Anticipatory Planning: Improving Long-Lived Planning by Estimating Expected Cost of Future Tasks
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Abstract (arXiv)
We consider a service robot in a household environment given a sequence of high-level tasks one at a time. Most existing task planners, lacking knowledge of what they may be asked to do next, solve each task in isolation and so may unwittingly introduce side effects that make subsequent tasks more costly. In order to reduce the overall cost of completing all tasks, we consider that the robot must anticipate the impact its actions could have on future tasks. Thus, we propose anticipatory planning: an approach in which estimates of the expected future cost, from a graph neural network, augment model-based task planning. Our approach guides the robot towards behaviors that encourage preparation and organization, reducing overall costs in long-lived planning scenarios. We evaluate our method on blockworld environments and show that our approach reduces the overall planning costs by 5% as compared to planning without anticipatory planning. Additionally, if given an opportunity to prepare the environment in advance (a special case of anticipatory planning), our planner improves overall cost by 11%.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 26 of its 54 references that are in the map. First posted on arXiv 8 May 2023.
Builds on
26 of 54 references.
References · 26
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
- 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
- 2019ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Taskscited by 773
- 2018Diversity is All You Need: Learning Skills without a Reward Functioncited by 734
- 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
- 2020Integrated Task and Motion Planningcited by 281
14 more
- 2018PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planningcited by 147
- 2020Reward-rational (implicit) choice: A unifying formalism for reward learningcited by 119
- 2020Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networkscited by 38
- 2019Preferences Implicit in the State of the Worldcited by 33
- 2020Avoiding Side Effects By Considering Future Taskscited by 32
- 2018Active model learning and diverse action sampling for task and motion planningcited by 28
- 2018Learning to guide task and motion planning using score-space representationcited by 27
- 2017Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance Modelingcited by 25
- 2019On the Utility of Model Learning in HRIcited by 21
- 2021Learning Human Objectives from Sequences of Physical Correctionscited by 21
- 2018Learning Quickly to Plan Quickly Using Modular Meta-Learningcited by 15
- 2018Courteous Autonomous Carscited by 14
- 2021Learning to Pick at Non-Zero-Velocity from Interactive Demonstrationscited by 6
- 2019Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networkscited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.