681 papers
The paper proposes RoTri-Diff, a diffusion-based imitation learning framework that explicitly models the spatial triadic relationship between two robot arms and an object to generate stable, coordinated bimanual manipulation trajectories, outperforming state-of-the-art baselines in both simulation and real-world tasks.
This paper proposes a fully onboard, vision-guided optimal control framework that enables agile drone racing through arbitrary gate poses by integrating a novel neural signed distance field (Gate-SDF) with a Model Predictive Path Integral (MPPI) controller to achieve robust, reference-free navigation without relying on explicit pose estimation or precomputed trajectories.
This paper proposes a kinematics-aware latent world model that integrates vehicle kinematic information and geometry-aware supervision into the Recurrent State-Space Model (RSSM) to enhance spatial representation and long-horizon imagination fidelity, thereby achieving more data-efficient and stable autonomous driving policy learning compared to existing baselines.
This paper presents a soft-rigid hybrid gripper that utilizes inflatable silicone pockets to actively modulate surface friction via internal air pressure, enabling the secure grasping of diverse objects—from heavy and slippery to fragile items—without relying on excessive normal force.
Faster-HEAL is a lightweight, privacy-preserving collaborative perception framework that addresses the challenges of heterogeneous autonomous vehicles by using low-rank visual prompt fine-tuning and pyramid fusion to align diverse features into a unified space, achieving superior detection performance with significantly reduced computational overhead compared to state-of-the-art methods.
This paper proposes a lightweight, digital-twin-based framework for edge-assisted vehicle tracking and collision prediction that achieves approximately 88% accuracy by relying on object detection and K-D tree-indexed path maps instead of computationally intensive trajectory prediction models.
The paper introduces DistGP, a distributed sparse Gaussian process model that enables multi-robot collaborative mapping through local computation and Gaussian belief propagation, achieving performance comparable to centralized training while outperforming distributed neural network optimizers in accuracy, robustness, and continual learning capabilities.
This review paper proposes a constraint-coupled perspective on underwater embodied intelligence, arguing that reliable autonomy requires integrating sensing, communication, and resource limitations into planning and control to address the interdependent challenges of real ocean environments.
This paper proposes a perceptive mixed-integer model predictive control framework that enables humanoid robots to dynamically plan foot placement and step timing on disconnected, obstacle-filled terrain by fusing depth data into convex foothold regions and enforcing safety via DCM capturability bounds.
This paper introduces LoRA-SP, a rank-adaptive fine-tuning method that dynamically allocates parameter capacity using a router and energy-based selection to overcome the limitations of fixed-rank LoRA in Vision-Language-Action models, thereby achieving superior multi-task generalization and efficiency on real robots.
This study investigates the self-righting capabilities of elongated, multi-legged robots by comparing biological centipede models with varying leg lengths, ultimately establishing morphology-strategy coupling principles and identifying critical limb-length thresholds that guide the design of robust, bio-inspired robots for complex terrains.
This paper proposes a novel proprioception method for micro-scale cable-driven continuum robots that integrates proximal cable tension and six-axis force/torque sensing with biomechanically inspired nonlinear modeling to enable accurate three-dimensional contact force perception and shape estimation, thereby overcoming limitations in miniaturization and sensor integration for safer clinical applications.
LITHE is a lightweight software architecture that enables the safe, hot-swapping of real-time C++ robotic control laws by a high-level Python "Brain" on commodity hardware, effectively bridging the gap between best-effort AI and deterministic real-time control to allow for continuous, on-the-fly evolution of embodied intelligence.
The paper introduces SLNet, a super-lightweight 3D point cloud recognition network utilizing Nonparametric Adaptive Point Embedding (NAPE) and Geometric Modulation Units (GMU) to achieve state-of-the-art accuracy on benchmarks like ModelNet40 and ScanObjectNN with significantly fewer parameters and computational costs compared to existing models.
This paper proposes GSAT, a self-supervised framework that leverages anomaly detection within a positive hypersphere in latent space to reliably estimate traversability for diverse terrains without requiring explicit negative supervision or additional prototypes.
The paper introduces HSC-VLA, a hierarchical framework that enhances robust bimanual manipulation in dense clutter by decoupling high-level semantic reasoning from low-level execution through explicit scene-clearing, achieving significantly higher success rates than monolithic baselines in complex long-horizon tasks.
This paper proposes an innovative inverse-dynamics observer that integrates a linear single-track vehicle model with a distributed tire representation described by hyperbolic partial differential equations to accurately estimate sideslip angles and tire forces using only yaw rate and lateral acceleration measurements, even under noise and model uncertainties.
InterReal is a unified physics-based imitation learning framework that enables humanoid robots to robustly learn and execute complex human-object interaction skills in real-world settings through a novel motion data augmentation scheme and an automatic reward learner.
The paper presents ICLR, a novel framework that enhances in-context imitation learning for robots by augmenting demonstration prompts with structured visual reasoning traces and jointly training a unified autoregressive transformer to predict both future trajectories and actions, thereby improving success rates and generalization in complex manipulation tasks.
The paper proposes ACCURATE, a robust 3D reconstruction framework that combines image segmentation with geometry-constrained topology traversal and dynamic programming to achieve high-accuracy reconstruction of arbitrary-shaped, deformable continuum bodies like guidewires and catheters under biplanar X-ray imaging.