Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning

• URL: http://arxiv.org/abs/2108.06618v1
• Date: Sat, 14 Aug 2021 21:32:33 GMT
• Title: Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning
• Authors: Taeyeong Choi, Grzegorz Cielniak
• Abstract summary: "Local planning" approaches adopt various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans but also ensure significantly reduced distances at no cost of prediction reliability.
• Score: 6.015556590955814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In our previous work, we designed a systematic policy to prioritize sampling locations to lead significant accuracy improvement in spatial interpolation by using the prediction uncertainty of Gaussian Process Regression (GPR) as "attraction force" to deployed robots in path planning. Although the integration with Traveling Salesman Problem (TSP) solvers was also shown to produce relatively short travel distance, we here hypothesise several factors that could decrease the overall prediction precision as well because sub-optimal locations may eventually be included in their paths. To address this issue, in this paper, we first explore "local planning" approaches adopting various spatial ranges within which next sampling locations are prioritized to investigate their effects on the prediction performance as well as incurred travel distance. Also, Reinforcement Learning (RL)-based high-level controllers are trained to adaptively produce blended plans from a particular set of local planners to inherit unique strengths from that selection depending on latest prediction states. Our experiments on use cases of temperature monitoring robots demonstrate that the dynamic mixtures of planners can not only generate sophisticated, informative plans that a single planner could not create alone but also ensure significantly reduced travel distances at no cost of prediction reliability without any assist of additional modules for shortest path calculation.

Related papers

• Experiment Planning with Function Approximation [49.50254688629728]
  We study the problem of experiment planning with function approximation in contextual bandit problems. We propose two experiment planning strategies compatible with function approximation. We show that a uniform sampler achieves competitive optimality rates in the setting where the number of actions is small.
  arXiv  Detail & Related papers  (2024-01-10T14:40:23Z)
• Interactive Joint Planning for Autonomous Vehicles [19.479300967537675]
  In interactive driving scenarios, the actions of one agent greatly influences those of its neighbors. We present Interactive Joint Planning (IJP) that bridges MPC with learned prediction models. IJP significantly outperforms the baselines that are either without joint optimization or running sampling-based planning.
  arXiv  Detail & Related papers  (2023-10-27T17:48:25Z)
• Learning-Initialized Trajectory Planning in Unknown Environments [4.2960463890487555]
  Planning for autonomous flight in unknown environments requires precise planning for both the spatial and temporal trajectories. We introduce a novel approach that guides optimization using a Neural-d Trajectory Planner. We propose a framework that supports robust online replanning with tolerance to planning latency.
  arXiv  Detail & Related papers  (2023-09-19T15:07:26Z)
• Trajectory Test-Train Overlap in Next-Location Prediction Datasets [5.039138978031649]
  Next-location prediction has important implications in several fields, such as urban planning, geo-marketing, and disease spreading. This paper tests the generalization capability of these predictors on public mobility datasets. We propose a methodology to rerank the outputs of the next-location predictors based on spatial mobility patterns.
  arXiv  Detail & Related papers  (2022-03-07T08:39:45Z)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• Differentiable Spatial Planning using Transformers [87.90709874369192]
  We propose Spatial Planning Transformers (SPT), which given an obstacle map learns to generate actions by planning over long-range spatial dependencies. In the setting where the ground truth map is not known to the agent, we leverage pre-trained SPTs in an end-to-end framework. SPTs outperform prior state-of-the-art differentiable planners across all the setups for both manipulation and navigation tasks.
  arXiv  Detail & Related papers  (2021-12-02T06:48:16Z)
• Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions [77.34726150561087]
  This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
  arXiv  Detail & Related papers  (2020-08-19T15:22:15Z)
• Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors [124.30562402952319]
  The ability to predict and plan into the future is fundamental for agents acting in the world. Current learning approaches for visual prediction and planning fail on long-horizon tasks. We propose a framework for visual prediction and planning that is able to overcome both of these limitations.
  arXiv  Detail & Related papers  (2020-06-23T17:58:56Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

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.