Robots Learn to Resolve Uncertainty in Complex Tasks

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Author: Denis Avetisyan

New research presents a visuomotor policy that allows robots to effectively handle ambiguous states during long-horizon manipulation by intelligently recalling past information.

Robotic manipulation often encounters state ambiguity-where sequential actions don’t adhere to the Markov assumption-necessitating a long history window for accurate control, a challenge addressed by a temporally-dependent inference process that, analogous to human reasoning, extends this window via an adaptive working memory to encode only current observations at each step.
Robotic manipulation often encounters state ambiguity-where sequential actions don’t adhere to the Markov assumption-necessitating a long history window for accurate control, a challenge addressed by a temporally-dependent inference process that, analogous to human reasoning, extends this window via an adaptive working memory to encode only current observations at each step.

This work introduces PAM, an adaptive working memory system enabling temporal disambiguation and robust performance in complex robotic manipulation scenarios.

Effective robotic manipulation often requires disambiguating observations that could correspond to multiple valid action trajectories. This challenge is addressed in ‘Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding’, which introduces PAM, a novel visuomotor policy leveraging adaptive working memory to maintain a long-term understanding of task context. By efficiently processing extended historical data-up to 300 frames-PAM achieves robust performance across ambiguous scenarios while sustaining high inference speeds. Could this approach, inspired by human cognitive processes, unlock more adaptable and reliable long-horizon robotic systems?

The Illusion of Now: Why Robots Struggle with Time

Conventional robotic control systems often operate under the premise of the Markov Assumption, a simplifying principle stating that a system’s future state is entirely determined by its present state, disregarding past history. While effective in straightforward scenarios, this approach falters when applied to complex, long-horizon tasks like intricate assembly or sustained manipulation. The assumption breaks down because real-world environments are inherently partially observable, and numerous future possibilities can align with a single present observation. Consequently, a robot relying solely on the current state may struggle to differentiate between plausible trajectories, leading to hesitant or incorrect actions. This limitation highlights the need for robotic systems capable of incorporating historical information and probabilistic reasoning to navigate uncertainty and achieve robust performance in dynamic environments.

State ambiguity presents a significant hurdle in robotic manipulation, arising when a robot perceives the same sensory input – the identical observation – while facing multiple plausible future actions. This isn’t a matter of imperfect sensors, but a fundamental issue in complex environments; for example, a robotic arm reaching for an object might interpret a partially obscured view as indicating several equally valid grasping positions. Consequently, the robot struggles to select a reliable trajectory, leading to hesitant or incorrect actions. This phenomenon hinders robust performance, particularly in scenarios demanding long-horizon planning, as the accumulation of such ambiguities can quickly derail a sequence of movements. Effectively, the robot is unsure which of several possible ‘realities’ it occupies, and therefore cannot confidently execute a task.

Robotic systems confronting state ambiguity necessitate a departure from immediate sensory input towards architectures that prioritize temporal understanding. Rather than reacting solely to the present, these systems must actively maintain an internal representation of past events and potential future outcomes, effectively building a “memory” of interactions. This allows a robot to disambiguate current observations by considering the sequence of actions and perceptions that led to the present moment, and to predict plausible trajectories based on extended reasoning. Such capabilities move beyond simple reactive control, enabling robots to anticipate consequences, recover from errors, and ultimately perform complex, long-horizon tasks in uncertain environments by leveraging the richness of historical data and projected possibilities.

A suite of real-world tasks, designed to evaluate performance under state ambiguity, was created and assessed by averaging the completion rates of individual subtasks within each task.
A suite of real-world tasks, designed to evaluate performance under state ambiguity, was created and assessed by averaging the completion rates of individual subtasks within each task.

PAM: A Patch for the Robot’s Short-Term Memory

The Policy with Adaptive Working Memory (PAM) incorporates a mechanism designed to actively manage and retain contextual information beyond immediate perception. This ‘Adaptive Working Memory’ isn’t a static buffer; instead, it dynamically recodes incoming data into a compressed, relevant representation. This process allows PAM to maintain awareness of past events and their implications over extended time horizons, facilitating decision-making that considers long-term dependencies. The system achieves this by prioritizing and retaining information deemed crucial for future predictions and actions, effectively overcoming the limitations of fixed-length memory traditionally used in reinforcement learning agents.

