Swooper: Learning High-Speed Aerial Grasping With a Simple Gripper

• URL: http://arxiv.org/abs/2603.05935v1
• Date: Fri, 06 Mar 2026 06:07:40 GMT
• Title: Swooper: Learning High-Speed Aerial Grasping With a Simple Gripper
• Abstract summary: High-speed aerial grasping presents significant challenges due to the high demands on precise, responsive flight control and coordinated gripper manipulation.<n>We propose Swooper, a deep reinforcement learning (DRL) based approach that achieves both precise flight control and active gripper control.<n>In 25 real-world trials, our policy achieves an 84% grasp success rate and grasping speeds of up to 1.5 m/s without any fine-tuning.
• Score: 3.894377831948157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-speed aerial grasping presents significant challenges due to the high demands on precise, responsive flight control and coordinated gripper manipulation. In this work, we propose Swooper, a deep reinforcement learning (DRL) based approach that achieves both precise flight control and active gripper control using a single lightweight neural network policy. Training such a policy directly via DRL is nontrivial due to the complexity of coordinating flight and grasping. To address this, we adopt a two-stage learning strategy: we first pre-train a flight control policy, and then fine-tune it to acquire grasping skills. With the carefully designed reward functions and training framework, the entire training process completes in under 60 minutes on a standard desktop with an Nvidia RTX 3060 GPU. To validate the trained policy in the real world, we develop a lightweight quadrotor grasping platform equipped with a simple off-the-shelf gripper, and deploy the policy in a zero-shot manner on the onboard Raspberry Pi 4B computer, where each inference takes only about 1.0 ms. In 25 real-world trials, our policy achieves an 84% grasp success rate and grasping speeds of up to 1.5 m/s without any fine-tuning. This matches the robustness and agility of state-of-the-art classical systems with sophisticated grippers, highlighting the capability of DRL for learning a robust control policy that seamlessly integrates high-speed flight and grasping. The supplementary video is available for more results. Video: https://zikenhuang.github.io/Swooper/.

Related papers

• Simple but Stable, Fast and Safe: Achieve End-to-end Control by High-Fidelity Differentiable Simulation [14.322775509774246]
  Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications.<n>We propose a novel end-to-end policy that directly maps depth images to low-level bodyrate commands by reinforcement learning.<n>Results demonstrate that the proposed approach achieves the highest success rate and the lowest jerk among state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-04-12T09:38:00Z)
• Learning Sim-to-Real Humanoid Locomotion in 15 Minutes [51.500643119683225]
  We introduce a simple and practical recipe based on off-policy RL algorithms, i.e., FastSAC and FastTD3.<n>Our simple recipe stabilizes off-policy RL algorithms at massive scale with thousands of parallel environments.<n>We demonstrate rapid end-to-end learning of humanoid locomotion controllers on Unitree G1 and Booster T1 robots.
  arXiv  Detail & Related papers  (2025-12-01T18:55:17Z)
• LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigation [15.483749658372124]
  LEARN is a lightweight, two-stage safety-guided reinforcement learning framework for multi-UAV navigation.<n>Our system combines low-resolution Time-of-Flight (ToF) sensors and a simple motion planner with a compact, attention-based RL policy.<n>We demonstrate LEARN's viability on six Crazyflie quadrotors, achieving fully onboard flight in diverse indoor and outdoor environments.
  arXiv  Detail & Related papers  (2025-11-21T20:29:03Z)
• RAPTOR: A Foundation Policy for Quadrotor Control [7.1760769144571865]
  Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car.<n>Modern robotic control systems, like neural network policies trained using Reinforcement Learning, are highly specialized for single environments.<n>We present RAPTOR, a method for training a highly adaptive foundation policy for quadrotor control.
  arXiv  Detail & Related papers  (2025-09-15T00:05:40Z)
• A General Infrastructure and Workflow for Quadrotor Deep Reinforcement Learning and Reality Deployment [48.90852123901697]
  We propose a platform that enables seamless transfer of end-to-end deep reinforcement learning (DRL) policies to quadrotors.<n>Our platform provides rich types of environments including hovering, dynamic obstacle avoidance, trajectory tracking, balloon hitting, and planning in unknown environments.
  arXiv  Detail & Related papers  (2025-04-21T14:25:23Z)
• What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study [24.239835581921458]
  We investigate key factors for learning robust RL-based control policies capable of zero-shot deployment in real-world quadrotors.<n>We develop a PPO-based training framework named SimpleFlight, which integrates these five techniques.<n>We validate the efficacy of SimpleFlight on Crazyflie quadrotor, demonstrating that it achieves more than a 50% reduction in trajectory tracking error.
  arXiv  Detail & Related papers  (2024-12-16T13:31:26Z)
• DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning [61.10299147201369]
  This paper introduces a novel autonomous RL approach, called DigiRL, for training in-the-wild device control agents. We build a scalable and parallelizable Android learning environment equipped with a VLM-based evaluator. We demonstrate the effectiveness of DigiRL using the Android-in-the-Wild dataset, where our 1.3B VLM trained with RL achieves a 49.5% absolute improvement.
  arXiv  Detail & Related papers  (2024-06-14T17:49:55Z)
• Learning to Fly in Seconds [7.259696592534715]
  We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times. Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct control after only 18 seconds of training on a consumer-grade laptop.
  arXiv  Detail & Related papers  (2023-11-22T01:06:45Z)
• FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing [71.76084256567599]
  We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL) Our system, FastRLAP (faster lap), trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. The resulting policies exhibit emergent aggressive driving skills, such as timing braking and acceleration around turns and avoiding areas which impede the robot's motion, approaching the performance of a human driver using a similar first-person interface over the course of training.
  arXiv  Detail & Related papers  (2023-04-19T17:33:47Z)
• Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator [56.11574814802912]
  State-of-the art robotic applications utilize series elastic actuators (SEAs) with closed-loop force control to achieve complex tasks such as walking, lifting, and manipulation. Model-free PID control methods are more prone to instability due to nonlinearities in the SEA. Deep reinforcement learning has proved to be an effective model-free method for continuous control tasks.
  arXiv  Detail & Related papers  (2023-04-11T00:51:47Z)
• Policy Search for Model Predictive Control with Application to Agile Drone Flight [56.24908013905407]
  We propose a policy-search-for-model-predictive-control framework for MPC. Specifically, we formulate the MPC as a parameterized controller, where the hard-to-optimize decision variables are represented as high-level policies. Experiments show that our controller achieves robust and real-time control performance in both simulation and the real world.
  arXiv  Detail & Related papers  (2021-12-07T17:39:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.