864 papers
This paper introduces PhysiFlow, a physics-aware, multi-brain Vision-Language-Action framework that leverages latent flow matching and robust tracking to enable efficient, stable, and semantically guided whole-body control for humanoid robots.
The paper introduces CompACT, a compact discrete tokenizer that compresses observations into just 8 tokens to enable computationally efficient, real-time decision planning within world models while maintaining competitive performance.
This paper proposes a hybrid Residual RL-MPC controller that augments a nominal Model Predictive Control with a Soft Actor-Critic learned residual policy to achieve robust, contact-gated microrobotic cell pushing under time-varying flow, demonstrating superior tracking accuracy and generalization compared to pure MPC and PID approaches.
This paper bridges the gap in mechanistic interpretability for Vision-Language-Action Models (VLAs) by introducing feature-observability and feature-controllability concepts, demonstrating that lightweight linear interventions can effectively steer robot behavior in real-time across different architectures without requiring fine-tuning.
The paper introduces cuRoboV2, a unified, GPU-native framework that integrates B-spline trajectory optimization, dense depth-fused distance fields, and scalable whole-body dynamics to enable safe, high-fidelity motion generation for high-DoF robots, achieving significant performance gains in success rates, collision avoidance, and computational efficiency over existing state-of-the-art methods.
Safe-SAGE is a unified framework that bridges high-level semantic understanding with low-level safety-critical control by employing a Laplace-modulated Poisson safety function within a multi-layer filter, enabling legged robots to navigate dynamic, semantically rich environments with context-dependent safety margins while maintaining rigorous guarantees.
RoboPocket is a smartphone-based system that enhances robot imitation learning by using AR visual foresight to guide targeted data collection and asynchronous online finetuning, thereby doubling data efficiency and enabling instant policy iteration without requiring physical robot execution.
This paper proposes a cooperative navigation framework that fuses LiDAR-based relative localization from a primary UAV with Visual-Inertial Odometry from a secondary micro-scale UAV to achieve accurate, drift-free guidance and enable large-area exploration with high precision.
This paper reviews and categorizes existing reward functions for reinforcement learning in autonomous driving into safety, comfort, progress, and traffic rule compliance, while highlighting their current limitations in standardization and context-awareness to propose future research directions for more robust and conflict-resolving reward designs.
This paper proposes a unified Bayesian framework that integrates a customized particle filter and Gaussian process implicit surfaces to enable robots to actively recognize object classes and poses, estimate shapes, and transfer knowledge from known objects to learn novel ones through guided tactile exploration.
This paper introduces MIKASA, a comprehensive benchmark suite featuring a classification framework and two distinct datasets (MIKASA-Base and MIKASA-Robo) to systematically evaluate and advance memory-enhanced reinforcement learning agents across diverse scenarios, with a specific focus on tabletop robotic manipulation.
This paper introduces Evolution 6.0, an autonomous robotic system powered by generative AI and multimodal models that enables robots to independently design and fabricate necessary tools while learning to use them to execute complex human-requested tasks.
This paper introduces RoboGuard, a two-stage guardrail architecture that combines a secure root-of-trust LLM for generating context-aware safety specifications with temporal logic control synthesis to effectively mitigate safety risks and prevent harmful behaviors in LLM-enabled robots without compromising performance.
This paper proposes a self-supervised UAV trajectory planning framework that integrates learning-based depth perception with differentiable optimization and neural time allocation to achieve robust, label-free navigation in 3D environments, significantly outperforming state-of-the-art methods in tracking accuracy and control efficiency.
This paper proposes TPK, a trustworthy trajectory prediction framework that integrates class-specific interaction and kinematic priors to ensure physically feasible and interpretable predictions for mixed traffic agents, demonstrating improved reliability over state-of-the-art baselines on the Argoverse 2 dataset despite a minor trade-off in raw accuracy.
This paper proposes "Feature Mixing," an extremely simple and fast modality-agnostic method for multimodal outlier synthesis that achieves state-of-the-art performance in out-of-distribution detection and segmentation while offering significant speedups, alongside the introduction of a new multimodal dataset called CARLA-OOD.
This paper introduces pyCub, an open-source, Python-based simulation framework for the iCub humanoid robot that eliminates the need for C++ and YARP middleware while providing scalable exercises to teach robotics fundamentals to students with varying programming skills.
This paper proposes Q-SVMPC, a novel framework that integrates Q-guided Stein variational inference with an RL-informed policy prior to enable diverse, robust, and sample-efficient trajectory optimization in Model Predictive Control, overcoming the mode collapse limitations of existing learning-based MPC methods.
VITA is a novel, noise-free, and conditioning-free flow matching framework that accelerates inference by directly mapping visual representations to structured latent actions via a jointly trained autoencoder and flow latent decoding, achieving state-of-the-art performance on diverse robotic tasks.
Point2Act is an efficient framework that leverages Multimodal Large Language Models to distill 2D semantic understanding into precise 3D action points, enabling zero-shot, context-aware robotic grasping in unseen environments within under 20 seconds.