Summary of Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning, by Andrey Zhitnikov and Vadim Indelman
Anytime Probabilistically Constrained Provably Convergent Online Belief Space Planning
by Andrey Zhitnikov, Vadim Indelman
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an anytime approach to finding a safe action for autonomously operating robots, using the Monte Carlo Tree Search (MCTS) method in continuous domains. Unlike previous methods, this approach ensures safety with respect to the current search tree without relying on convergence. The algorithm is proven to converge in probability with an exponential rate, and simulations show that it finds safer actions than a baseline approach. Additionally, the algorithm consistently finds better actions than the baseline in terms of objective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps robots make good decisions by finding safe actions. It uses a special search method called MCTS, which works well in continuous spaces. Unlike other approaches, this one is always safe and doesn’t need to finish searching before making a decision. The algorithm gets better over time and makes safer choices than others do. |
Keywords
» Artificial intelligence » Probability
