Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower
- URL: http://arxiv.org/abs/2204.02390v1
- Date: Tue, 5 Apr 2022 17:55:58 GMT
- Title: Learning Pneumatic Non-Prehensile Manipulation with a Mobile Blower
- Authors: Jimmy Wu, Xingyuan Sun, Andy Zeng, Shuran Song, Szymon Rusinkiewicz,
Thomas Funkhouser
- Abstract summary: blowing controller must continually adapt to unexpected changes from its actions.
We introduce a multi-frequency version of the spatial action maps framework.
This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control.
- Score: 30.032847855193864
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a
means of efficiently moving scattered objects into a target receptacle. Due to
the chaotic nature of aerodynamic forces, a blowing controller must (i)
continually adapt to unexpected changes from its actions, (ii) maintain
fine-grained control, since the slightest misstep can result in large
unintended consequences (e.g., scatter objects already in a pile), and (iii)
infer long-range plans (e.g., move the robot to strategic blowing locations).
We tackle these challenges in the context of deep reinforcement learning,
introducing a multi-frequency version of the spatial action maps framework.
This allows for efficient learning of vision-based policies that effectively
combine high-level planning and low-level closed-loop control for dynamic
mobile manipulation. Experiments show that our system learns efficient
behaviors for the task, demonstrating in particular that blowing achieves
better downstream performance than pushing, and that our policies improve
performance over baselines. Moreover, we show that our system naturally
encourages emergent specialization between the different subpolicies spanning
low-level fine-grained control and high-level planning. On a real mobile robot
equipped with a miniature air blower, we show that our simulation-trained
policies transfer well to a real environment and can generalize to novel
objects.
Related papers
- CAIMAN: Causal Action Influence Detection for Sample Efficient Loco-manipulation [17.94272840532448]
We present CAIMAN, a novel framework for learning loco-manipulation that relies solely on sparse task rewards.
We employ a hierarchical control strategy, combining a low-level locomotion policy with a high-level policy that prioritizes task-relevant velocity commands.
We demonstrate the framework's superior sample efficiency, adaptability to diverse environments, and successful transfer to hardware without fine-tuning.
arXiv Detail & Related papers (2025-02-02T16:16:53Z) - Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments [49.30744329170107]
We propose a novel approach for optimal online motion planning with minimal information about dynamic obstacles.
The proposed methodology combines Monte Carlo Tree Search (MCTS), for online optimal planning via model simulations, with Velocity Obstacles (VO), for obstacle avoidance.
We show the superiority of our methodology with respect to state-of-the-art planners, including Non-linear Model Predictive Control (NMPC), in terms of improved collision rate, computational and task performance.
arXiv Detail & Related papers (2025-01-16T16:45:08Z) - A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.
We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT.
We also propose a reinforcement learning-based drone tracking method called R-VAT.
arXiv Detail & Related papers (2024-12-01T09:37:46Z) - Modular Neural Network Policies for Learning In-Flight Object Catching
with a Robot Hand-Arm System [55.94648383147838]
We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects.
Our framework consists of five core modules: (i) an object state estimator that learns object trajectory prediction, (ii) a catching pose quality network that learns to score and rank object poses for catching, (iii) a reaching control policy trained to move the robot hand to pre-catch poses, and (iv) a grasping control policy trained to perform soft catching motions.
We conduct extensive evaluations of our framework in simulation for each module and the integrated system, to demonstrate high success rates of in-flight
arXiv Detail & Related papers (2023-12-21T16:20:12Z) - Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning
for Triggering and Control of Rotational Maneuvers [11.29285364660789]
Inverted landing in a rapid and robust manner is a challenging feat for aerial robots, especially while depending entirely on onboard sensing and computation.
Previous work has identified a direct causal connection between a series of onboard visual cues and kinematic actions that allow for reliable execution of this challenging aerobatic maneuver in small aerial robots.
In this work, we first utilized Deep Reinforcement Learning and a physics-based simulation to obtain a general, optimal control policy for robust inverted landing.
arXiv Detail & Related papers (2022-09-22T14:38:10Z) - Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate
Model Predictive Trajectory Tracking [76.27433308688592]
Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation.
We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience.
Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
arXiv Detail & Related papers (2022-06-07T13:51:35Z) - Optimizing Airborne Wind Energy with Reinforcement Learning [0.0]
Reinforcement Learning is a technique that learns to associate observations with profitable actions without requiring prior knowledge of the system.
We show that in a simulated environment Reinforcement Learning finds an efficient way to control a kite so that it can tow a vehicle for long distances.
arXiv Detail & Related papers (2022-03-27T10:28:16Z) - Distilling Motion Planner Augmented Policies into Visual Control
Policies for Robot Manipulation [26.47544415550067]
We propose to distill a state-based motion planner augmented policy to a visual control policy.
We evaluate our method on three manipulation tasks in obstructed environments.
Our framework is highly sample-efficient and outperforms the state-of-the-art algorithms.
arXiv Detail & Related papers (2021-11-11T18:52:00Z) - OSCAR: Data-Driven Operational Space Control for Adaptive and Robust
Robot Manipulation [50.59541802645156]
Operational Space Control (OSC) has been used as an effective task-space controller for manipulation.
We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors.
We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
arXiv Detail & Related papers (2021-10-02T01:21:38Z) - ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for
Mobile Manipulation [99.2543521972137]
ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals.
Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments.
ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
arXiv Detail & Related papers (2020-08-18T08:05:15Z) - Multi-Task Reinforcement Learning based Mobile Manipulation Control for
Dynamic Object Tracking and Grasping [17.2022039806473]
A multi-task reinforcement learning-based mobile manipulation control framework is proposed to achieve general dynamic object tracking and grasping.
Experiments show that our policy trained can adapt to unseen random dynamic trajectories with about 0.1m tracking error and 75% grasping success rate.
arXiv Detail & Related papers (2020-06-07T21:18:36Z)
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.