Reward-Centered ReST-MCTS: A Robust Decision-Making Framework for Robotic Manipulation in High Uncertainty Environments
arXiv:2503.05226v2 Announce Type: replace-cross Abstract: Monte Carlo tree search is attractive for robotic manipulation because it can improve action selection through simulation without requiring a fully differentiable policy. In uncertain domains, however, sparse terminal rewards and noisy transitions can make shallow search brittle: many candidate branches remain indistinguishable until late rollouts, and small simulation budgets amplify this ambiguity. This paper presents Reward-Centered ReST-MCTS, a decision-making framework that decomposes intermediate feedback into rule, heuristic, opt
The increasing complexity and uncertainty of real-world robotic tasks necessitate more robust decision-making frameworks, pushing research beyond traditional methods.
Improved robotic manipulation in uncertain environments is a critical enabler for wider adoption of automation, impacting various industries from logistics to manufacturing.
This framework offers a method to enhance the reliability and efficiency of robotic systems operating in unpredictable conditions, reducing the brittleness of shallow search techniques.
- · Robotics manufacturers
- · Automation integrators
- · Logistics companies
- · AI software developers
- · Companies reliant on primitive automation approaches
- · Manual labor in repetitive manipulation tasks
More capable robots enter diverse and less structured real-world environments.
Increased deployment of robots in unpredictable settings leads to greater demand for advanced AI agents and specialized sensor fusion.
The enhanced versatility of robots could accelerate the development of general-purpose humanoid robots capable of addressing a wider array of human tasks.
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Read at arXiv cs.AI