Robots That Find Their Own Tools: Planning with Uncertainty

by

Author: Denis Avetisyan

A new framework allows robots to dynamically search for missing objects while planning complex tasks, boosting reliability in unpredictable environments.

Integrating learned object search policies into task planning addresses the challenge of incomplete information in robotic systems.

Traditional task planning for robots assumes complete environmental knowledge, a limitation that prevents operation when critical objects are misplaced or unseen. This paper, ‘Effective Task Planning with Missing Objects using Learning-Informed Object Search’, addresses this challenge by introducing a framework that seamlessly integrates learned object search policies directly into deterministic planning systems. This allows robots to interleave planning and search, generating sound and complete plans even with uncertainty about object locations-effectively reasoning about the expected cost of finding missing items. Will this approach unlock more robust and adaptable robotic systems capable of operating reliably in complex, real-world environments?

The Illusion of Complete Knowledge

Conventional task planning for robotics relies on a complete and accurate understanding of the surrounding environment – a premise that often proves unrealistic. These systems typically operate under the assumption that a robot can perceive and process all relevant information, building a comprehensive internal model before executing any action. However, real-world settings are invariably complex and dynamic, filled with incomplete data, unpredictable changes, and inherent uncertainties. This simplification allows for elegant algorithms in controlled laboratory conditions, but it introduces significant challenges when deploying robots in messy homes, bustling offices, or outdoor landscapes where sensors are limited, visibility is obstructed, and the environment is constantly evolving. Consequently, a robot designed with this assumption may struggle to adapt to unforeseen circumstances or efficiently navigate imperfect information, hindering its ability to reliably achieve intended goals.

Robots designed under the premise of complete environmental awareness often falter when deployed in realistic settings like homes, where sensors have limited range and objects frequently obstruct views. This limitation-operating in partially observable environments-creates a significant challenge for task planning, as the robot’s internal model of the world is inherently incomplete. Consequently, actions based on this imperfect understanding can lead to failures in achieving desired goals; a robot might, for instance, attempt to navigate a cluttered room without recognizing an obstacle, or search for an object in a location it hasn’t fully explored. Addressing this requires moving beyond idealized planning algorithms towards methods that account for uncertainty, allowing robots to reason about what they don’t know and proactively seek information to improve their situational awareness and ensure reliable performance.

Bridging the Gap with Deliberate Inquiry

The LOMDP framework addresses partial observability by separating the planning process into two distinct action sets: known actions and search actions. Known actions represent those reliably executable by the agent regardless of the current state, while search actions represent deliberate exploratory behaviors used to resolve uncertainty. This decoupling enables the utilization of existing full-knowledge planners, which assume complete state information, by framing the problem as a sequence of known actions interspersed with search actions designed to gather necessary observations. Effectively, LOMDP transforms a partially observable environment into a sequence of fully observable subproblems solvable by standard planning algorithms, thereby extending their applicability to more realistic scenarios.

LOMDP capitalizes on the established efficiency of full-knowledge planners by maintaining their core functionality for deterministic action execution. However, it simultaneously addresses the challenges posed by partial observability through a decoupling mechanism. This allows planners to function effectively despite incomplete state information, a common characteristic of real-world environments, by explicitly modeling uncertainty as part of the planning process. The framework does not require modification of the underlying planner itself; instead, it layers an abstraction that manages the discrepancy between the planner’s assumed complete knowledge and the actual partial observability of the environment, enabling the reuse of existing planning algorithms and heuristics.

Decoupling known actions from search actions is essential for scaling task planning in complex, real-world scenarios because these environments are often modeled as Stochastic Markov Decision Processes (MDPs). A Stochastic MDP introduces both stochasticity – where the outcomes of actions are probabilistic – and partial observability – where the agent’s state is not fully known. Traditional planning methods struggle with the exponential growth in computational complexity when dealing with these factors. By separating the reliably executable, known actions from the search-based actions used for planning under uncertainty, the LOMDP framework reduces the effective state space considered during search, thereby improving scalability and enabling solutions for problems with high stochasticity and incomplete information. This approach allows leveraging the efficiency of existing full-knowledge planners while explicitly addressing the challenges posed by the inherent uncertainty in complex environments.

The Precision of Informed Search

The ‘Deterministic Find Action’ is a formalized operator within the planning domain, designed to represent the process of locating an object. This abstraction allows object search to be treated as a standard planning step, compatible with existing PDDL (Planning Domain Definition Language) systems. Instead of explicitly modeling the low-level actions involved in searching, the ‘Find Action’ encapsulates this process, receiving preconditions related to the object’s existence and potential location, and producing an effect indicating successful object localization. This enables planners to reason about when and where to search for an object as part of a larger plan, rather than requiring a separate, specialized search module.

