Robotics & Embodied AI

Transition realization is a game-changer for World Action Models, with LEON boosting performance and robustness beyond traditional methods.

PDPO revolutionizes robot crowd navigation by generating action chunks that enhance safety and efficiency in dense human environments.

Achieving 15m tracking accuracy for 38mg receivers with less than 180uW power consumption could revolutionize landscape-scale movement studies.

Achieving a 98% success rate in CAV joining maneuvers reveals the critical balance between safety and efficiency in mixed traffic environments.

CLAP achieves zero-shot deployment of physical simulators across diverse robot embodiments, outperforming traditional models in complex environments.

Interaction response can be harnessed to accurately calibrate material properties of 3D Gaussian representations, outperforming traditional methods by a substantial margin.

Automated fluorescence measurements reveal plant stress with unprecedented precision, transforming how we assess agricultural health.

Explicitly incorporating state awareness into task planning with MM-LLMs leads to a 32.8% increase in action executability, revolutionizing human-robot collaboration.

Tensegrity robots can morph into task-specific configurations, revolutionizing cooperative robotic behaviors in dynamic environments.

Contact-induced constraints can dramatically enhance localization accuracy in underwater environments plagued by visual ambiguity and drift.

KDG-SemNOMA transforms 6G robotic vehicle communications by significantly enhancing visual perception while minimizing bandwidth and energy usage.

Vision-language models struggle significantly with interactive navigation tasks in procedurally generated environments, revealing critical gaps in current AI capabilities.

MILO redefines 3D human-object interaction reconstruction by leveraging Large Reconstruction Models, achieving unprecedented accuracy from just a single image.

Integrating depth information with visual data leads to a dramatic boost in 3D awareness, outperforming traditional methods on key benchmarks.

SpatialCrafter achieves unprecedented 3D consistency in image-to-scene generation, effectively eliminating long-term drift and enhancing detail fidelity.

Aligning object poses with their geometric axes can dramatically enhance estimation accuracy while simplifying model architecture requirements.

Achieving camera calibration accuracy that meets the Cramer-Rao Lower Bound using flawed and asynchronous GPS data could revolutionize drone-based imaging systems.

Real-time control of pneumatic soft actuators can achieve precision within 1.5-2.3 mm, even in complex tasks like drawing digits and tracking motion.

Multi-agent SLAM can now achieve high-quality 3D reconstruction using only RGB and inertial data, making it accessible for consumer-grade devices.

Attackers can now induce specific failure behaviors in VLA models with unprecedented precision, revealing a new dimension of vulnerability in AI systems.

sLoTh enables continual learning in sparse event-based transformers with less than 1% parameter updates, achieving competitive performance while slashing energy consumption by 6.5x.

Real-world testing reveals that Assisted Lane Change systems may permit dangerous maneuvers that violate safety distance regulations, challenging current approval processes.

MAV scheduling can be automated to maximize data collection efficiency while minimizing energy use, revolutionizing marine research operations.

SHARP guarantees 100% task completion in dense warehouse layouts where other methods fail, revealing the critical role of fixed Safe Havens in multi-agent coordination.

A sub-million-parameter robot manipulation policy outperforms larger models by leveraging predictive coding for real-time state correction.

FLARE transforms VLAs from brittle performers into resilient agents capable of autonomously recovering from common execution failures in robotic manipulation.

DPA-I2P achieves a remarkable 45% reduction in pose estimation errors, setting a new standard for image-to-point cloud registration in autonomous driving.

Real-time object navigation can see up to an 11% boost in success rates by addressing inference latency and asynchronous stepping in model design.

A modular fixturing system can maintain stability across robotic disassembly stages, achieving impressive stability margins of over 80% for complex products.

A-sharp reduces delivery times in multi-agent warehouse scenarios by dynamically optimizing retreat targets, outperforming its predecessor in the majority of configurations tested.

FlashVLA achieves over 30 Hz control frequency with smooth asynchronous execution, revolutionizing real-time robotic manipulation.

Current methods falter in efficiently rearranging scenes with occlusions, exposing a critical gap in embodied agent capabilities.

Localized load balancing with minimal communication enables heterogeneous robot teams to achieve robust coordination and rapid convergence in chaotic environments.

GRAFT boosts robot manipulation success rates by 25 percentage points while slashing the computational costs of online learning.

Achieving over 97% success in long-horizon robot manipulation, TemporalFlow-VLA reveals the critical role of execution history in action prediction.

MeshPriorDiT achieves a groundbreaking 75% reduction in prediction error for cloth dynamics by integrating local and global modeling strategies.

A novel soft gripper design maintains stable grasping forces over time, achieving unprecedented accuracy by compensating for stress relaxation in real-time.

Riemann-1.0 transforms embodied intelligence by unifying robot policy execution and world simulation, achieving unprecedented success rates in real-world manipulation tasks.

A novel gripper design enables reliable grasping and manipulation of thin objects, achieving high success rates without complex control adjustments.

Steering miscalibration can lead to dangerous path tracking errors, but an EKF-based online calibration method significantly reduces these errors in real-world mobile robots.

Achieving holonomic interpolation for rigid-motion jets could revolutionize how we model complex motion in robotics and computer graphics.

Realism in deep-sea robotics simulations just got a major upgrade, bridging the gap between visual fidelity and physical accuracy.

Residual learning can enhance the robustness of rehabilitation robots without sacrificing the interpretability of traditional control methods.

Excluding just 10% of critical Memory Anchors can lead to a staggering 4.5x increase in catastrophic forgetting in robots.

Trajectory selection in autonomous driving can now achieve over 87% safety clearance certification, dramatically improving reliability in real-world conditions.

Learning a cost function for LQR control transforms the Poppy Humanoid into a reliable bipedal walker, achieving significant performance gains.

A bimanual robot can learn complex juggling patterns in under five minutes by leveraging its existing knowledge, despite significant discrepancies between simulation and reality.

SOLO achieves a remarkable 97.5% mean traversal success on complex terrains, showcasing a leap in humanoid locomotion capabilities.

A policy trained on one aircraft can successfully adapt to multiple others, achieving impressive performance without any retraining.

CUBIST achieves state-of-the-art results in action quality assessment by leveraging multimodal data for precise error attribution and feedback generation.