ChainQueen: Real-time differentiable simulator for soft robotics with high precision.

Yuanming Hu, Jiancheng Liu, Andrew Spielberg et al.

Proposes a lightweight snake gate detection algorithm combined with improved pose estimation and predictive control, enabling autonomous drone racing at 1.5m/s with minimal error.

S. Li, M. M. O. I. Ozo, C. De Wagter et al.

Baidu Apollo EM motion planner combines multi-lane path and speed optimization using DP and spline QP, ensuring safety and comfort.

Haoyang Fan, Fan Zhu, Changchun Liu et al.

Proposed TOG-Net, a framework for task-oriented grasping via simulated self-supervision; achieved 71.1% sweeping and 80.0% hammering success rates.

Kuan Fang, Yuke Zhu, Animesh Garg et al.

Multi-modal deep learning model achieves 85% accuracy in pedestrian intent prediction, enhancing autonomous driving safety.

Amir Rasouli, John K. Tsotsos

Jacquard dataset uses simulated environments to generate large-scale grasp locations, enhancing robotic grasp detection performance.

Amaury Depierre, Emmanuel Dellandréa, Liming Chen

Proposes a sparse 3D topological graph for MAV planning, using GPU-accelerated GVD extraction and skeletonization to enable fast, robust pathfinding in noisy environments.

Helen Oleynikova, Zachary Taylor, Roland Siegwart et al.

Proposes asymmetric Actor-Critic leveraging full states during training and visual inputs at inference, achieving robust sim-to-real transfer without real data.

Lerrel Pinto, Marcin Andrychowicz, Peter Welinder et al.

Combining domain randomization with generative autoregressive models, achieving over 90% success on unseen objects in robotic grasping.

Joshua Tobin, Lukas Biewald, Rocky Duan et al.

Proposes an incremental deep Boltzmann machine for adaptive hierarchical scene context modeling, dynamically adding layers based on confidence measures.

Fethiye Irmak Doğan, Hande Çelikkanat, Sinan Kalkan

Social Attention learns nonlocal pedestrian importance, reducing mean ADE to 0.30 m and FDE to 2.59 m on ETH/UCY.

Anirudh Vemula, Katharina Muelling, Jean Oh

Proposes conditional imitation learning with high-level commands, enabling end-to-end autonomous driving with improved controllability and robustness.

Felipe Codevilla, Matthias Müller, Antonio López et al.

Ergodic exploration (EEDI) optimizes trajectories to match expected information density, outperforming traditional info-max controls in nonlinear systems.

Lauren M. Miller, Yonatan Silverman, Malcolm A. MacIver et al.

Proposes a grasp pose detection method in point clouds, achieving a 93% success rate.

Andreas ten Pas, Marcus Gualtieri, Kate Saenko et al.

Deep RL-based socially aware motion planning enables autonomous robots to navigate safely and naturally among pedestrians, respecting social norms like passing on the right.

Yu Fan Chen, Michael Everett, Miao Liu et al.

Gaussian Process-based multiresolution mapping combined with informative path planning reduces agricultural monitoring error by 45%.

Marija Popovic, Teresa Vidal-Calleja, Gregory Hitz et al.

Proposes distributed pose graph optimization (DGS, SOR, JOR) with object models, reducing communication by over 90%.

Siddharth Choudhary, Luca Carlone, Carlos Nieto et al.

Voxblox system incrementally converts TSDF to ESDF for real-time UAV path planning, achieving 20x faster updates with <0.1m error.

Helen Oleynikova, Zachary Taylor, Marius Fehr et al.

Uses 3D CNN trained on 440,000+ models for fast shape completion, boosting robotic grasping success to 93.33%.

Jacob Varley, Chad DeChant, Adam Richardson et al.

Proposes an autonomous robot system using RGBD-based implicit mapping and active learning for dense clutter sorting, achieving over 85% success and 80% classification accuracy.

Janne V. Kujala, Tuomas J. Lukka, Harri Holopainen