864 papers
This paper proposes an interpretable, multimodal gesture recognition framework that fuses inertial and capacitive sensor data via log-likelihood ratio to enable robust, real-time, hands-free teleoperation of drones and mobile robots, supported by a new dataset and demonstrating performance comparable to vision-based methods with significantly lower computational costs.
This paper proposes a reinforcement learning-based approach for coverage path planning on 3D surfaces using deformable objects, which leverages harmonic UV mapping and scaled grouped convolutions to process contact feedback in simulation and successfully demonstrates effective wiping tasks on a physical Kinova Gen3 manipulator.
This paper presents a data-driven framework that combines Koopman operator theory with the Safe Set Algorithm to enable real-time, whole-body safe control of nonlinear robotic systems by formulating tracking and safety constraints within a single quadratic program.
This paper proposes a Dual-Interaction-Aware Cooperative Control (DIACC) strategy based on Multi-Agent Reinforcement Learning, which integrates decentralized decision-making, centralized value estimation, and a novel reward design to effectively alleviate mixed traffic congestion by distinguishing between cooperative and observational vehicle interactions.
This paper introduces GAIDE, a neural informed sampler that leverages graph-based attention masking to encode spatial and embodiment structures, thereby significantly improving the efficiency and success rate of sampling-based motion planning for robotic manipulators compared to existing methods.
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.