Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
February 14, 2026InformationOpen Access

DeepChance-OPT: A Robust Decision-Making Framework for Dynamic Grasping in Precision Assembly

View Full Paper
Ask AI
Bookmark
Share

Authors

TWTong WeiHJHaibo Jin

Discussion

Loading...

Member takes

Overview

Experiments reveal DeepChance-OPT enhances decision-making safety and efficiency in precision assembly, indicating its practical applications.

Key Points

  • The aim is to develop a decision-making framework that enhances safety and efficiency in dynamic environments with multiple uncertainties.
  • Proposed an end-to-end differentiable disturbance-rejection framework.
  • Encoded historical observations into a low-dimensional latent representation.
  • Modeled temporal uncertainty propagation in latent space to predict future states.
  • Introduced a differentiable chance-constrained mechanism for risk assessment.
  • Executed under a unified architecture for closed-loop decision-making.
  • Achieved average decision latency of less than 4 ms.
  • Reduced constraint violation rate to 2.3%.
  • Maintained a success rate above 87.5% under composite uncertainty scenarios.
  • Outperformed traditional and data-driven approaches in precision assembly tasks.

Cite This Study

Wei et al. (2026) studied this question.

synapsesocial.com/papers/699011172ccff479cfe57909https://doi.org/10.3390/info17020187
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Chance Constraint Game‐Theoretic Differential Dynamic Programming for Safe Trajectory Optimization Under Uncertainties2026
  2. 2Dynamic optimization and precision evolution model of complex mechanical system in digital manufacturing environment2026
  3. 3Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inference2024
  4. 4Balancing autonomy and oversight in reliable agentic artificial intelligence through adaptive human interaction architectures2026 · 1 citations
  5. 5GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems2025