Policy-Guided Lazy Search with Feedback for Task and Motion Planning

• URL: http://arxiv.org/abs/2210.14055v4
• Date: Wed, 23 Aug 2023 12:03:58 GMT
• Title: Policy-Guided Lazy Search with Feedback for Task and Motion Planning
• Authors: Mohamed Khodeir, Atharv Sonwane, Ruthrash Hari, Florian Shkurti
• Abstract summary: PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning problems. We propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments.
• Score: 19.789123503976917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems.

Related papers

• Interactive and Expressive Code-Augmented Planning with Large Language Models [62.799579304821826]
  Large Language Models (LLMs) demonstrate strong abilities in common-sense reasoning and interactive decision-making. Recent techniques have sought to structure LLM outputs using control flow and other code-adjacent techniques to improve planning performance. We propose REPL-Plan, an LLM planning approach that is fully code-expressive and dynamic.
  arXiv  Detail & Related papers  (2024-11-21T04:23:17Z)
• Leveraging Environment Interaction for Automated PDDL Translation and Planning with Large Language Models [7.3238629831871735]
  Large Language Models (LLMs) have shown remarkable performance in various natural language tasks. Planning problems into the Planning Domain Definition Language (PDDL) has been proposed as a potential solution. We propose a novel approach that leverages LLMs and environment feedback to automatically generate PDDL domain and problem description files.
  arXiv  Detail & Related papers  (2024-07-17T19:50:51Z)
• Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty [56.30846158280031]
  Task planning for embodied AI has been one of the most challenging problems. We propose a task-agnostic method named 'planning as in-painting' The proposed framework achieves promising performances in various embodied AI tasks.
  arXiv  Detail & Related papers  (2023-12-02T10:07:17Z)
• Robust Multi-Agent Pickup and Delivery with Delays [5.287544737925232]
  Multi-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents. Current algorithms for MAPD do not consider many of the practical issues encountered in real applications. We present two solution approaches that provide robustness guarantees by planning paths that limit the effects of imperfect execution.
  arXiv  Detail & Related papers  (2023-03-30T14:42:41Z)
• A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems [11.66506213335498]
  Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. We formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL. We provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem.
  arXiv  Detail & Related papers  (2022-12-11T05:30:44Z)
• Multi-Objective Policy Gradients with Topological Constraints [108.10241442630289]
  We present a new algorithm for a policy gradient in TMDPs by a simple extension of the proximal policy optimization (PPO) algorithm. We demonstrate this on a real-world multiple-objective navigation problem with an arbitrary ordering of objectives both in simulation and on a real robot.
  arXiv  Detail & Related papers  (2022-09-15T07:22:58Z)
• Accelerated Reinforcement Learning for Temporal Logic Control Objectives [10.216293366496688]
  This paper addresses the problem of learning control policies for mobile robots modeled as unknown Markov Decision Processes (MDPs) We propose a novel accelerated model-based reinforcement learning (RL) algorithm for control objectives that is capable of learning control policies significantly faster than related approaches.
  arXiv  Detail & Related papers  (2022-05-09T17:09:51Z)
• Integrated Task and Motion Planning [30.415785183398334]
  Planning for a robot that operates in environments containing a large number of objects is known as task and motion planning (TAMP) TAMP problems contain elements of discrete task planning, discrete-continuous mathematical programming, and continuous motion planning, and thus cannot be effectively addressed by any of these fields directly. In this paper, we define a class of TAMP problems and survey algorithms for solving them, characterizing the solution methods in terms of their strategies for solving the continuous-space subproblems and their techniques for integrating the discrete and continuous components of the search.
  arXiv  Detail & Related papers  (2020-10-02T16:23:08Z)
• POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments [89.43830036483901]
  We focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method uses as input the current pose of an agent and a RGB-D frame. We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1.
  arXiv  Detail & Related papers  (2020-09-17T08:23:50Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Jump Operator Planning: Goal-Conditioned Policy Ensembles and Zero-Shot Transfer [71.44215606325005]
  We propose a novel framework called Jump-Operator Dynamic Programming for quickly computing solutions within a super-exponential space of sequential sub-goal tasks. This approach involves controlling over an ensemble of reusable goal-conditioned polices functioning as temporally extended actions. We then identify classes of objective functions on this subspace whose solutions are invariant to the grounding, resulting in optimal zero-shot transfer.
  arXiv  Detail & Related papers  (2020-07-06T05:13:20Z)

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.