DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief Propagation
We propose a new distributed dynamic Gaussian belief propagation (DD-GBP) method that directly plans joint-space trajectories through parallel local inference.
ProofPaper ↗
Key points
- Safely executing long-horizon multi-arm tasks online in complex dynamic environments remains challenging.
- A unified Gaussian-process (GP) parameterization connects adaptive planning horizons, interpolation-time factors, and continuous execution.
- Dynamic subnet reconstruction and event-triggered updates reduce communication and inference costs while responding to moving obstacles.
- In 50 long-horizon, multi-target four-arm scenes with moving obstacles, DD-GBP achieves $92.5%$ collision-free all-target completion and the highest success rate among six representative baselines.
Sources (1)
- [1]DD-GBP: Anytime Multi-Arm Planning and Continuous Execution via Distributed Dynamic Gaussian Belief PropagationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 04:43 PM
We propose a new distributed dynamic Gaussian belief propagation (DD-GBP) method that directly plans joint-space trajectories through parallel local inference.
Safely executing long-horizon multi-arm tasks online in complex dynamic environments remains challenging.
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