685 papers
ReconDrive is a fast, feed-forward framework that adapts the VGGT foundation model with hybrid prediction heads and static-dynamic composition to achieve high-fidelity, scalable 4D Gaussian Splatting for autonomous driving scenes, outperforming existing feed-forward methods while matching the quality of slower optimization-based approaches.
This paper proposes an Approximate Imitation Learning framework that enables a quadrotor to fly at high speeds through cluttered environments using only a single event camera by training an end-to-end neural network with a large offline dataset and lightweight state simulations, thereby avoiding the computational cost of rendering synthetic event data while achieving robust real-world performance.
FeasibleCap is a gripper-in-hand data collection system that provides real-time, hardware-free executability feedback through visual and haptic cues, enabling demonstrators to capture valid robot trajectories without headsets or learned dynamics models while improving replay success and preserving cross-embodiment transfer.
This paper introduces three neural-aided and model-based algorithms for dog dead reckoning (DDR) that utilize only inertial sensors to achieve accurate positioning with less than 10% absolute distance error for both biological and robotic dogs, validated through the newly created DogMotion dataset and a robotic legged dog dataset.
The paper proposes Staged Multi-Agent Training (SMAT), a four-stage curriculum that progressively trains a human-exoskeleton system to achieve stable co-adaptation, resulting in a control policy that significantly reduces hip muscle activation and delivers consistent, positive mechanical power across diverse users without requiring subject-specific retraining.
GeoLoco is a robust, RGB-only humanoid locomotion framework that leverages geometric priors from a frozen Visual Foundation Model and a specialized cross-attention mechanism to achieve zero-shot sim-to-real transfer on the Unitree G1 without relying on active depth sensors.
This paper presents a reinforcement learning framework for training exoskeleton controllers to reduce biological joint moments and establishes a quantitative validation pipeline that demonstrates strong simulation-to-data consistency in torque predictions, particularly at the hip, while identifying specific challenges in timing and power injection for sim-to-real transfer.
PanoDP is a communication-free learning framework that integrates panoramic depth perception with differentiable physics-based training signals to enable robust, collision-free autonomous navigation in complex, partially observable environments with both static and dynamic obstacles.
TempoFit is a training-free, plug-and-play method that enhances frozen Vision-Language-Action policies for long-horizon manipulation by retrieving and injecting layer-wise temporal key-value memory from previous timesteps, thereby improving success rates in non-Markovian environments without increasing inference latency or requiring model retraining.
The paper proposes AtomicVLA, a unified planning-and-execution framework that utilizes a Skill-Guided Mixture-of-Experts architecture to dynamically compose atomic skill abstractions, thereby significantly improving scalability and performance in long-horizon robotic manipulation and continual learning tasks compared to existing monolithic VLA models.
This paper proposes a multi-agent informative path planning framework for off-world exploration that utilizes Gaussian belief mapping and dual-domain coverage to effectively discover sparse, visually ambiguous evidence while balancing information gain with operational safety in hazardous, communication-constrained environments.
This paper presents DAISS, a dual-arm robotic system that utilizes a phase-aware imitation learning policy trained on high-fidelity teleoperated demonstrations to automate the complex, asymmetric bimanual coordination required for ultrasound-guided needle interventions.
This paper presents an open-source, low-cost teleoperation framework for mobile bimanual manipulators that utilizes commodity hardware like smartphones and foot pedals to achieve intuitive whole-body control, demonstrating improved task performance and reduced cognitive load compared to traditional keyboard-based methods.
UniUncer is a lightweight, unified framework for end-to-end autonomous driving that jointly estimates and leverages uncertainty for both static map elements and dynamic agents through probabilistic regression, uncertainty-aware query fusion, and adaptive gating, thereby significantly improving trajectory accuracy and planning robustness with minimal computational overhead.
This paper introduces RoboPCA, a pose-centered affordance learning framework that jointly predicts task-appropriate contact regions and poses from human demonstrations via the Human2Afford data curation pipeline, enabling robots to effectively manipulate objects with improved consistency and generalization across tasks and categories.
C-Explorer is a decentralized framework for multi-UAV exploration that addresses communication limitations and inefficient traversal by utilizing connectivity-aware task representation and a contiguity-driven allocation strategy, achieving significant reductions in exploration time and path length compared to state-of-the-art methods.
This paper introduces AeroPlace-Flow, a training-free framework that enables aerial manipulators to perform precise, language-grounded object placement by synthesizing goal images, grounding them into 3D space, and generating collision-aware object flows without requiring predefined poses or task-specific training.
This paper proposes reframing human-AI-robot interaction as "scaffolding" rather than autonomy, positioning humans as creative directors who define intent and steer revisions while AI mediates robotic execution to support sustained human agency and creativity in open-ended environments.
This paper proposes a stability-aligned residual control architecture that enables robotic systems to rapidly recover from mid-episode dynamics shifts by keeping the nominal policy frozen while using a bounded, gated additive residual channel to adaptively compensate for unobserved disturbances, achieving up to an 87% reduction in recovery time across various robotic platforms.
This paper presents a multi-legged robot equipped with biomimetic, gradient-compliant tactile antennae that enable robust navigation and recovery in confined, obstacle-rich environments by mapping antenna deformation to collision states for real-time steering without relying on global environmental information or vision.