864 papers
This paper introduces FEP-Nav, a biologically-inspired framework that enables robust real-time visual navigation by minimizing Variational Free Energy through a dual-mechanism architecture of top-down decoding and adaptive normalization, allowing autonomous agents to maintain performance under noisy and shifting sensory conditions without gradient-based updates.
This paper introduces the Multi-feature Implicit Model (MIMO), a novel object representation that leverages implicit neural fields to encode spatial features for robust shape reconstruction and relation modeling, enabling a framework that effectively learns task-oriented grasping and rearrangement from human demonstrations in both simulation and real-world scenarios.
This paper proposes a risk-aware autonomous driving framework that extends linear temporal logic specifications to incorporate event timing and severity, formulating a linear programming problem to synthesize policies that balance collision risks and traffic rule violations, as validated in the Carla simulator.
The GenTact Toolbox is a computational pipeline that enables the procedural generation and multi-material 3D printing of context-driven, whole-body artificial skins tailored to specific robot shapes and operational tasks, moving beyond traditional one-size-fits-all designs.
This paper proposes a contact-condition guided diffusion model that generates high-fidelity, detail-rich vision-based tactile images from RGB object images and contact force data, significantly outperforming traditional physics-based simulation methods in accuracy and texture reconstruction across diverse sensor setups.
This paper introduces a 3D dynamics-aware manipulation framework that enhances policy performance by integrating 3D world modeling through self-supervised depth and flow prediction tasks, thereby providing policies with crucial 3D foresight for robust depth-wise manipulation without compromising inference speed.
This paper introduces the PeRoI dataset, which captures diverse pedestrian reactions to robots in various contexts, and proposes the NeuRoSFM model to leverage this data for improved prediction of pedestrian-robot interactions in socially aware navigation.
This paper proposes LMPOcc, a plug-and-play framework that leverages a lightweight fusion module to integrate global occupancy priors into local 3D semantic prediction while simultaneously updating global maps via multi-vehicle crowdsourcing, thereby achieving state-of-the-art performance and enabling scalable, open-vocabulary 3D scene understanding.
This paper proposes a novel, hierarchical, and risk-aware reward function for reinforcement learning in autonomous driving that integrates normalized objectives and an extended Responsibility-Sensitive Safety model, resulting in a 21% reduction in collision rates while maintaining high route progress in unsignalized intersection scenarios.
This paper proposes a novel boundary-guided trajectory prediction framework that leverages HD map constraints and kinematic acceleration profiles to generate physically feasible, on-road, and robust autonomous driving predictions, significantly reducing off-road errors and improving generalization compared to existing baselines.
This paper proposes an automatic curriculum learning framework that employs a "teacher" to dynamically generate driving scenarios with adaptive complexity based on an agent's current capabilities, thereby overcoming the inefficiencies of fixed scenarios and domain randomization to achieve faster convergence and superior generalization in end-to-end autonomous driving reinforcement learning.
This paper introduces C*, a novel real-time coverage path planning algorithm for unknown environments that utilizes a Rapidly Covering Graph to ensure complete, near-optimal coverage with minimal computational complexity, significantly outperforming existing methods in efficiency and adaptability for both single and multi-robot systems.
The paper introduces RoboPARA, a novel LLM-driven framework that utilizes dependency graph generation and re-traversal to optimize dual-arm task parallelism, accompanied by the new X-DAPT dataset for evaluation, demonstrating superior efficiency and reliability in complex multitasking scenarios compared to existing methods.
This paper introduces Presymplectification Networks (PSNs), a novel framework that restores non-degenerate symplectic geometry for constrained and dissipative mechanical systems by learning a symplectification lift via Dirac structures, thereby enabling accurate, structure-preserving long-term prediction of complex multibody dynamics like those of the ANYmal quadruped robot.
This paper presents an imitation learning framework that enables legged robots to learn steerable, stylistically consistent locomotion behaviors and transitions directly from unlabeled real-world motion data by combining kinodynamic retargeting, automatic behavioral mode discovery, and reinforcement learning.
This paper presents a sensorless 4-channel bilateral teleoperation framework that enables stable, high-speed force feedback control on low-cost manipulators through disturbance-observer-based estimation and simplified tuning, ultimately demonstrating that such force-enhanced data significantly improves imitation learning performance.
This paper presents RVSG, a Vision Language Model-based testing framework developed with PAL Robotics that automatically generates diverse, requirement-violating human interaction scenarios in simulation to safely and effectively evaluate the robustness of industrial Autonomous Mobile Robots against unpredictable behaviors.
The paper introduces LHM-Humanoid, a unified learning framework and benchmark that employs reinforcement learning and policy distillation to enable humanoid agents to perform robust, long-horizon loco-manipulation tasks across diverse, cluttered environments without relying on pre-trained skill libraries or environment resets.
This paper presents a novel hybrid framework that combines offline-trained neural networks with online model predictive control to enable agile, accurate, and robust quadrotor gate traversal, achieving high peak accelerations and rapid recovery from severe disturbances through analytically derived policy gradients.
This paper presents a reinforcement learning-based quadrotor navigation method that utilizes privileged time-of-arrival maps and a yaw alignment loss to successfully navigate around large obstacles in cluttered environments, achieving an 86% success rate in simulation and demonstrating collision-free flight in real-world outdoor conditions.