All papers

The paper introduces test-time command optimization to reduce trajectory errors in humanoid behavior foundation models via closed-loop simulation search.

The paper develops funnel motion planning and control for robotic manipulators using neural signed configuration distance functions for collision avoidance.

The paper proposes an edge-native dual-expert model using actuator-load and joint-angle feedback for multi-finger grasp classification and stop prediction.

The paper presents FoundationVIO, integrating inertial sensing with feed-forward geometric foundation models to recover metric scale in visual odometry.

The paper introduces EnactOpt, an optimization method that evolves execution harnesses using diagnostic feedback to improve embodied agent performance.

The paper introduces FreeBar, a training-free barrier-constrained inference plugin for real-time asynchronous execution of vision-language-action models.

The paper evaluates a shared-visualization teleoperation interface to improve operator coordination and situational awareness in multi-robot systems.

The paper presents a training-free graph optimization framework that coordinates multi-agent embodied search using uncertain vision-language model priors.

The paper proposes inverse kinematics and compliance optimization control for a rigid-soft hybrid-link humanoid robot modeled using piece-wise constant strain.

The paper uses mutation testing to evaluate whether fuzzy logic-based risk estimation improves situation coverage for autonomous mine-exploring drones.

The paper presents nanoflann, a C++ KD-tree library supporting exact nearest-neighbor search on user-defined product manifolds at compile time.

The paper introduces a decentralized reinforcement learning policy for heterogeneous maritime multi-robot task allocation under spatio-temporal environmental uncertainties.

The paper proposes an inference-time search method to select embodiment-feasible humanoid reactions from human-to-humanoid retargeted motion models.

The paper presents ProWorld, a multi-view world model with proprioceptive feedback that tracks robot policy success rates in closed-loop evaluation.

The paper presents a memory-augmented world action model combining visual state and latent event memory for long-horizon robotic manipulation.

The paper introduces an open-source MuJoCo simulation environment for aerial robots equipped with tendon-driven soft continuum arms.

The paper reviews how robotic physical models can help investigate whole-body mechanical interactions and sensing across complex physical terrains.

The paper proposes a hierarchical navigation framework that coordinates exploration, error recovery, and revisiting past locations for robotic object search.

The paper introduces an uncertainty-guided spatial mapping framework to improve target search efficiency in zero-shot object navigation.

The paper investigates multi-agent reinforcement learning algorithms to balance human guidance and geometric safety in robotic surgical shared control.

The paper evaluates combinatorial skill generalization in embodied manipulation and provides a modular diagnostic benchmark for chained robot tasks.

The paper proposes a constrained reinforcement learning framework for whole-body loco-manipulation by quadruped robots operating in simulated lunar gravity conditions.

The paper introduces a constrained reinforcement learning method for end-to-end approach, capture, and detumbling of non-cooperative orbital targets by a spacecraft.

The paper presents a reinforcement learning approach for quadruped locomotion directly from raw lidar point clouds without building elevation maps.

The paper introduces a visual self-modeling method that learns explicit screw-axis robot kinematics directly from multi-view images and joint readings.

The paper develops an end-to-end vision model that directly generates navigation trajectories for quadrotors from stereo camera images without explicit depth mapping.

The paper presents a textile sensor combining muscle vibrations and strain measurements to trigger assistance in soft knee exosuits.

The paper presents a data collection framework that aligns teacher demonstrations with robot interface actions for open-world mobile manipulation.

The paper proposes a multi-agent reinforcement learning approach using graph attention to coordinate automated vehicles at unsignalized intersections under imperfect communications.

The paper presents a streaming world action modeling framework that improves temporal continuity and execution smoothness in robot policy rollouts.

The paper introduces a supervised learning method to compose movement primitives into complex robotic manipulation tasks using unsegmented trajectories.

The paper develops a predictive control framework enabling bipedal robots to cross obstacles smoothly across both walking and running speeds.

The paper presents an algorithm for distributed multi-robot area coverage path planning that explicitly incorporates headland paths.

The paper describes a multi-sensor system using lidar, RTK positioning, and thrust feedback for mid-air drone launch and recovery.

The paper develops a linear dynamic model and control method to prevent trailer sway and jackknifing in autonomous passenger vehicles.

The paper introduces a reinforcement learning framework that optimizes the physical arm morphology of a planar manipulator to reduce real-world motor effort.

The paper presents a competitive task allocation framework using temporal vision windows and artificial potential fields for dual-arm industrial manipulation.

The paper proposes a flow-matching method initialized from recent history to speed up training and inference in robot foundation models.

The paper introduces a framework for robotic manipulation that enables self-improvement by attributing downstream failures to earlier actions without demonstrations or fine-tuning.

The paper presents a framework that factorizes humanoid control into terrain-invariant intent and perceptive terrain-conditioned realization using reinforcement learning.

This paper presents a learned onboard proprioceptive reflex policy enabling quadrotors to recover stability autonomously after high-speed collisions.

This paper proposes a signal temporal logic model predictive control method combining hard safety constraints with soft interaction quality objectives.

This paper proposes a Jacobian-augmented kinematic safety filter enforcing joint limits and task-space viability through nested quadratic programs.

This paper presents an open hardware-partitioned miniature vehicle separating onboard safety control from off-board neural model inference.

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.