All papers

Presents a model predictive contouring control framework for robotic manipulators tracking paths under moving reference frames.

Introduces a GPU-accelerated differentiable simulator to optimize contact-rich manipulation trajectories for robot hands via batched model predictive control.

Presents a plug-and-play pedestrian dead reckoning sensor module for wearable navigation in GPS-degraded environments.

Proposes a hierarchical delivery routing framework across building, floor, and room levels that incorporates access and privacy constraints.

Presents a visual safety certification framework using depth-normalized monocular optical flow to certify collision risk without 3D geometric reconstruction.

Introduces a robot manipulation policy that caches vision-language model outputs once per episode to accelerate action prediction without recomputing task context.

Develops a modular inverse-dynamics workflow to recover unique actuator and joint wrenches in closed-chain robotic mechanisms.

Introduces a dataset and spatial-memory policy model to evaluate sound-triggered mobile robot manipulation across unseen multi-scene environments.

Proposes a 2D image feedback perception and control framework for autonomous endovascular navigation using centerline graphs and rule-based controllers.

Presents a wearable tag and cloud platform for tracking workers seamlessly across indoor and outdoor industrial environments.

Presents a conversational editing framework that aligns language instructions with measurable kinematic events for humanoid motion generation.

Proposes a two-level on-policy distillation method to consolidate specialized skills from multiple teacher models into one flow-based policy.

Introduces an attribution-gated estimation framework to separate intended user motion from voluntary torque capability in assistive robotics.

Fuses neural and muscular signals into an adaptive exoskeleton control strategy to distinguish user inability from mental disengagement.

Develops a selective query selection and reward framework to make vision-language models efficient preference guides for robotic manipulation.

Proposes test-time hidden-state steering along expert demonstration geometries to improve the robustness of imitation learning policies.

Introduces a multimodal world model and dataset to anticipate impending execution anomalies during contact-rich robotic manipulation.

Presents a unified policy learning prehensile and non-prehensile actions together for reaching object poses across diverse objects and grippers.

Identifies metric flaws in the Bench2Drive benchmark where comfort scores reward stationary vehicles, and proposes a distance-based correction.

Extracts object trajectories from single-shot human demonstration videos to train generalizable manipulation policies without explicit action labels.

Formulates a time-optimal ergodic search method that accounts for target return phases to reduce total robotic retrieval time.

Introduces an augmented Lagrangian trajectory optimizer based on affine geometric heat flow to handle constrained mechanical systems.

Presents an open-source reflex controller enabling sub-millisecond mode switching for safety-critical physical human-robot interaction.

Studies how allocating training samples to auxiliary prediction targets affects performance in vision-language-action models under a fixed budget.

Proposes a teacher-student world-action model that uses latent actions and visual predictions to learn robot control from action-free videos.

Develops a physics-grounded digital twin prototype to de-risk robotic welding processes for space manufacturing and modular assembly.

Compares fractional-order and integer-order viscoelastic models to evaluate human perceptual realism during haptic rendering.

Presents an event-driven neuromorphic visual navigation paradigm for micro aerial vehicles combining spiking neural networks with neuromorphic hardware.

Introduces a test-time verification method using latent world models to detect critical states and select better actions in flow-based policies.

Proposes an active visual tracking framework that optimizes robot motion to prevent pedestrian occlusions when following a target person.

Analyzes the planning-execution gap in frozen world-model planners and adapts trajectory representations to downstream controller shifts.

Develops a control barrier function allocator to preserve wrench feasibility during slow servo reconfigurations on tiltrotor aerial robots.

Designs an onboard pipeline using a lightweight language model to infer obstacle-specific safety clearance for aerial robot navigation around humans.

Presents a closed-form triangle splatting framework for efficient novel-view synthesis by linearly solving spherical harmonics over a known mesh.

Introduces a residual adapter for pretrained general motion trackers to give humanoid robots compliant, force-aware responses to physical disturbances.

This paper introduces an adaptive visual token compression method to reduce latency and computation during vision-language-action robot policy inference.

This paper integrates whole-body model predictive control with online parameter estimation to improve humanoid robot teleoperation under heavy payloads.

This paper presents a method for flow matching robotic policies that guides behavior mode selection through targeted source-space distributions.

This paper presents an autonomous motion planning and vision-guided control framework for fuel-efficient hopping maneuvers of lunar drones.

This paper introduces a framework that learns fixed-size latent temporal representations to guide action policies in long-horizon robotic manipulation.

This paper presents an active inspection planning framework using image-based autoencoder variance to account for harsh lighting variations in orbit.

This paper proposes a reinforcement learning framework trained entirely in simulation for path-safe hydroponic lettuce harvesting with physical robot arms.

This paper introduces a vision-tactile-action model pretrained on human hand data for contact-rich dexterous robotic manipulation.

This paper presents a framework using cached value predictions as memory to guide online reinforcement learning for long-horizon robotic manipulation.

This paper analyzes normalization by evaluation within an untyped domain model by treating variable substitutions as operations.

This paper audits quantum software testing tools and benchmarks against 410 real-world defects to identify gaps in fault coverage.

This paper audits quantum software testing tools and benchmarks against 410 real-world defects to identify gaps in fault coverage.

This paper proposes a tokenized joint source-network coding framework to improve video streaming resilience against network packet loss.

This paper proposes a tokenized joint source-network coding framework to improve video streaming resilience against network packet loss.

This paper presents a control and communication framework using reachable sets and barrier functions to guarantee multi-agent safety during intermittent communication.

This paper presents a control and communication framework using reachable sets and barrier functions to guarantee multi-agent safety during intermittent communication.

This paper introduces a posetal game framework for auditable multi-agent motion planning under partially ordered legal constraints.

This paper introduces a posetal game framework for auditable multi-agent motion planning under partially ordered legal constraints.

This paper presents a spectral Duhamel-Picard method and physics-informed neural operator for simulating post-exposure bake processes in semiconductor manufacturing.

This paper presents a spectral Duhamel-Picard method and physics-informed neural operator for simulating post-exposure bake processes in semiconductor manufacturing.

This paper presents a spectral Duhamel-Picard method and physics-informed neural operator for simulating post-exposure bake processes in semiconductor manufacturing.

This paper presents a short, simplified proof establishing a polylogarithmic upper bound for list replicability across binary concept classes.

This paper presents a short, simplified proof establishing a polylogarithmic upper bound for list replicability across binary concept classes.

This paper presents a short, simplified proof establishing a polylogarithmic upper bound for list replicability across binary concept classes.

This paper introduces a latent annealed posterior sampling framework with a variance-preserving bridge for inferring random fields from sparse observations.