beam footholds.

This paper presents a method that updates future-action beliefs from execution feedback to repair only affected actions in manipulation chunks.

This paper introduces diagnostic reasoning and structured guidance prompts to correct mode-averaging errors in vision-language-action manipulation policies.

This paper presents an adaptation framework combining kinematic priors and reinforcement learning to transfer whole-body controllers across humanoid embodiments.

This paper proposes a set-membership filtering architecture using contractor programming for continuous-time state estimation under bounded uncertainty and outliers.

This paper introduces a real-world multi-sensor benchmark and dataset for outdoor wheel-legged robot perception, navigation, and scene reconstruction.

This paper proposes a predictive contact-guidance framework that forecasts tactile outcomes to steer generative robot manipulation actions before execution.

This paper presents a modular robotic system that detects tomato plant diseases and executes targeted pesticide spraying in commercial greenhouses.

This paper presents a latent skip planning framework that distills world models to speed up visual navigation rollout and planning.

This paper introduces a real-time framework using diffusion models to enable stylized legged locomotion for physics-based simulated humanoid characters.

This paper analyzes manual picking forces and presents a compliant two-jaw gripper design for robotic cherry tomato harvesting in greenhouses.

This paper introduces a trajectory reversal and temporal resampling data augmentation method to improve data efficiency in robot imitation learning.

This paper presents a differentiable vehicle dynamics framework to link perception directly to actuator-level control in end-to-end driving models.

The paper develops quality functions based on distance metrics to assess the diversity of generated test scenarios for automated driving verification.

The paper develops quality functions based on distance metrics to assess the diversity of generated test scenarios for automated driving verification.

The paper introduces OneDemo, a reinforcement learning approach that edits a single expert demonstration to generate diverse valid trajectories for robot tasks.

The paper introduces a method to convert human affordance labels into robot-specific affordance maps conditioned on physical embodiment constraints.

The paper presents a composite indoor-outdoor navigation benchmark and a modular agent framework to evaluate long-horizon multi-stage robotic delivery missions.

The paper introduces CoSMO, using conformal prediction with vision-language models to generate safety-calibrated obstacle bounds for open-world robot navigation.

The paper presents DR-VLA, transferring outdoor navigation behaviors from egocentric human web videos to quadruped delivery robots via domain-invariant scene representations.

The paper introduces CrossFly, a simulation benchmark evaluating aerial robot navigation and subsequent physical interaction across combined indoor and outdoor environments.

The paper proposes a residual stiffening layer that dynamically adjusts robotic impedance control by detecting resistance without explicit force sensing.

The paper introduces a real-time surgical tracking system that integrates object detection, particle filtering, and monocular depth estimation to handle tool occlusions.

The paper proposes an optimization method that jointly designs robot hand mechanical parameters and retargeting algorithms to match human grasping postures faithfully.

The paper introduces a sonar-only mapping system that combines beamforming place descriptors, occupancy grid mapping, and pose graph optimization for indoor navigation.

The paper presents an attribute reliability monitor that tracks output consistency over time to detect failures in vision-language models for Earth observation.

The paper introduces WAVE-0, connecting vision-language-action policies with a lightweight latent world model to guide action generation through predicted future outcomes.

The paper introduces ForkCast, which evaluates counterfactual future outcomes using a world model to intervene and recover frozen vision-language-action manipulation policies.

The paper introduces RoboFQE, an off-policy evaluation method to assess visual manipulation policies directly from offline trajectories without generating new rollouts.

The paper introduces GreenWAM, a robotics foundation world model trained across twenty robot bodies using a shared anatomical action space.

The paper presents Equilibrium Energy Control, using an energy landscape to detect policy uncertainty and trigger expert takeovers for lifelong robot learning.

The paper introduces EgoMatrix, a pipeline that converts monocular egocentric human videos into physically executable bimanual dexterous robot demonstration trajectories.

The paper presents a method to transfer contact-rich manipulation behaviors to robots from a single demonstration using ordered contact trajectory prompts.

The paper proposes a masked diffusion framework to plan coordinated paths and recharge schedules for collaborative aerial and ground robot teams under constraints.

The paper decouples contact-rich manipulation learning into a demonstration-trained future prediction model and an inverse dynamics model trained on play data.

The paper introduces interaction latents that combine motion, contact, and force trajectories into unified action representations for contact-rich robot manipulation.

The paper introduces an agentic runtime that uses separate perception tools, rule-based verification, and feedback-driven recovery to improve closed-loop driving safety.

The paper presents EmbodiedEvo, a framework where coding agents self-improve physical manipulation policies by evolving reusable skills and lessons through execution feedback.

The paper translates Liquid Haskell into Rocq to provide formal semantics and verify refinement types using an interactive theorem prover.

Formalizes programming language denotational semantics directly in the Agda proof assistant without heavy reformulation.

Mechanizes contravariant universe subtyping in Martin-Löf type theory using the Rocq proof assistant.

Introduces a scenario-based proof methodology to simplify the formal verification of distributed protocols.