PATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constra
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: PATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constrained Motion Planning. This research proposes PATACON, a GPU-parallel bidirectional motion planning algorithm that operates in tangent space to exploit local manifold geometry induced by constraint functions. It addresses computational ineffic… arXiv Robotics — research abstracts: Sampling-based Kinodynamic Planning with World Models. This research presents a new method for motion planning using world models, where sampling-based strategies are applied to latent spaces to improve efficiency and success rates. The approach outperforms traditional optim…
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Environment: simulation
“ce, nearest-neighbor-guided RRT and projection-based KPIECE, with GPU-native batched expansion for efficient device utilization. We evaluate with an off-the-shelf DINO-WM on four simulated domains and a real-world block-pushing task. Our approaches match or exceed the success rate of CEM and MPPI at up to 13.8x lower planning time in simulation, and improve the success rate by 20% on the real robot.”
Exact source · revision 1Source owner not reported
Reported quantity · planning time: 13.8 other · Basis not reported
“ce, nearest-neighbor-guided RRT and projection-based KPIECE, with GPU-native batched expansion for efficient device utilization. We evaluate with an off-the-shelf DINO-WM on four simulated domains and a real-world block-pushing task. Our approaches match or exceed the success rate of CEM and MPPI at up to 13.8x lower planning time in simulation, and improve the success rate by 20% on the real robot.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 20 percent · Basis not reported
“ce, nearest-neighbor-guided RRT and projection-based KPIECE, with GPU-native batched expansion for efficient device utilization. We evaluate with an off-the-shelf DINO-WM on four simulated domains and a real-world block-pushing task. Our approaches match or exceed the success rate of CEM and MPPI at up to 13.8x lower planning time in simulation, and improve the success rate by 20% on the real robot.”
Exact source · revision 1Source owner not reported
Developments
- 2026-10-12 04:00 UTC · 1 reportsPATACON: GPU-Parallel Tangent-Bundle RRT-Connect for ConstraarXiv Robotics — research abstracts：PATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constrained Motion Planning
- 2026-10-12 04:00 UTC · 1 reportsSampling-based Kinodynamic Planning with World ModelsarXiv Robotics — research abstracts：Sampling-based Kinodynamic Planning with World Models
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSampling-based Kinodynamic Planning with World Models
This research presents a new method for motion planning using world models, where sampling-based strategies are applied to latent spaces to improve efficiency and success rates. The approach outperforms traditional optimizers like CEM and MP, achieving faster planning times and higher success rates in both simulated and real-world tasks.
- arXiv Robotics — research abstractsPATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constrained Motion Planning
This research proposes PATACON, a GPU-parallel bidirectional motion planning algorithm that operates in tangent space to exploit local manifold geometry induced by constraint functions. It addresses computational inefficiencies in constrained motion planning by reducing overhead from constraint projection and nearest-neighbor search, achieving significant speedups in high-dimensional tasks.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).