ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling

• URL: http://arxiv.org/abs/2511.04758v1
• Date: Thu, 06 Nov 2025 19:17:42 GMT
• Title: ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling
• Authors: Caelan Garrett, Fabio Ramos,
• Abstract summary: Bimanual and humanoid robots are appealing because of their human-like ability to leverage multiple arms to efficiently complete tasks.<n> controlling multiple arms at once is computationally challenging due to the growth in the hybrid discrete-continuous action space.<n> Task and Motion Planning (TAMP) algorithms can efficiently plan in hybrid spaces but generally produce plans, where only one arm is moving at a time, rather than schedules that allow for parallel arm motion.<n>We present ScheduleStream, the first general-purpose framework for planning & scheduling with sampling operations.
• Score: 3.3656696418661975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bimanual and humanoid robots are appealing because of their human-like ability to leverage multiple arms to efficiently complete tasks. However, controlling multiple arms at once is computationally challenging due to the growth in the hybrid discrete-continuous action space. Task and Motion Planning (TAMP) algorithms can efficiently plan in hybrid spaces but generally produce plans, where only one arm is moving at a time, rather than schedules that allow for parallel arm motion. In order to extend TAMP to produce schedules, we present ScheduleStream, the first general-purpose framework for planning & scheduling with sampling operations. ScheduleStream models temporal dynamics using hybrid durative actions, which can be started asynchronously and persist for a duration that's a function of their parameters. We propose domain-independent algorithms that solve ScheduleStream problems without any application-specific mechanisms. We apply ScheduleStream to Task and Motion Planning & Scheduling (TAMPAS), where we use GPU acceleration within samplers to expedite planning. We compare ScheduleStream algorithms to several ablations in simulation and find that they produce more efficient solutions. We demonstrate ScheduleStream on several real-world bimanual robot tasks at https://schedulestream.github.io.

Related papers

• A Task and Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks [3.635602838654497]
  We present an approach for integrated task and motion planning based on an AND/OR graph network.<n>We leverage it to implement different classes of task and motion planning problems (TAMP)<n>The approach is evaluated and validated both in simulation and with a real dual-arm robot manipulator, that is, Baxter from Rethink Robotics.
  arXiv  Detail & Related papers  (2025-03-10T17:28:22Z)
• Plan-over-Graph: Towards Parallelable LLM Agent Schedule [53.834646147919436]
  Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning.<n>This paper introduces a novel paradigm, plan-over-graph, in which the model first decomposes a real-life textual task into executable subtasks and constructs an abstract task graph.<n>The model then understands this task graph as input and generates a plan for parallel execution.
  arXiv  Detail & Related papers  (2025-02-20T13:47:51Z)
• 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)
• Task and Motion Planning for Execution in the Real [24.01204729304763]
  This work generates task and motion plans that include actions cannot be fully grounded at planning time. Execution combines offline planned motions and online behaviors till reaching the task goal. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise.
  arXiv  Detail & Related papers  (2024-06-05T22:30:40Z)
• Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Delivery [10.530860023128406]
  We propose a generic multi-robot planning mechanism that combines an optimal task planner and an optimal path planner. The Integrated planner, through the interaction of the task planner and the path planner, produces optimal collision-free trajectories for the robots.
  arXiv  Detail & Related papers  (2024-03-02T17:48:40Z)
• Constant-time Motion Planning with Anytime Refinement for Manipulation [17.543746580669662]
  We propose an anytime refinement approach that works in combination with constant-time motion planners (CTMP) algorithms. Our proposed framework, as it operates as a constant time algorithm, rapidly generates an initial solution within a user-defined time threshold. functioning as an anytime algorithm, it iteratively refines the solution's quality within the allocated time budget.
  arXiv  Detail & Related papers  (2023-11-01T20:40:10Z)
• Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments [54.39292848359306]
  We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
  arXiv  Detail & Related papers  (2023-03-27T01:50:45Z)
• 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)
• Distributed Allocation and Scheduling of Tasks with Cross-Schedule Dependencies for Heterogeneous Multi-Robot Teams [2.294915015129229]
  We present a distributed task allocation and scheduling algorithm for missions where the tasks of different robots are tightly coupled with temporal and precedence constraints. An application of the planning procedure to a practical use case of a greenhouse maintained by a multi-robot system is given.
  arXiv  Detail & Related papers  (2021-09-07T13:44:28Z)
• A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by Persistently Optimizing a Switchable Action Dependency Graph [65.70656676650391]
  We consider multiple Automated Guided Vehicles (AGVs) navigating a common workspace to fulfill various intralogistics tasks. One approach is to construct an Action Dependency Graph (ADG) which encodes the ordering of AGVs as they proceed along their routes. If the workspace is shared by dynamic obstacles such as humans or third party robots, AGVs can experience large delays. We present an online method to repeatedly modify acyclic ADG to minimize route completion times of each AGV.
  arXiv  Detail & Related papers  (2020-10-11T14:39:50Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)

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.