The Frame Feature Extractor is a crucial component responsible for processing multimodal input data. It employs DINOv2 for visual feature extraction and MiniLM to process language-based information, effectively fusing these modalities. This fusion process yields two distinct, but related, feature sets: motion primitives, which capture dynamic elements within the input, and contextual features, which represent the broader environmental understanding. The extracted features are then utilized for downstream processing within the PAM architecture, providing a rich representation of the agent’s surroundings and its own actions.

The Context Router within the PAM architecture performs dimensionality reduction on the feature vectors generated by the Frame Feature Extractor, creating a compressed contextual representation. This compression is achieved through a learned process, enabling the model to retain salient information for extended periods without incurring excessive computational costs. Specifically, the router utilizes a bottleneck architecture to distill the high-dimensional feature space into a lower-dimensional embedding, facilitating long-term temporal reasoning by preserving crucial contextual details while discarding redundant information. This compact representation allows PAM to effectively process and recall relevant historical data for improved decision-making over extended sequences.

Causal Attention within the PAM architecture enables the model to concurrently process perceptual inputs and internal decision-making features. This mechanism differs from standard attention by explicitly modeling the causal relationships between observed states and subsequent actions. By jointly attending to both perceptual cues – such as visual or auditory data – and decision features representing the agent’s internal state and goals, PAM can better disambiguate relevant information and improve the robustness of its actions. This joint attention allows the model to identify which perceptual inputs are causally linked to specific decisions, facilitating more informed and contextually appropriate behavior. The implementation allows for selective weighting of these features, prioritizing those with the strongest causal influence on the agent’s actions.

Progressive Activation of Memory (PAM) generates actions by adaptively recoding multimodal inputs into compact context features derived from both current frame observations and an extended history window, utilizing a two-stage training process that progressively activates model parameters.
Progressive Activation of Memory (PAM) generates actions by adaptively recoding multimodal inputs into compact context features derived from both current frame observations and an extended history window, utilizing a two-stage training process that progressively activates model parameters.

Predictive Action: Guessing the Future, and Reconstructing the Past

Predictive Action Matching (PAM) employs ‘Flow Matching’ to forecast future action trajectories by learning a velocity field that maps the current state to a future state. This velocity field is learned through regression, directly predicting the change in state – the velocity – required to move from one point in state space to another. Specifically, the action head within PAM is trained to estimate this velocity field, enabling the agent to predict where an action will lead over a defined time horizon. By learning this mapping, PAM can then sample future states and evaluate potential actions based on their predicted outcomes, facilitating improved long-term planning and control.

The Predictive Action Model (PAM) utilizes an auxiliary objective to enhance its comprehension of historical context by predicting past image embeddings. This is achieved by training the model to reconstruct the visual representation of prior frames given the current state and predicted actions. Specifically, the model learns to map the current observation and intended trajectory to an embedding that closely matches the embedding of the past image. This reconstruction task serves as a regularizer, forcing the model to learn a more robust and informative state representation, and improves performance in scenarios with ambiguous or incomplete observations by providing a stronger basis for understanding the sequence of events leading to the current state.

The integration of predictive action and historical context reconstruction within PAM addresses the challenge of state ambiguity by providing a mechanism for consistent control. By forecasting future action trajectories and simultaneously reconstructing past image embeddings, the model establishes a more complete understanding of the environment and its own influence upon it. This dual-objective approach allows PAM to disambiguate uncertain states – situations where current observations are insufficient to determine the correct action – by referencing predicted outcomes and validating them against a reconstructed historical record. Consequently, the model exhibits improved robustness in dynamic and potentially noisy environments, maintaining consistent control even when faced with incomplete or ambiguous sensory input.

PAM demonstrates strong interpretability through attention maps revealing its focus on key historical frames and relevant modalities-visual observations and joint states-for resolving state ambiguity and effectively encoding working memory across tasks like Wipe the Table Twice and Guessing Game.
PAM demonstrates strong interpretability through attention maps revealing its focus on key historical frames and relevant modalities-visual observations and joint states-for resolving state ambiguity and effectively encoding working memory across tasks like Wipe the Table Twice and Guessing Game.

Performance and the Illusion of Progress

The proposed Planning with Ambiguity Modulation (PAM) framework demonstrates a substantial advancement in robotic task planning, particularly when confronted with complex, long-horizon challenges. Rigorous evaluation on the ‘Libero-Long’ benchmark suite reveals PAM’s significant outperformance compared to established methodologies like Motion Tilting Learning (MTIL) and Long-horizon Diffusion Policy (LongDP). This isn’t merely incremental improvement; PAM achieves a success rate notably exceeding existing techniques, indicating a greater capacity to navigate the complexities inherent in extended robotic sequences. The framework’s ability to effectively address ambiguous states allows for more reliable planning and execution, suggesting a pathway towards robotic systems capable of tackling increasingly intricate real-world scenarios with greater autonomy and robustness.

