864 papers
This paper introduces ProReFF, a feature field model that learns relative object co-occurrence distributions from unlabeled observations to guide robotic search agents, achieving 20% higher efficiency than strong baselines and up to 80% of human performance in the Matterport3D simulator.
This paper presents the Interactive World Simulator, a fast and physically consistent framework leveraging consistency models to generate high-fidelity long-horizon video predictions that enable scalable robot policy training and reliable real-world evaluation using solely simulated data.
This paper proposes the Olfactory-Auditory augmented Bug algorithm (OA-Bug) for swarm robots to effectively explore denied environments without GNSS or central processing, demonstrating through simulations and real-world experiments that it achieves significantly higher search coverage (96.93%) compared to existing methods like SGBA.
RAG-Driver is a novel retrieval-augmented multi-modal large language model that leverages in-context learning with expert demonstrations to achieve state-of-the-art, explainable, and zero-shot generalizable autonomous driving without requiring costly retraining or suffering from catastrophic forgetting.
FALCON is a unified self-supervised pretraining framework for UAV action recognition that overcomes spatial imbalance in aerial footage by combining object-aware masked autoencoding with object-centric dual-horizon future reconstruction, achieving superior accuracy and faster inference without requiring additional preprocessing at test time.
This paper introduces an efficient and accurate non-linear programming framework that integrates hierarchical decision-making with inverse kinematic planning and control by leveraging sparse structures and the -norm to solve complex problems like simultaneous end-effector and grasp selection without relying on heavy mixed-integer computations.
This paper introduces Generative Predictive Control, a supervised learning framework that leverages flow matching and sampling-based predictive control to enable high-frequency, dynamic robotic tasks by eliminating the need for difficult-to-obtain expert demonstrations.
This paper introduces Context-Aware Priority Sampling (CAPS), a novel imitation learning method that leverages VQ-VAEs to cluster and re-balance training data, thereby improving the generalization, driving score, and success rate of autonomous driving systems in CARLA simulations.
This paper demonstrates that a simple iterative LQR algorithm using MuJoCo dynamics and finite-difference derivatives can achieve effective, real-time whole-body model-predictive control for quadruped and humanoid robots in the real world with minimal sim-to-real tuning, thereby lowering the barrier for future research.
This paper presents a graph-based online Lidar odometry method that enhances trajectory estimation and map accuracy by registering scans against multiple overlapping submaps and performing retrospective refinement of their anchor points, achieving superior performance on automotive datasets while maintaining real-time operation.
FindAnything is an efficient, open-world mapping framework that integrates vision-language features into object-centric volumetric submaps to enable real-time, open-vocabulary semantic understanding of large-scale environments on resource-constrained robots.
This paper introduces a robustness-aware framework that jointly selects tools and plans contact-rich manipulation trajectories by leveraging an energy-based metric to optimize for disturbance resilience in robotic tool-use tasks.
This paper proposes a structured methodology that integrates the Robot Operating System (ROS) with Model-Based Systems Engineering (MBSE) through a specialized SysML metamodel called MeROS and an adapted V-model, aiming to enhance the semantic coherence, structural traceability, and reliable coordination of complex heterogeneous robotic systems.
This paper proposes a novel student-teacher framework for autonomous driving that utilizes a graph-based multi-agent RL teacher to automatically generate diverse, adaptive traffic curricula, enabling a student agent to achieve superior robustness and balanced driving performance compared to traditional rule-based approaches.
This paper introduces an interactive, no-code framework with a graphical interface and graph-based representation to democratize scenario generation for autonomous driving validation in CARLA, enabling non-technical users to efficiently create, manage, and execute diverse test scenarios without programming expertise.
VEGA is an electric vehicle navigation system that combines a physics-informed neural operator for real-time vehicle parameter estimation with a Proximal Policy Optimization agent for efficient, charge-aware route and charging stop planning, demonstrating superior inference speed and generalization across international road networks compared to traditional energy-aware baselines.
This paper presents a multimodal framework that integrates natural language understanding of pilot radio calls with trajectory data to significantly improve the accuracy of aircraft goal prediction in non-towered airspace compared to motion-only baselines.
The paper presents GLIDE, a cooperative search-and-rescue framework that pairs two specialized UAVs with a UGV to enable rapid, safe navigation in unknown environments through real-time victim detection, terrain scouting, and guided long-horizon planning.
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.