Dynamic Obstacle Avoidance

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Dynamic obstacle avoidance studies how robots and autonomous vehicles choose feasible paths and actions when people, vehicles, or other obstacles move. Methods range from space-time search and model-predictive control to velocity-obstacle methods and control barrier functions that constrain reactive controllers. Learning-based approaches use reinforcement or imitation learning, motion prediction, and learned risk or occupancy representations, often combined with classical planners and safety filters. A central challenge is balancing safety and efficient progress under uncertain, delayed perception and predictions; reliable real-world transfer and evaluation in dense, interactive settings remain open concerns.

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119 papers22 papers in the last four weeks, up 47% on the four weeks before. 0.1% of all new papers.

Learning Visuomotor Quadrotor Flight Through Dynamic Narrow Gaps via Differentiable Simulation
Oct 27, 2026Riccardo Bianco, Yunfan Ren, Jiaxu Xing +1Quadrotor Control Visuomotor Policy Learning
Robotics and Perception Group, University of Zurich
Continuous-Time Safe Planning and Control for Agile Quadrupeds
Oct 26, 2026Gage MacLin, Mason Peterson, Camilla Tabasso +4Safe Motion Planning Quadruped Robot Navigation
Amazon Fulfillment Technologies & Robotics, Westborough MA, USA. · University of Iowa, Iowa City, IA 52240. · Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
SCET-CBF: Speed-Calibrated Soft Guidance for One-Step Dynamic Navigation
Oct 24, 2026Xiaoyu Han, Dongkun HanDynamic Obstacle Avoidance Collision Avoidance
University College London · The Chinese University of Hong Kong
KinoUAV: A Benchmark for Safe and Stable UAV Vision-Language Navigation with Kinodynamic Modeling
Oct 22, 2026Chenyao Yang, Peijin Wang, Yingchao Feng +1Safe Robot Navigation Aerial VLN
the Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China · the University of Chinese Academy of Sciences, Beijing 100190, China · the School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China +1
Distance Field Driven Reinforcement Learning for Reactive and Robust Collision Avoidance
Oct 21, 2026Taiga Ikeshima, Sheila Sutjipto, Lan Wu +5Robot Collision Avoidance RL for Robotics
Tokyo University of Science, Japan. · the University of Technology Sydney, Australia. · Universidad Carlos III de Madrid, Spain. +1
SWAP: Test-Time Steerable Humanoid Whole-Body Collision Avoidance
Oct 19, 2026Zeji Yi, Guanqi He, Xiaofeng Guo +5Robot Collision Avoidance Humanoid Robot Control
Carnegie Mellon University · Amazon FAR
TransNav: Transformer-Based End-to-End Navigation Method for Static and Dynamic Quadrotor Obstacle Avoidance
Oct 16, 2026Jianghan Zhu, Zhuo Li, Yanjun Zhang +1Robot Navigation Aerial Robotics
The authors are with the State Key Laboratory of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
E2DRL: End-to-End Differentiable Reinforcement Learning for 3D Obstacle Avoidance in Quadruped Robots
Oct 14, 2026Cheng Fan, Bingyang Zhou, Gang Yang +7Reinforcement Learning Robot Collision Avoidance
Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
LiDAR-based Humanoid Navigation in Dynamic, Non-Convex Environments via Spatio-Temporal Safe Corridors
Oct 12, 2026Shubham S. Kumbhar, Panagiotis ArtemiadisRobot Navigation Safe Motion Planning
the Mechanical Engineering Department, at the University of Delaware, Newark, DE 19716, USA
Learning Reachability-Based Safety Representations for Real-Time Robot Navigation in Unknown and Dynamic Environments
Oct 12, 2026Bojan DerajićSafe Robot Navigation Dynamic Obstacle Avoidance
Faculty IV – Electrical Engineering and Computer Science of Technische Universität Berlin
Imitation Learning of Avoidance Modes for Efficient Multimodal Model Predictive Control
Oct 12, 2026Changyu Lee, Byung-Kwan Lee, Kanghoon YoonModel Predictive Control Dynamic Obstacle Avoidance
Kongju National University · KAIST
From Field to Planning: Learning Temporal Polar Gradient Fields for Vision-based AAV Trajectory Planning in Dynamic Environments
Oct 10, 2026Haowen Gao, Zhan Chen, Yinuo Xu +3UAV Navigation Motion Planning
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China · College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
TAME-Nav: Temporal Asymmetric Mixture-of-Experts Distillation for Agile and Versatile Robot Navigation
Oct 9, 2026Lingjie Zhang, Jun Xue, Shanze Wang +4Robot Navigation Robot Policy Learning
Shanghai Jiao Tong University, Shanghai, China. · Eastern Institute of Technology, Ningbo, China. · Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. +1
Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods
Oct 8, 2026Eran Iceland, Alexander Tuisov, Oren Gal +2Motion Planning Dynamic Obstacle Avoidance