Recent evaluations demonstrate that the proposed Planning with Ambiguity Mitigation (PAM) framework achieves a 91% success rate when applied to diverse, real-world robotic tasks. This represents a substantial performance leap, exceeding the capabilities of existing methods; notably, PAM delivers a 49.2% improvement over the previous state-of-the-art, LongDP, which achieved a 61% success rate. Furthermore, PAM’s performance more than doubles that of the baseline method, MTIL, which attained a 45% success rate under identical conditions. These results highlight PAM’s capacity to reliably execute complex robotic manipulations, offering a significant advancement in the field and suggesting its potential for widespread application in automation and beyond.

The Predictive Action Memory (PAM) framework demonstrates a noteworthy capability in long-horizon robotic tasks, achieving an 84.7% success rate on the challenging Libero-Long benchmark. This performance level is particularly significant as it matches the state-of-the-art result previously established by the π0 algorithm. This parity suggests PAM offers a competitive and viable alternative, effectively tackling the complexities inherent in extended sequences of robotic actions. By attaining comparable results to a leading method, PAM validates its approach to addressing long-term dependencies and planning in dynamic environments, highlighting its potential for broader application in advanced robotics systems.

The practical application of robotic manipulation often suffers from inherent uncertainties in perceiving and interpreting the environment – a challenge known as state ambiguity. The proposed PAM framework directly confronts this issue, enabling robots to operate with greater dependability in complex, real-world settings. By effectively mitigating the impact of imprecise state information, PAM allows for more consistent and successful task completion, even when faced with noisy sensor data or unpredictable conditions. This advancement moves beyond idealized scenarios and facilitates the deployment of robotic systems in dynamic environments where perfect perception is unattainable, ultimately increasing their robustness and widening the scope of potential applications in areas like manufacturing, logistics, and assistive robotics.

Conventional robotic systems often rely on the Markov Assumption – the idea that a system’s future state depends solely on its present state, ignoring past history. This research demonstrates a departure from that limitation, introducing a framework that explicitly accounts for historical context in robotic decision-making. By addressing the shortcomings of this assumption, particularly in complex, long-horizon tasks, the work facilitates the development of robotic systems capable of greater adaptability and robustness. This enhanced capacity for understanding and responding to nuanced situations, informed by past interactions, promises to unlock more intelligent and reliable robotic performance in real-world scenarios, moving beyond the constraints of systems that treat each moment as independent.

The pursuit of elegant solutions in robotic manipulation, as demonstrated by PAM’s adaptive working memory, invariably invites future complications. This paper attempts to resolve state ambiguity via temporal reasoning, but it’s a temporary victory. One anticipates unforeseen edge cases will emerge, demanding further refinement of the history window. As Grace Hopper observed, “It’s easier to ask forgiveness than it is to get permission.” The same holds true for deploying these systems; the elegance of the theory rarely survives contact with the unpredictable realities of long-horizon manipulation. Tests may suggest robustness, but they remain, at best, a form of faith, not certainty.

What’s Next?

The pursuit of resolving state ambiguity, as exemplified by this work, inevitably reveals a simple truth: each elegantly disambiguated state merely exposes a new, more subtle ambiguity lurking beneath. PAM, with its adaptive working memory, offers a temporary reprieve, a slightly larger history window before the inevitable drift towards unrecoverable error. It’s a predictable outcome; production environments aren’t designed to reward theoretical purity, only demonstrable uptime. The system will fail in novel ways, and the ‘long horizon’ will simply become a longer log of failures to parse.

Future iterations will likely focus on ‘robustness’ – a euphemism for adding layers of heuristics to mask fundamental limitations. Expect to see increased integration with simulation, not to achieve true generalization, but to generate synthetic corner cases for increasingly complex failure modes. The goal won’t be intelligence, but exhaustively cataloging all the ways things can go wrong. Documentation, of course, remains a myth invented by managers.

Ultimately, this line of inquiry reinforces a core principle: anything that promises to simplify life adds another layer of abstraction. And each layer, while momentarily relieving the pressure, merely concentrates the point of failure. The real challenge isn’t solving ambiguity; it’s building systems that gracefully degrade – and accepting that CI is now the temple, and every passing build a desperate, whispered prayer.

Original article: https://arxiv.org/pdf/2512.24638.pdf

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

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2026-01-02 18:36