All papers

This paper presents a depth-conditioned adapter that converts unitless trajectory predictions from visual navigation policies into metric paths.

This paper develops a constrained differentiable optimization method for quadrotor obstacle avoidance using smooth clearance functions.

This paper proposes a Riemannian pullback regularization method enforcing non-holonomic vehicle kinematics into latent world models for autonomous driving.

This paper introduces a training method using reconstruction-guided 3D flow to teach world dynamics to vision-language-action models.

This paper presents a multi-floating-link parallel wire-driven robot system coupled via an inter-body wire.

This paper adapts dynamic movement primitives across segmented pouring phases for one-shot learning of latte art manipulation.

This paper introduces a benchmark evaluating how language model agents reuse embodied skills to complete physical tasks on robots.

This paper evaluates the sensitivity of robot world-action models to rephrased spatial language instructions across manipulation scenes.

This paper introduces a tactile conditioning framework separating contact signals into distinct temporal roles for robotic insertion policies.

This paper proposes a runtime failure monitor combining visual anomaly detection with action-conditioned future risk forecasting for robotic policies.

This paper presents an audio-conditioned vision-language-action policy enabling robots to perform manipulation tasks using sound feedback.

This paper proposes estimating kinematic constraints from generative policy ensembles to tune impedance control gains for articulated object manipulation.

This paper uses graph neural networks to predict obstacle avoidance modes, speeding up multimodal model predictive control for autonomous systems.

This paper presents an experimental study protocol measuring how humorous robot apologies affect user trust recovery after robot errors.

This paper introduces a robot manipulation model coordinating semantic reasoning, world modeling, and actions via shared predictive intent tokens.

This paper presents a benchmark evaluating automated repair of real robot navigation software bugs using physics-simulation behavioral oracles.

This paper introduces an industrial benchmark for evaluating long-horizon robotic assembly across diverse DIN-norm compliant manipulation tasks.

This paper proposes an on-policy adaptation framework to adjust legged robot locomotion policies when robot foot morphology or footwear changes.

This paper proposes a dual-branch generative framework that separates surface texture from contact force signals in vision-based tactile sensors.

This paper introduces a geometric framework to model aerodynamic promptness and control authority for tilting-propeller multirotor drones.

This paper presents a nonlinear knee-ankle parallel mechanism coupling that shifts actuator mass closer to the hip in humanoid legs.

The paper evaluates generalization limits in world action models by decoupling future image generation from an inverse-dynamics action recovery model.

The paper presents a framework combining physical validation, finite element simulations, and graph neural networks for real-time soft tactile deformation modeling.

The paper formulates risk-constrained temporal logic planning in Gaussian belief spaces as a sound and complete mixed-integer convex program.

The paper presents a redundancy-resolution method for bimanual humanoid teleoperation combining adaptive elbow guidance with singularity-robust wrist tracking.

The paper introduces a predictive joint-feasibility margin to prevent conflicting acceleration limits in second-order control barrier function quadratic programs.

The paper introduces a gradient-free inference-time control method that adapts diffusion model noise distributions to meet robotic execution constraints.

The paper proposes a task-space motion-force nonlinear model predictive control framework designed to generalize across robotic platforms and unmodeled interactions.

The paper demonstrates that language model coding agents can generate reactive and declarative programs for continuous robot behaviors using targeted guidelines.

The paper presents a navigation framework for humanoids that couples LiDAR safe corridors with footstep planning to negotiate dynamic, non-convex obstacles.

The paper proposes a physics-aware simultaneous localization and mapping method that models dynamic deformable objects using 4D Gaussian fields and spring-mass mechanics.

The paper presents a toolkit combining a neural network and keypoint tracking for dynamic pedestrian re-identification and future occupancy forecasting.

The paper investigates expert intervention delays in robot imitation learning and introduces a method to learn cost functions from delayed interventions.

The paper develops reach-avoid control certificates and safety filters over an infinite horizon for contact-based stochastic hybrid dynamical systems.

The paper presents a benchmark evaluating household robot planners across long horizons, uncertainty, and varied initial memory states.

The paper proposes a control barrier function filter that makes minimal adjustments to guarantee recurrent visits to target sets within specific deadlines.

The paper evaluates a frozen frontier vision-language model used directly as a robot policy on long-horizon memory-dependent manipulation tasks.

The paper introduces a framework to learn coupled observation-action symmetries from offline robot interaction data using adversarial tuple matching.

The paper audits trajectory-smoothness curation metrics and finds they reflect episode length rather than task success in imitation learning datasets.

The paper presents a framework to predict 3D scene flow conditioned on actions using 2D observations for kinematics-free robot control.

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.