689 papers
This paper introduces Inverse Resistive Force Theory (I-RFT), a physics-informed machine learning framework that enables robots to accurately estimate granular terrain properties from proprioceptive contact forces under arbitrary gait trajectories, thereby facilitating data-efficient environmental characterization and adaptive locomotion strategies.
This paper demonstrates that a single, subject- and posture-agnostic time-varying cost function, efficiently estimated via the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, can accurately predict human reaching movements by revealing a unified optimality principle dominated by joint-acceleration regulation.
This paper introduces MWM, a mobile world model that enhances action-conditioned rollout consistency and inference efficiency for image-goal navigation through a novel two-stage training framework featuring Action-Conditioned Consistency post-training and Inference-Consistent State Distillation.
The paper introduces STEP, a preference-conditioned reinforcement learning framework that optimizes robotic 3D bin packing by explicitly balancing spatial efficiency against operational time, achieving a 44% reduction in execution time without compromising packing density.
This paper proposes a dual-mode human-robot joint planning system that combines an LLM-assisted active elicitation mechanism with real-time intent inference to effectively mitigate task-relevant knowledge gaps and latent human intent, significantly reducing interaction costs and execution time in open-world environments.
This paper introduces MINT, a novel framework that enhances human-AI drone collaboration by using large language models to actively elicit minimal, targeted information from operators to resolve environmental uncertainties, thereby significantly improving task success rates while reducing the need for frequent human intervention.
This paper presents a unified control framework for aerial manipulators that integrates a physics-based blade-element model with a learning-based residual force estimator and online rotor-speed adaptation to achieve robust trajectory tracking and wall-contact operations in complex wind and near-wall environments.
This paper bridges the gap between dynamic programming-based planning and reinforcement learning by developing a derandomized RL variant, mathematically analyzing the conditions under which their differing formulations (such as cost minimization versus reward maximization and goal termination versus infinite-horizon discounting) are equivalent, and advocating for the optimization of true cost over arbitrary parameters.
This paper presents an end-to-end, viewpoint-agnostic grasping pipeline for mobile legged manipulators that leverages vision-language models and partial observation compensation to achieve robust, language-guided object selection and safe execution in cluttered environments, outperforming view-dependent baselines with a 90% success rate.
This paper proposes a task-aware observation interface that canonicalizes raw RGB inputs into unified semantic-geometric representations using segmentation and depth injection, thereby significantly enhancing the robustness of visuomotor policies to out-of-distribution appearance shifts without requiring policy retraining.
This paper introduces a method using transfer entropy to identify influential robot actions during human-robot conversations, demonstrating its effectiveness in analyzing nonlinear interactions to improve robotic system design and adaptability.
RoboRouter is a training-free framework that enhances robotic manipulation performance by intelligently routing diverse, off-the-shelf policies to the most suitable one for each task based on semantic representations and historical execution data, achieving significant success rate improvements in both simulation and real-world settings without requiring additional model training.
NaviDriveVLM proposes a decoupled framework that separates high-level reasoning and motion planning using a large-scale Navigator and a lightweight Driver, achieving superior end-to-end performance on the nuScenes benchmark while reducing training costs and enhancing interpretability.
DyQ-VLA is a dynamic quantization framework for Embodied Vision-Language-Action models that leverages real-time kinematic proxies to adaptively switch and allocate bit-widths, significantly reducing memory footprint and improving inference speed while maintaining near-original performance.
This paper presents a vision-only autonomous bronchoscopy framework utilizing hierarchical long-short agents and a world-model critic to achieve accurate, sensor-free intraoperative navigation in preclinical models, demonstrating performance comparable to expert human operators.
This paper presents a robust framework for safe omnidirectional humanoid stair locomotion that combines a single-stage training strategy with dense unsafe stepping penalties and a refined sparse LiDAR elevation mapping system to achieve high success rates in both simulation and real-world deployments.
This paper proposes a trajectory-driven global optimization framework, inspired by CMA-ES, that enables unified, high-fidelity structural-hydrodynamic modeling of underwater underactuated and soft robotic systems by simultaneously identifying coupled internal and external parameters, achieving accurate real-to-sim consistency across diverse mechanisms without manual retuning.
The paper introduces RAPID, a novel Edge-Cloud Collaborative inference framework designed to optimize the deployment of Vision Language Action models by addressing visual noise interference and step-wise task redundancy, thereby achieving up to a 1.73x speedup with minimal overhead.
VORL-EXPLORE is a hybrid learning and planning framework for multi-robot exploration in dynamic environments that couples task allocation with motion execution via a shared navigability fidelity signal, enabling adaptive arbitration between global and reactive policies to prevent bottlenecks and ensure robust, collision-free coverage.
TeamHOI is a decentralized framework that leverages a Transformer-based policy and a masked Adversarial Motion Prior strategy to enable a single unified policy to control scalable, physically realistic cooperative human-object interactions among any number of humanoid agents.