864 papers
This paper presents an accurate dynamics model for the newly introduced Crazyflie Brushless quadcopter, validated through simulations and hardware experiments, and demonstrates its effectiveness in training reinforcement learning controllers for complex acrobatic maneuvers that successfully transfer from simulation to reality.
PROBE is a learning-free LiDAR place recognition descriptor that models BEV occupancy as Bernoulli random variables and analytically marginalizes over continuous translations via the polar Jacobian to achieve sensor-independent, rotation-robust 3D place recognition with state-of-the-art performance among handcrafted methods.
This paper proposes and validates a proprioceptive shape estimation method for large-scale tensegrity manipulators that utilizes inclination angles from IMUs and energy minimization to achieve high-accuracy reconstruction (2.1% error) under both static and disturbed conditions without relying on external sensors.
This paper introduces HarvestFlex, the first study demonstrating that vision-language-action policies can be successfully adapted to real-world greenhouse strawberry harvesting using a closed-loop system with three-view RGB sensing and minimal teleoperated data, achieving a 74.0% success rate without relying on depth sensors or explicit geometric calibration.
This paper introduces MagRobot, the first universal open-source simulation platform designed to overcome the cost and consistency challenges of experimental prototyping by enabling the efficient design, visualization, and benchmarking of magnetically navigated rigid and soft robots across diverse medical applications.
This paper introduces Moving Through Clutter (MTC), an open-source Virtual Reality framework that addresses the lack of data for scene-aware humanoid locomotion by procedurally generating diverse 3D cluttered environments, capturing whole-body human motion, and providing a benchmarked dataset of 348 trajectories to advance robot navigation in complex, real-world settings.
This paper introduces TADPO, a novel reinforcement learning framework that extends Proximal Policy Optimization with off-policy teacher guidance and on-policy student exploration to enable zero-shot sim-to-real, high-speed autonomous driving on full-scale off-road vehicles navigating complex, unmapped terrain.
This paper identifies a "linguistic blindness" failure mode in Vision-Language-Action (VLA) models where they ignore contradictory instructions in favor of visual priors, and proposes IGAR, a train-free attention recalibration method that effectively restores language grounding and prevents erroneous actions without requiring model retraining.
The paper introduces Curious-VLA, a two-stage framework that overcomes the exploration limitations of standard driving VLA models by employing Feasible Trajectory Expansion during imitation learning and Adaptive Diversity-Aware Sampling with a Spanning Driving Reward during reinforcement learning, achieving state-of-the-art performance on the Navsim benchmark.
This paper introduces RODEO, a blockchain-based framework that integrates Decentralized Autonomous Organizations (DAOs) with service robots via a ROS-ETH bridge to enable auditable, transparent, and self-sustaining robotic operations through on-chain task verification and tokenized compensation.
This paper presents a novel pipeline that transforms archived omnidirectional RGB-LiDAR logs into robust 3D Gaussian Splatting initialization assets by addressing sensor distortion and data density challenges through ERP-to-cubemap conversion, color-stratified downsampling, and multi-modal registration, thereby enabling the creation of high-fidelity digital twins from standard, underutilized sensor data.
This paper introduces Uni-Walker, a lifelong embodied navigation framework that addresses catastrophic forgetting in large language model-based agents by decoupling navigation knowledge into shared and task-specific components using DE-LoRA, knowledge inheritance, and expert subspace orthogonality to enable continuous adaptation across diverse scenes and instruction styles.
This paper proposes a method to fine-tune compact, open-source vision-language models (500M–4B parameters) to generate executable behavior trees for robotic task planning by constructing a novel dataset from existing robotic episodes, achieving an 87% success rate in household tasks that rivals state-of-the-art closed-source models while using significantly fewer computational resources.
The paper proposes "Sticky-Glance," an object-centric gaze grounding framework that achieves robust intent recognition for human-robot collaboration using minimal gaze samples by stabilizing attention through a sticky-glance algorithm, thereby significantly improving selection accuracy, tracking rates, and overall task efficiency compared to existing baselines.
This paper introduces a structured Robotics Physical Safety Framework that bridges classical risk engineering with modern machine learning by utilizing explicit asset declaration, vulnerability enumeration, and hazard-driven synthetic data generation to train models on formalized safety envelopes.
This paper proposes a Dual-Agent Multiple-Model Reinforcement Learning (DAMMRL) framework for a shared-control 6-DoF rehabilitation robot that utilizes an event-triggered strategy to decouple human and robot tasks, dynamically optimizing co-adaptation by allowing the human to select speed-accuracy trade-offs while the robot adjusts its motion steps to suppress oscillations and improve task success rates.
DreamToNav is a novel autonomous robot framework that leverages generative video models to translate natural language prompts into executable motion plans, enabling robots to "dream" and physically execute complex navigation tasks with high accuracy across different locomotion platforms.
KISS-IMU is a novel self-supervised inertial odometry framework that eliminates the need for ground truth data by leveraging LiDAR-based ICP registration and pose graph optimization as supervisory signals, while employing motion-balanced training and uncertainty-aware inference to ensure robustness across diverse robotic platforms and environments.
VG3S is a novel framework that enhances 3D semantic occupancy prediction for autonomous driving by integrating strong geometric priors from frozen Vision Foundation Models into Gaussian splatting via a hierarchical feature adapter, achieving significant performance gains on the nuScenes benchmark.
This paper proposes a few-shot real-to-sim framework that calibrates analytical simulators with minimal real-world data and employs a differentiable mesh-based graph neural network to achieve high-fidelity rigid-contact modeling, thereby enhancing both simulation accuracy and the efficiency of gradient-based robotic policy learning.