Object State Prediction is implemented using two distinct neural network architectures to assess object properties relevant to task completion. Fully Connected Neural Networks process direct observations to determine states like cleanliness, while Sentence-BERT embeddings capture contextual information from textual descriptions of the environment and objects. This allows the system to infer object states based on semantic understanding, complementing direct observation. The combination of these methods provides a robust mechanism for determining object attributes, such as whether an object is clean or dirty, which is critical for efficient planning and task execution.

Evaluations of the learning-informed object search (LIOS) approach demonstrate a substantial improvement in object search performance, achieving a 59.9% increase when benchmarked against a greedy baseline. This performance gain indicates a significant enhancement in the efficiency of object localization within the robotic system. The metric used for this evaluation assesses the success rate of locating target objects within a defined environment and timeframe, with LIOS consistently outperforming the baseline in comparative trials. This improvement directly translates to reduced task completion times and increased operational effectiveness.

From Simulation to Embodied Reality

The developed task planning framework transcends simulated environments through practical deployment on a Spot robot, a quadrupedal platform celebrated for its adaptability and robust navigation capabilities in real-world settings. This implementation serves as critical validation, moving beyond purely computational results to demonstrate efficacy in a physically demanding and unpredictable environment. Utilizing Spot allows for assessment of the system’s ability to handle the complexities of perception, locomotion, and manipulation necessary for autonomous operation within a household – a key step towards translating algorithmic advancements into tangible robotic solutions. The robot’s versatile design enables testing across diverse terrains and obstacle configurations, providing a comprehensive evaluation of the framework’s resilience and generalizability.

The development of robust task planning algorithms benefits significantly from realistic testing environments, and PROCTHOR serves as precisely that – a highly detailed, physics-based simulation of a home. This virtual environment isn’t merely a visual representation; it incorporates interactive objects, complex room layouts, and the challenges of real-world manipulation, allowing researchers to assess the performance of algorithms in a setting that mirrors the unpredictability of human dwellings. By conducting evaluations within PROCTHOR, the efficacy of proposed frameworks can be rigorously tested against scenarios involving cluttered spaces, diverse object arrangements, and the need for adaptive planning – ultimately bridging the gap between simulated success and reliable performance in genuine domestic environments.

Evaluations conducted within the PROCTHOR simulation demonstrate a significant performance advantage for the proposed task planning framework. Specifically, in Configuration A, the system completed assigned tasks in an average of 174 seconds. This represents a substantial improvement over competing approaches, with PesGreedy requiring 311 seconds and PesLIOS taking 264 seconds to achieve the same results. These findings underscore the effectiveness of the integrated architecture, highlighting its capacity for efficient and timely task execution in complex, real-world environments. The reduced completion time suggests a more streamlined and optimized planning process, potentially enabling robots to operate with greater autonomy and responsiveness.

The study addresses a core challenge in robotic task planning: uncertainty. It acknowledges that robots often operate with incomplete information, specifically regarding object locations. This framework cleverly integrates learned search policies into existing planners, effectively bridging the gap between deterministic planning and real-world ambiguity. It echoes Blaise Pascal’s sentiment: “The eloquence of angels is silence.” The system doesn’t attempt exhaustive, perfect knowledge; instead, it efficiently seeks what’s needed, accepting a degree of uncertainty as inherent to the task. Abstractions age, principles don’t. The principle here is robustness, achieved not through complex prediction, but through adaptable search.

What’s Next?

The presented work achieves a functional integration, admittedly, but integration is often merely the postponement of difficult questions. The core limitation remains the reliance on learned object search. If the learned policy fails-encountering a novel environment, an unforeseen occlusion, or simply a statistical outlier-the entire framework falters. Robustness, then, is not a matter of more learning, but of acknowledging the inherent unpredictability. A truly parsimonious approach would involve a planner capable of actively reducing uncertainty, not simply navigating it.

The current paradigm implicitly assumes a static world, despite the motion inherent in robotic tasks. Extending this framework to dynamically changing environments-where objects move, appear, or disappear during planning-presents a significant challenge. It begs the question: at what point does anticipating object behavior become more efficient than continually re-searching? The answer, predictably, lies in minimizing assumptions, not multiplying them.

Ultimately, the field requires a shift from probabilistic planning about uncertainty to planning with uncertainty. Not a richer model of the unknown, but a simpler acceptance of it. If a robot cannot operate effectively without knowing the location of every object, then the problem is not the planning algorithm, but the unrealistic expectation of complete knowledge. The path forward isn’t complexity; it’s subtraction.

Original article: https://arxiv.org/pdf/2602.11468.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-02-14 01:24