# Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects

arXiv: https://arxiv.org/abs/2212.04554 · HTML: https://mlsanity.com/paper/2212.04554/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2022.

## Built on by (most cited first, 7 of 7)

- [Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness](https://mlsanity.com/paper/2403.10454/) (2024)
- [LLM-Guided Probabilistic Program Induction for POMDP Model Estimation](https://mlsanity.com/paper/2505.02216/) (2025)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Flow-based Domain Randomization for Learning and Sequencing Robotic Skills](https://mlsanity.com/paper/2502.01800/) (2025)
- [Entropy-regularized Point-based Value Iteration](https://mlsanity.com/paper/2402.09388/) (2024)
- [EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning](https://mlsanity.com/paper/2609.35047/) (2026)
- [POrTAL: Plan-Orchestrated Tree Assembly for Lookahead](https://mlsanity.com/paper/2512.06002/) (2025)

## Abstract (arXiv)

Representing and reasoning about uncertainty is crucial for autonomous agents acting in partially observable environments with noisy sensors. Partially observable Markov decision processes (POMDPs) serve as a general framework for representing problems in which uncertainty is an important factor. Online sample-based POMDP methods have emerged as efficient approaches to solving large POMDPs and have been shown to extend to continuous domains. However, these solutions struggle to find long-horizon plans in problems with significant uncertainty. Exploration heuristics can help guide planning, but many real-world settings contain significant task-irrelevant uncertainty that might distract from the task objective. In this paper, we propose STRUG, an online POMDP solver capable of handling domains that require long-horizon planning with significant task-relevant and task-irrelevant uncertainty. We demonstrate our solution on several temporally extended versions of toy POMDP problems as well as robotic manipulation of articulated objects using a neural perception frontend to construct a distribution of possible models. Our results show that STRUG outperforms the current sample-based online POMDP solvers on several tasks.