666 papers
This paper introduces DeReCo, a novel multi-agent reinforcement learning framework that decouples representation and coordination learning through a three-stage training strategy to overcome bidirectional interference, thereby enabling sample-efficient and robust decentralized cooperative transport across objects with diverse shapes and physical properties.
This paper proposes a novel adaptive vision-based control scheme with null-space interaction for redundant robots that ensures stable, safe, and effective human-robot collaboration in unknown environments by decoupling primary task execution from interactive adjustments, as validated through augmented reality experiments and Lyapunov stability analysis.
This paper introduces MRDrive, an open-source mixed reality driving simulator that bridges the gap between ecological validity and experimental control by allowing users to interact with a real vehicle cabin while immersed in a virtual driving environment to support automotive user research.
This paper introduces "See & Switch," a vision-based interactive framework for Programming by Demonstration that utilizes eye-in-hand images to enable reliable online conditional branching and anomaly detection in dexterous robot tasks, achieving high accuracy across diverse conditions and novice users.
This paper proposes a systematic framework using Robust Positive Invariant sets to compute formally guaranteed trajectory tracking error bounds for autonomous helicopters, comparing three controller architectures to balance tracking performance with the conservatism of the derived error bounds.
AffordGrasp is a diffusion-based framework that generates physically stable and semantically accurate human grasps by integrating affordance-aware latent representations with a dual-conditioning process to bridge the modality gap between 3D object geometry and textual interaction instructions.
This paper introduces DiffRacing, a novel framework that enhances differentiable policy learning for vision-based drone racing by integrating vector fields to provide stable gradient signals for balancing high-speed gate traversal with obstacle avoidance, while employing a differentiable Delta Action Model to enable robust sim-to-real transfer without explicit system identification.
This paper introduces a Dual-Horizon Hybrid Internal Model that enables stable, continuous quadrupedal jumping under lunar gravity using only proprioceptive sensing, validated through the MATRIX hardware-in-the-loop testbed which emulates reduced gravity and lunar terrain in real time.
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.
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.
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.
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 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.
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.
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.
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 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 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 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 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.