864 papers
This paper presents a vision-based framework for legged robots that enables robust decision-driven semantic exploration by integrating confidence-calibrated perception, controlled-growth topological memory, and utility-driven subgoal selection to overcome the limitations of conventional geometry-centric navigation in open-world environments.
This paper introduces GPHDM, a novel framework that extends Gaussian Process Dynamical Models to hyperbolic manifolds to generate physically consistent, human-like robot motions by preserving the hierarchical taxonomy and temporal dynamics of movement.
This paper introduces ROSflight 2.0, a modular, open-source ROS 2-based autopilot ecosystem designed to lower barriers for UAV research and accelerate the transition from simulation to hardware, featuring a lean architecture that successfully controls multirotors at 400 Hz with all loops running on a companion computer.
ROSplane 2.0 is an open-source, ROS 2-based fixed-wing autopilot framework designed by researchers to accelerate UAV experimentation through a lean, modular architecture, enhanced control algorithms, and a streamlined aerodynamic modeling pipeline that simplifies the transition from simulation to real-world testing.
Phys2Real is a real-to-sim-to-real reinforcement learning framework that enhances sim-to-real transfer for precise robotic manipulation by fusing vision-language model-inferred physical parameter priors with online interactive adaptation through uncertainty-aware ensemble estimation.
This paper presents a sample-based hybrid mode control framework that formulates mode selection, switching timing, and duration as an integer optimization problem to achieve asymptotically optimal, reactive switching between non-differentiable and algorithmic control strategies for complex robotic tasks.
This paper introduces Consensus Complementarity Control Plus (C3+), an enhanced contact-implicit model predictive control algorithm that enables a complete robotic pipeline to robustly and efficiently push diverse single and multi-object configurations to target poses in real-time, achieving a 98% success rate on hardware.
This paper introduces AURASeg, an attention-guided segmentation framework featuring a Residual Boundary Refinement Module and an Attention Progressive Upsampling Decoder to enhance drivable-area boundary precision and multi-scale feature representation for onboard robot navigation, demonstrating superior performance on multiple datasets and successful deployment on a Jetson Nano.
This paper presents a real-time online framework that utilizes modified sliding-window Hankel Dynamic Mode Decomposition with singular-value hard thresholding and Cadzow projection to denoise partial measurements and construct predictive models for dynamic obstacle motion, enabling stable, variance-aware forecasting suitable for robotic motion planning.
This paper proposes and evaluates four augmented reality indicators designed to visualize a robot's field of view, finding that allocentric indicators placed in the task space and egocentric indicators modifying the robot's eyes effectively improve human accuracy in collaborative tasks while maintaining high confidence and low cognitive load.
ExpReS-VLA is a specialized Vision-Language-Action model that enables rapid, memory-efficient on-device adaptation to specific robotic tasks by combining compressed experience replay, retrieval-augmented generation, and a novel contrastive loss to prevent catastrophic forgetting while significantly improving performance on both spatial and long-horizon benchmarks.
The paper introduces CAVER, a novel robot equipped with a specialized audio-exciting end-effector, a combined audiovisual representation, and a curiosity-driven exploration algorithm that efficiently learns object correlations to improve material classification and audio-only imitation tasks.
This paper proposes a contact-safe reinforcement learning framework that combines Proximal Policy Optimization with movement primitives and an energy-aware Cartesian impedance controller to generate robust, safe, and energy-efficient task-space trajectories for complex contact-rich manipulation in 3D environments.
This paper introduces WNumMPC, a hierarchical multi-agent navigation framework that combines a reinforcement learning-based planner and a model-based controller to resolve symmetry-induced deadlocks in dense environments by leveraging topological winding numbers for robust, communication-free coordination.
Bi-AQUA is a novel bilateral control-based imitation learning framework for underwater robot arms that integrates transformer-based action chunking with explicit lighting modeling to achieve robust performance in challenging, variable illumination conditions.
EchoVLA is a memory-enhanced Vision-Language-Action model for mobile manipulation that synergizes scene and episodic declarative memories to improve navigation and task performance, validated by the new MoMani benchmark and demonstrating significant gains over existing baselines in both simulation and real-world settings.
This paper proposes a novel autonomous lane-changing planning framework that integrates dynamic risk fields with time-varying convex feasible spaces and a constrained iLQR solver to achieve safe, efficient, and comfortable trajectories that outperform traditional methods in complex traffic scenarios.
This paper introduces the concept of dependent reachable sets for two-agent pursuit scenarios, characterizing their geometric bounds and shape through theoretical analysis and simulations of the constant bearing pursuit strategy.
This paper introduces XR-DT, an Extended Reality-enhanced Digital Twin framework that integrates a novel Human-Aware Model Predictive Path Integral (HA-MPPI) controller with an attention-based trajectory prediction model to enable safe, efficient, and interpretable motion planning for mobile robots operating alongside humans.
This paper introduces Safe Model Predictive Diffusion (Safe MPD), a training-free planning framework that integrates a safety shield directly into the diffusion denoising process to generate kinodynamically feasible and safe trajectories in real-time, outperforming existing methods in success rate and safety without requiring post-processing corrections.