All papers

This paper presents a morphology-conditioned learning framework to train a single locomotion policy across varied legged robot configurations.

This paper presents an adaptive architecture coordinating exploration, detection, and validation modules for object-goal navigation.

This paper develops an end-to-end differentiable reinforcement learning method for three-dimensional obstacle avoidance in quadrupedal robots.

This paper presents a training-free method to adaptively select action chunks and execution horizons to speed up robot policy inference.

This paper presents an orchestration layer that detects failures and backtracks along executed trajectories to re-attempt robot tasks.

This paper proposes a framework enabling vision-language-action models to adapt to novel manipulation tasks using a single human video demonstration.

This paper introduces an interleaved dual-hand action representation to improve cross-hand coordination in bimanual dexterous manipulation models.

This paper proposes a feedback control framework for combining control inputs and feedback signals across simulation and physical setups.

This paper introduces a method that predicts action errors to decide when and how much to refine robot action proposals.

This paper presents a modular actuation platform designed to standardize hardware and software across different continuum robot designs.

This paper presents a symbolic supervisory control architecture to formally certify safety and reachability for learned robot motion models.

This paper introduces a handheld interface that transmits physical contact forces to human operators without using force sensors.

This paper proposes an active perception method for robots to estimate human upper-limb functional mobility through repeated handover interactions.

This paper introduces a training-free token pruning method to accelerate inference in world action models without degrading action performance.

This paper introduces an indoor navigation benchmark for teams of robot agents searching for multiple multi-modal object goals.

This paper surveys learning-based tactile manipulation, covering representations, policy learning, foundation models, and sim-to-real transfer.

This paper presents a self-locking deployable linkage that enables robotic vacuum cleaners to support loads without continuous motor torque.

This paper introduces a framework that connects task reasoning to robotic action generation via intermediate atomic-motion descriptions.

The paper presents an object-centric vision-language-action framework that tracks persistent object slots and predicts future states for manipulation.

The paper proposes a visual quality estimator to adaptively balance camera and IMU weighting during bundle adjustment.

The paper introduces a hierarchical navigation policy that determines candidate routes from local geometry to prevent averaging conflicting demonstrations.

The paper introduces polynomial-time approximation schemes for multi-robot motion planning with polygonal obstacles and robots.

The paper proposes a receding-horizon safety critic and differentiable shield to prevent robot failures in reinforcement learning.

The paper introduces an adaptive model predictive control algorithm for CubeSat attitude control using only magnetorquers.

The paper introduces a large multimodal dataset of paired visual, tactile, and trajectory demonstrations for dexterous robot manipulation.

The paper proposes a student-teacher framework that distills long-horizon model predictive control into vision-guided short-horizon control.

The paper presents an automated pipeline that converts real workspace scans into simulation assets for scalable robot learning.

The paper compares discrete and continuous action parametrizations across robot foundation models in simulation and physical tests.

The paper provides a survey and unifying taxonomy of foundation models and systems for agentic robots.

The paper proposes a distributed dynamic belief propagation method for real-time trajectory planning across multiple robot arms.

The paper introduces a tiered-resolution mapping method that integrates high-resolution LiDAR data only along planned paths.

The paper establishes a dual certification framework for non-convex robot state estimation under severe sensor degeneracy.

The paper presents a graph-based reweighting method to train manipulation policies from mixed-quality demonstrations and intervention data.

The paper incorporates continuous physical time into robot policy memory to improve action timing across variable observation intervals.

The paper introduces an open-source tool enabling robots to act on spoken instructions before the speaker finishes talking.

The paper uses kinematically grounded video generation to synthesize customizable reference motions for humanoid robot loco-manipulation.

The paper presents a coordination framework for humanoid robots playing table tennis doubles with collision-avoidance planning.

The paper introduces a multi-device dataset and collaborative framework for wearable inertial human motion localization.

The paper introduces a benchmark and model for autonomous driving vision-language-action systems handling conflicting or unsafe navigation instructions.

The paper presents a Lean 4 framework to verify deployed Ethereum bytecode directly against executable virtual machine semantics.

The paper proves that typechecking is decidable for explicitly annotated terms in System F-sub-top without minimum types.

The paper characterizes scan-induced artifacts in dynamic 3D meshes to build a no-reference visual quality assessment method.

The paper characterizes scan-induced artifacts in dynamic 3D meshes to build a no-reference visual quality assessment method.

The paper shows that camera frame rate harmonics confound optical electric network frequency estimates in video forensics, failing to detect generated video.

The paper uses multi-agent language model simulations to evaluate why international news outlets replicate the agenda of major Western media.

The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows.

The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows.

The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows.

The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels.

The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels.

The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels.

The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations.

The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations.

The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations.

The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates.

The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates.

The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates.

The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells.

The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells.

The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells.