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

arXiv: https://arxiv.org/abs/2511.04758 · HTML: https://mlsanity.com/paper/2511.04758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 18 of 18)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Imitating Task and Motion Planning with Visuomotor Transformers](https://mlsanity.com/paper/2305.16309/) (2023)
- [cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation](https://mlsanity.com/paper/2310.17274/) (2023)
- [Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances](https://mlsanity.com/paper/2108.04145/) (2021)
- [Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly](https://mlsanity.com/paper/2106.02489/) (2021)
- [dRRT*: Scalable and Informed Asymptotically-Optimal Multi-Robot Motion Planning](https://mlsanity.com/paper/1903.00994/) (2019)
- [Cooperative Task and Motion Planning for Multi-Arm Assembly Systems](https://mlsanity.com/paper/2203.02475/) (2022)
- [Differentiable GPU-Parallelized Task and Motion Planning](https://mlsanity.com/paper/2411.11833/) (2024)
- [APEX-MR: Multi-Robot Asynchronous Planning and Execution for Cooperative Assembly](https://mlsanity.com/paper/2503.15836/) (2025)
- [Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs](https://mlsanity.com/paper/1905.03179/) (2019)
- [Hierarchical Temporal Logic Task and Motion Planning for Multi-Robot Systems](https://mlsanity.com/paper/2504.18899/) (2025)
- [A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning](https://mlsanity.com/paper/2210.08005/) (2022)
- [Towards computing low-makespan solutions for multi-arm multi-task planning problems](https://mlsanity.com/paper/2305.17527/) (2023)
- [Multi-Robot Geometric Task-and-Motion Planning for Collaborative Manipulation Tasks](https://mlsanity.com/paper/2310.08802/) (2023)
- [Lazy-DaSH: Lazy Approach for Hypergraph-based Multi-robot Task and Motion Planning](https://mlsanity.com/paper/2504.05552/) (2025)

## Abstract (arXiv)

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.