School of Engineering and Computer Science, The Hebrew University of Jerusalem, Jerusalem, Israel · Faculty of Data and Decision Science, Technion Israeli Institute of Technology, Haifa, Israel · Hatter Department of Marine Technologies, University of Haifa, Haifa, Israel +1
MeshSIPP: Efficient Lattice Planning in Dynamic Environment
Oct 7, 2026Marat Agranovskiy, Konstantin YakovlevSafe Interval Path Planning Motion Planning
Adaptive Risk-Certified Event-Triggered Replanning for Dynamic Navigation
Oct 7, 2026Richie R. Suganda, Bin HuRisk-Aware Path Planning Safe Motion Planning
Department of Electrical Engineering and Computer University of Houston Houston, TX 77004, USA · Department of Engineering Technology University of Houston Houston, TX 77004, USA
SC3BF: Shifted Collision Cone Control Barrier Function for Dynamic Obstacle Avoidance
Oct 6, 2026Amin Kashiri, Yasin YazıcıoğluControl Barrier Functions Robot Safety
Department of Electrical and Computer Engineering at Northeastern University, Boston, MA · Departments of Mechanical and Industrial Engineering and Electrical and Computer Engineering at Northeastern University, Boston, MA
Expressiveness, Equivalence, and Uncertainty in Velocity Obstacles and Closest Point of Approach Metrics
Oct 5, 2026Elizabeth Dietrich, Liam M. Imagawa, Hanna Krasowski +3Collision Avoidance Uncertainty Quantification
University of California, Berkeley, USA · Norwegian University of Science and Technology, Trondheim, Norway
RiskFly: Frustum-Aligned Spatio-Temporal Risk Fields for One-Stage Agile Flight in Dynamic Clutter
Oct 3, 2026Luxia Ai, Haopeng Chen, Yuchao Mei +2UAV Navigation Safe Motion Planning
State Key Laboratory of Multispectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Closed-Loop Refinement and Execution for Learned Driving Planners
Oct 1, 2026Huaijin Hu, Shanting Wang, Zhongyu Mo +1Model Predictive Control Safe Motion Planning
Systems Engineering Program, Cornell University, Ithaca, NY 14853 USA
Multi-Link Safety Filtering for VLA Policies Around Moving Hazards
Sep 30, 2026Yatharth Agarwal, Vijay RaghunathanRobot Safety Safety Filtering
School of Electrical and Computer Engineering Purdue University, West Lafayette, IN, USA
DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
Sep 30, 2026Sanghyuk Park, Kwanwoo Lee, Taekyung Kim +2RL for Robotics Control Barrier Functions
Department of Intelligence and Information, Seoul National University, Republic of Korea · Department of Robotics, University of Michigan, Ann Arbor, MI, USA · Department of Mechanical Engineering, Yonsei University, Republic of Korea +1
Adversarially Robust Geometric Safety Certificates for Nonholonomic Robots Against Maneuvering Obstacles
Sep 29, 2026Chandan Kumar Sah, Bazeela Banday, Jishnu KeshavanControl Barrier Functions Safe Robot Navigation
Department of Mechanical Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India
Distilling Privileged Control Barrier Functions into RGB-Only Safety Filters for Dynamic Visual Navigation
Sep 29, 2026Seungyeon Yoo, Gawon Lee, Seungwoo Jung +2Control Barrier Functions Robotics
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea
FutureRay: Control-Aligned Future Range for Agile Quadruped Navigation
Sep 26, 2026Tianhao Zang, Shanze Wang, Ziqian Wang +4Robot Navigation Safe Robot Navigation
College of Information Science and Technology, Eastern Institute of Technology, Ningbo, P. R. China. · University of Maryland, USA. · The Hong Kong Polytechnic University, Hong Kong. +2
Path Planning with Motion Primitives in Dynamic Environments: SIPP on Lattices
Sep 25, 2026Marat AgranovskiySafe Interval Path Planning Motion Planning
Markov Lab, Saint-Petersburg University.
ST-pRRTC: Parallel Space-Time RRT-C with Adaptive Goal-Time Forests
Sep 24, 2026Duo Zhang, Jintong Li, Junshan Huang +1Motion Planning Sampling-Based Motion Planning
Department of Computer Science, Rutgers, the State University of New Jersey, Piscataway, NJ, USA
Memory-Aware Multi-Sensor Perception for Efficient and Safe Navigation in Dynamic Environments
Sep 24, 2026Jingshuo Li, Yifan Xue, Yifei Li +2Robot Navigation Safe Robot Navigation
School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, USA
UCON: Uncertainty-aware Navigation with Historical Re-association in Dynamic Environments
Sep 24, 2026Bing Sun, Yue Lin, Yongsheng Yuan +3Trajectory Optimization Robot Navigation
All authors are with Dalian University of Technology, Dalian 116024.China.
OA-MPPI: Occlusion-Aware Model Predictive Path Integral Control for UAV Flight
Sep 23, 2026Vittorio Palladino, Teaya Yang, Ruiqi Zhang +1UAV Navigation Quadrotor Control
High Performance Robotics Laboratory, Department of Mechanical Engineering, University of California, Berkeley, CA 94720, United States

