659 papers
This paper introduces a large-scale framework for Vision-and-Language Navigation that leverages web-based room tour videos and implicit geometry representations to overcome simulator limitations, enabling robust zero-shot navigation agents with state-of-the-art performance across multiple benchmarks.
The paper proposes RAE-NWM, a navigation world model that operates in a dense DINOv2 feature space using a Conditional Diffusion Transformer with a decoupled head and time-driven gating to achieve superior structural stability and action accuracy compared to traditional latent-space approaches.
This paper introduces MORLAX, a GPU-native multi-objective reinforcement learning algorithm, and MO-Playground, a suite of GPU-accelerated environments, which together enable massively parallelized training that achieves 25–270x speedups and superior Pareto fronts for complex robotics tasks compared to legacy CPU-based approaches.
The paper introduces TRIP-Bag, a portable, suitcase-contained teleoperation system that enables rapid, high-fidelity data collection for robot learning by eliminating the embodiment gap through direct joint-to-joint control in diverse, non-laboratory environments.
WESPR is a lightweight, real-time framework that integrates geometric perception and local weather data to predict wind fields generated by environmental obstacles, enabling quadrotors to proactively plan safe, energy-efficient paths and adapt control strategies for robust flight in turbulent conditions.
This paper presents a robust spatiotemporal motion planning framework for multi-agent autonomous racing that combines topological gap identification via stochastic Gaussian processes with a PTC-accelerated Linear Time-Varying MPC to achieve high-speed overtaking with strict kinematic feasibility and significantly reduced computational latency.
This paper introduces STONE, a large-scale, multi-modal off-road dataset featuring synchronized LiDAR, camera, and radar data with automated, annotation-free 3D traversability labels, alongside a new benchmark for voxel-level traversability prediction.
SPAN-Nav is an end-to-end foundation model that achieves state-of-the-art, robust generalization in versatile vision-language navigation by leveraging a massive 4.2-million-annotation dataset to learn universal 3D spatial priors, which are efficiently encoded into a single token to guide action reasoning across diverse indoor and outdoor environments.
This paper presents a computationally lightweight, segment-based control architecture for multi-legged robots with six or more legs that effectively navigates rough terrain by bridging the gap between event-driven and CPG-based controllers through tightly coupled ground contact and fictive locomotion.
This paper presents a provably safe motion planning framework for robot manipulators in uncertain, non-convex environments by integrating a deep stochastic Koopman operator for state prediction with a hierarchical sum-of-squares verification filter within a Model Predictive Path Integral controller to generate certified, collision-free trajectories.
This paper proposes a high-slip-ratio control framework for automated vehicles that actively excites peak tire-road friction during empty-haul operations to enable accurate, safe, and scalable estimation of the tire-road friction coefficient through constrained optimal control and robust statistical projection.
This paper presents a novel, annotation-free framework that leverages language models and vision-language reasoning to autonomously extract 3D UAV trajectories and classifications from Internet-scale videos, demonstrating that zero-shot transfer performance on anti-UAV tasks improves consistently with increased data volume without requiring target-domain training.
This paper presents a low-cost, untethered bimanual mobile manipulator built on the open-source XLeRobot platform with integrated NVIDIA Jetson Orin compute, featuring an optimized mechanical design and a specialized power topology to enable autonomous navigation and vision-based manipulation for under $1300.
The paper presents ImpedanceDiffusion, a hierarchical framework that combines image-conditioned diffusion models for global path planning with reactive APF tracking and VLM-enhanced variable impedance control to enable safe, high-speed, and adaptive UAV swarm navigation in cluttered indoor environments without explicit map construction.
This paper transforms the commercially available Inspire RH56DFX hand into a reliable research tool by introducing hardware characterization, a sim2real validated MuJoCo model for grasp planning, and a hybrid closed-loop speed-force controller, achieving significant performance improvements in peg-in-hole insertion and diverse object grasping tasks.
This paper presents SurgCalib, a markerless, Gaussian Splatting-based framework that achieves accurate hand-eye calibration for the da Vinci surgical robot by refining kinematic estimates through a differentiable rendering pipeline, thereby overcoming cable-driven inaccuracies and avoiding the sterility issues associated with traditional fiducial markers.
This paper presents a hybrid learning and model-based approach that integrates multimodal tactile sensing with internal-force optimization to achieve reactive slip stabilization in multifingered grasps with a sub-50 ms closed-loop latency.
This paper presents the design and experimental validation of a robust relative localization system for the SnailBot modular robot, which fuses ArUco marker recognition, optical flow, and IMU data to enable accurate real-time positioning for collaborative tasks.
The paper introduces FAME, a force-adaptive reinforcement learning framework that enables a full-scale humanoid to robustly maintain balance during bimanual manipulation by conditioning its policy on a learned latent context of joint configurations and estimated interaction forces, thereby significantly expanding its manipulation envelope without requiring wrist force sensors.
This paper introduces Path-Consistent Safety Filtering (PACS), a novel approach that ensures formal safety guarantees for diffusion policies in dynamic environments while preserving task success rates by applying set-based reachability analysis to brake trajectories in a manner consistent with the policy's training distribution.