An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios

• URL: http://arxiv.org/abs/2102.03138v1
• Date: Fri, 5 Feb 2021 12:30:23 GMT
• Title: An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios
• Authors: Chengmin Zhou, Bingding Huang, Pasi Fr\"anti
• Abstract summary: In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning. It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.
• Score: 0.8594140167290099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent robots provide a new insight into efficiency improvement in industrial and service scenarios to replace human labor. However, these scenarios include dense and dynamic obstacles that make motion planning of robots challenging. Traditional algorithms like A* can plan collision-free trajectories in static environment, but their performance degrades and computational cost increases steeply in dense and dynamic scenarios. Optimal-value reinforcement learning algorithms (RL) can address these problems but suffer slow speed and instability in network convergence. Network of policy gradient RL converge fast in Atari games where action is discrete and finite, but few works have been done to address problems where continuous actions and large action space are required. In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning, therefore optimal speeds and directions of robot are generated. Experimental results demonstrate that our algorithm converges faster and stable than optimal-value RL. It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.

Related papers

• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• Exploring Robot Trajectory Planning -- A Comparative Analysis of Algorithms And Software Implementations in Dynamic Environments [0.0]
  Trajectory planning is a crucial word in Modern & Advanced Robotics. It's a way of generating a smooth and feasible path for the robot to follow over time. Trajectory planning is extensively used in Automobile Industrial Robot, Manipulators, and Mobile Robots.
  arXiv  Detail & Related papers  (2024-07-18T09:30:27Z)
• Hybrid of representation learning and reinforcement learning for dynamic and complex robotic motion planning [3.794762046318001]
  This paper introduces a hybrid algorithm for robotic motion planning: long short-term memory (LSTM) pooling and skip connection for attention-based discrete soft actor critic (LSA-DSAC) Experiments show that LSA-DSAC outperforms the state-of-the-art in training and most evaluations.
  arXiv  Detail & Related papers  (2023-09-07T15:00:49Z)
• Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks [29.239926645660823]
  This paper introduces a novel learning-to-plan framework that exploits the concept of constraint manifold. Our approach generates plans satisfying an arbitrary set of constraints and computes them in a short constant time, namely the inference time of a neural network. We validate our approach on two simulated tasks and in a demanding real-world scenario, where we use a Kuka LBR Iiwa 14 robotic arm to perform the hitting movement in robotic Air Hockey.
  arXiv  Detail & Related papers  (2023-01-11T06:54:11Z)
• Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding [15.58317292680615]
  We propose a GNN-based approach that uses temporal encoding and imitation learning with data aggregation for learning both the embeddings and the edge prioritization policies. Experiments show that the proposed methods can significantly accelerate online planning over state-of-the-art complete dynamic planning algorithms.
  arXiv  Detail & Related papers  (2022-10-16T01:27:16Z)
• Obstacle Avoidance for Robotic Manipulator in Joint Space via Improved Proximal Policy Optimization [6.067589886362815]
  In this paper, we train a deep neural network via an improved Proximal Policy Optimization (PPO) algorithm to map from task space to joint space for a 6-DoF manipulator. Since training such a task in real-robot is time-consuming and strenuous, we develop a simulation environment to train the model. Experimental results showed that using our method, the robot was capable of tracking a single target or reaching multiple targets in unstructured environments.
  arXiv  Detail & Related papers  (2022-10-03T10:21:57Z)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• 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)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)

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.