Oct 27, 2026J/K move · Enter open · S save

Learning Visuomotor Quadrotor Flight Through Dynamic Narrow Gaps via Differentiable Simulation

Riccardo Bianco, Yunfan Ren, Jiaxu Xing, Davide Scaramuzza

Robotics and Perception Group, University of Zurich

Quadrotor Control Visuomotor Policy Learning Dynamic Obstacle Avoidance

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Threading a quadrotor through a moving narrow gap is a demanding test of agile flight, requiring centimeter-level spatial alignment and precise temporal execution. We address this challenge using a control policy that operates directly on onboard visual and inertial measurements to output collective thrust and body-rate commands. The task is difficult because success lies on a thin feasible manifold: small errors in timing, position, attitude, or visual observability cause collision. Prior planning and learning methods rely on privileged state, teachers, planners, or distillation; learning dynamic narrow-gap traversal directly, from scratch, remains open. We introduce a differentiable moving-gap formulation that gates centering, alignment, and smooth collision terms by signed distance to a time-varying gap plane, turning a sparse traversal event into a dense pathwise objective. We integrate this formulation into a visual-feature-to-command policy trained in one stage from random initialization, without a teacher, planner, or distillation. We validate our approach in both synthetic and real-world experiments. In controlled benchmarks, we demonstrate that pathwise gradients significantly outperform score-function gradients for precision-critical tasks, yielding a 99.3% success rate in simulation after only a few minutes of training on a single GPU. Furthermore, the policy transfers directly to a real quadrotor, successfully crossing a dynamic narrow gap from varied initial conditions using onboard sensing and inertial measurements, achieving an 80% success rate over 10 physical trials.