864 papers
This paper introduces Act-Observe-Rewrite (AOR), a framework enabling multimodal language models to iteratively improve robot manipulation policies by synthesizing and rewriting executable Python controller code based on visual feedback and failure analysis, achieving high success rates across tasks without demonstrations, reward engineering, or gradient updates.
This paper presents a fully autonomous navigation stack for a quadruped robot running on low-power edge hardware without GPUs or network connectivity, which successfully navigated complex underground mine environments with a 100% success rate across 20 trials using a combination of LiDAR-inertial odometry, map-based localization, and classical planning algorithms.
PTLD introduces a sim-to-real framework for dexterous manipulation that distills privileged real-world tactile data into a robust state estimator, enabling proprioceptive policies trained in simulation to significantly improve performance on complex in-hand tasks without requiring realistic tactile simulation.
The paper proposes Many-RRT*, a robust sampling-based motion planner that simultaneously grows trees from multiple inverse kinematics solutions to overcome the challenges of non-invertible forward kinematics in high-degree-of-freedom serial manipulators, thereby achieving significantly higher success rates and lower trajectory costs compared to traditional RRT methods.
The paper introduces MEMO, a neuro-symbolic framework that aggregates and synthesizes multi-user natural language corrections and task successes into a retrieval-augmented skillbook, enabling robots to dynamically generate generalized skills and improve performance on novel manipulation tasks.
This paper presents a distributed and decentralized vision-based state estimation framework using a Distributed Decentralized Extended Information Filter (DDEIF) to enable robust, GPS-denied cooperative slung load transportation by multiple UAVs, demonstrating its effectiveness and resilience to sensor and communication losses through Gazebo simulations.
This paper introduces a novel framework that combines Distributional Reinforcement Learning with Imitation Learning to train mobile manipulators to execute risk-aware, reactive whole-body motions in dynamic, unmapped environments by leveraging egocentric depth observations and runtime-adjustable risk sensitivity.
The paper presents ELLIPSE, a robust waypoint prediction method for mobile robots that combines multivariate deep evidential regression with lightweight domain augmentation and post-hoc recalibration to effectively mitigate overconfidence under distribution shifts and improve uncertainty reliability in safety-critical open-world environments.
This paper introduces a risk-aware formalism for evaluating system trajectories under uncertainty by explicitly modeling system-environment interactions and hierarchical objectives, thereby establishing a consistent preference ordering and enhancing explainability in autonomous driving scenarios.
This paper introduces RoboMME, a large-scale standardized benchmark and a suite of memory-augmented VLA models designed to systematically evaluate and advance robotic generalist policies in long-horizon, history-dependent manipulation tasks.
This paper presents a real-time control and software architecture that enables a commercial Flyability Elios 3 quadrotor to perform fully autonomous, contact-based ultrasound non-destructive testing in unstructured industrial environments using exclusively onboard sensing.
This paper introduces GIANT, a novel multi-robot trajectory planning framework that combines global path integration with attentive graph neural networks to achieve robust, high-success collision avoidance in complex, dynamic environments.
This paper presents a human-in-the-loop workflow leveraging Large Language Models to automate the generation of nanobind wrappers for the large-scale C++ robotics library OMPL, demonstrating that this approach significantly reduces manual effort while maintaining runtime performance comparable to legacy solutions.
This paper proposes an autonomous valet parking pipeline that improves task completion, prediction accuracy, and social acceptance by explicitly predicting other agents' parking intentions from their motion history, demonstrating that this approach outperforms methods relying on short-term motion prediction or implicit intention modeling.
The paper introduces LEGS-POMDP, a modular framework that integrates language, gesture, and visual observations within a Partially Observable Markov Decision Process to effectively handle uncertainty in object identity and location for robust open-world robot search.
This paper presents GenTact-Prox, a modular, fully 3D-printed artificial skin that integrates tactile and proximity sensing to enable whole-body contact anticipation, and introduces a data-driven framework for mapping the perisensory space to facilitate online object-aware operation on robots.
This paper proposes an NSGA-III-based optimization framework that generates Pareto optimal gaits for legged modular robots with detachable joints, successfully balancing the minimization of joint torque to prevent mechanical failure with the maintenance of locomotion speed and stability across diverse terrains.
This paper presents and validates a self-aligning tool changer for modular robots that utilizes passive geometric features to robustly compensate for positioning errors, enabling reliable autonomous module exchange without complex force sensing.
This study demonstrates that adaptive policy switching for two-wheeled differential robots traversing diverse lunar lava tube terrains can be effectively achieved by classifying terrain types with over 98% accuracy using short-term pitch data and Gaussian mixture models.
This paper proposes a decentralized exploration framework for large robot swarms that combines a self-organizing algorithm for dynamic team formation with a novel large language model-based strategy for autonomous target selection, validated through extensive simulations.