Guided search for task and motion plans using learned heuristics

Chitnis, Rohan; Hadfield-Menell, Dylan; Gupta, Abhishek; Srivastava, Siddharth; Groshev, Edward; Lin, Christopher H.; Abbeel, Pieter

doi:10.1109/icra.2016.7487165

Cited by 55 publications

(38 citation statements)

References 12 publications

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“…Several recent works apply learning to TMP. Chitnis et al [33] give a probabilistically complete method which uses reinforcement learning to guide both the task level search for a plan as well as the "refinement" from a symbolic description into a concrete plan. In [34] the notion of "score-space" is introduced as a metric to measure similarity between problem instances to improve motion planning time.…”

Section: Heuristics For Task and Motion Planningmentioning

confidence: 99%

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Wells

Dantam

Shrivastava

et al. 2019

IEEE Robot. Autom. Lett.

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Task and motion planning (TMP) combines discrete search and continuous motion planning. Earlier work has shown that to efficiently find a task-motion plan, the discrete search can leverage information about the continuous geometry. However, incorporating continuous elements into discrete planners presents challenges. We improve the scalability of TMP algorithms in tabletop scenarios with a fixed robot by introducing geometric knowledge into a constraint-based task planner in a robust way. The key idea is to learn a classifier for feasible motions and to use this classifier as a heuristic to order the search for a task-motion plan. The learned heuristic guides the search towards feasible motions and thus reduces the total number of motion planning attempts. A critical property of our approach is allowing robust planning in diverse scenes. We train the classifier on minimal exemplar scenes and then use principled approximations to apply the classifier to complex scenarios in a way that minimizes the effect of errors. By combining learning with planning, our heuristic yields order-of-magnitude run time improvements in diverse tabletop scenarios. Even when classification errors are present, properly biasing our heuristic ensures we will have little computational penalty.

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Section: Heuristics For Task and Motion Planningmentioning

confidence: 99%

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Wells

Dantam

Shrivastava

et al. 2019

IEEE Robot. Autom. Lett.

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show abstract

“…In addition, some recent papers have investigated learning effective samplers within the context of TAMP . Chitnis et al (2016) frame learning plan parameters as a reinforcement-learning problem and learn a randomized policy that samples from a discrete set of robot base and object poses. Kim et al (2017) proposed a method for selecting from a discrete set of samples by ranking new samples based on their correlation with previously attempted samples.…”

Section: Related Workmentioning

confidence: 99%

Learning compositional models of robot skills for task and motion planning

Wang

Garrett²,

Kaelbling³

et al. 2021

The International Journal of Robotics Research

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The objective of this work is to augment the basic abilities of a robot by learning to use sensorimotor primitives to solve complex long-horizon manipulation problems. This requires flexible generative planning that can combine primitive abilities in novel combinations and, thus, generalize across a wide variety of problems. In order to plan with primitive actions, we must have models of the actions: under what circumstances will executing this primitive successfully achieve some particular effect in the world? We use, and develop novel improvements to, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the constraints on skill effectiveness from small numbers of expensive-to-collect training examples. In addition, we develop efficient adaptive sampling methods for generating a comprehensive and diverse sequence of continuous candidate control parameter values (such as pouring waypoints for a cup) during planning. These values become end-effector goals for traditional motion planners that then solve for a full robot motion that performs the skill. By using learning and planning methods in conjunction, we take advantage of the strengths of each and plan for a wide variety of complex dynamic manipulation tasks. We demonstrate our approach in an integrated system, combining traditional robotics primitives with our newly learned models using an efficient robot task and motion planner. We evaluate our approach both in simulation and in the real world through measuring the quality of the selected primitive actions. Finally, we apply our integrated system to a variety of long-horizon simulated and real-world manipulation problems.

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“…reasoning about complex targets and feasible movements in configuration space). For example, Chitnis et al [290] exploited the randomized local search algorithm, which can be formulated as an MDP. They aimed to speed up task and motion planning (TAMP), which has the capability to learn samplers of continuous action parameters by exploiting the SGD classifier.…”

Section: A Humanoid Robotsmentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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Guided search for task and motion plans using learned heuristics

Cited by 55 publications

References 12 publications

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Learning Feasibility for Task and Motion Planning in Tabletop Environments

Learning compositional models of robot skills for task and motion planning

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

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