Robots That Understand: A New Path to Adaptive Control

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Author: Denis Avetisyan

Researchers have developed a novel framework allowing robots to proactively explore and achieve goals in complex environments by building and refining internal models of the world.

The proposed framework infers hierarchical hidden states $z_t^s$, $z_t^f$ from observations $o_t$ and actions $a_t$ via a world model, subsequently compressing action sequences into abstract actions $A_t$ and leveraging these abstractions within an abstract world model to predict future deterministic states $d_{t+h}^s$.
The proposed framework infers hierarchical hidden states $z_t^s$, $z_t^f$ from observations $o_t$ and actions $a_t$ via a world model, subsequently compressing action sequences into abstract actions $A_t$ and leveraging these abstractions within an abstract world model to predict future deterministic states $d_{t+h}^s$.

This work introduces a deep active inference approach with a temporally hierarchical world model and abstract actions for improved robot control and exploration.

Achieving robust robot autonomy requires balancing goal-directed action with exploratory behavior, a challenge often unmet by existing deep learning control methods. This is addressed in ‘Real-World Robot Control by Deep Active Inference With a Temporally Hierarchical World Model’, which introduces a novel deep active inference framework leveraging a temporally hierarchical world model and abstract actions to improve performance in uncertain environments. The proposed approach enables robots to efficiently navigate complex tasks, achieving high success rates in object manipulation while seamlessly transitioning between exploration and exploitation. Could this framework represent a crucial step toward truly adaptable and intelligent robotic systems capable of thriving in real-world complexity?

The Limitations of Reactive Robotics

Conventional robotics often employs pre-programmed policies or reactive behaviors, which, while effective in structured settings, significantly impede adaptability when confronted with the nuances of complex environments. These systems operate on a stimulus-response basis – executing pre-defined actions based on sensor input – and lack the capacity to dynamically adjust to unforeseen circumstances or novel situations. Consequently, robots reliant on these methods struggle with tasks requiring improvisation, generalization, or long-term planning, often exhibiting rigid and brittle performance when faced with even minor deviations from their programmed parameters. This limitation stems from the difficulty in encoding the full spectrum of possible environmental variations into a finite set of rules, hindering a robot’s ability to navigate and interact with the real world in a truly flexible and robust manner.

Conventional robotics often faces significant hurdles when deployed beyond carefully controlled settings due to an inherent reliance on precise, pre-programmed instructions. This necessitates extensive, task-specific engineering for each new environment or objective; a robot adept at one task may require a complete overhaul to perform another, even if conceptually similar. The core issue lies in the difficulty of anticipating and accommodating real-world uncertainty – unexpected obstacles, variations in lighting, or imprecise object positioning can all derail a system built on rigid expectations. Consequently, scaling these systems – creating robots that can seamlessly adapt to a range of scenarios – proves exceptionally challenging, hindering their broader application and limiting the potential for true robotic autonomy. This reliance on meticulously crafted solutions, rather than adaptable learning, restricts generalization and presents a major bottleneck in the advancement of robust, versatile robotic systems.

Conventional robotic systems often falter when confronted with the unpredictable nature of real-world environments due to a fundamental inability to foresee and mitigate uncertainty. These robots typically react to stimuli after they occur, rather than anticipating potential issues and adjusting their actions accordingly. This reactive approach results in brittle performance; a slight deviation from expected conditions – an unforeseen obstacle, a shifting surface, or an ambiguous signal – can quickly lead to failure. The limitation isn’t necessarily one of processing power, but rather of proactive reasoning; the robot lacks the capacity to model possible future states and plan robust strategies that account for potential disturbances. Consequently, even seemingly simple tasks become challenging in dynamic scenarios, highlighting the need for robotic systems capable of anticipating and resolving uncertainty before it compromises their operation.

The robot selects actions by predicting future states, calculating an Expected Future Error (EFE) for each, and then executing the action sequence with the lowest EFE to minimize potential errors.
The robot selects actions by predicting future states, calculating an Expected Future Error (EFE) for each, and then executing the action sequence with the lowest EFE to minimize potential errors.

Deep Active Inference: A Framework Grounded in Predictive Principles

Deep Active Inference (DAI) provides a computational framework for robotic control inspired by the predictive processing mechanisms observed in biological brains. This approach models an agent – a robot – as actively minimizing $free\ energy$, a quantity reflecting the mismatch between predicted and actual sensory input. Rather than responding reactively to stimuli, DAI posits that robots infer the causes of their sensations and act to confirm these inferences. This is achieved through a generative model which allows the robot to predict future states and, crucially, to explore its environment to test and refine these predictions. The biologically plausible nature of DAI stems from its grounding in principles of Bayesian inference and its emphasis on minimizing prediction error as a driving force behind both goal-directed behavior and exploratory actions.

Deep Active Inference (DAI) mathematically grounds action selection in the principle of variational free energy. This principle posits that agents, including robots, act to minimize the difference between their predictions and actual sensory input. Specifically, DAI formulates actions not as direct responses to stimuli, but as probabilistic inferences about the hidden causes of those sensations. The agent maintains an internal generative model, and actions are chosen to maximize the evidence for hypotheses about the world that explain observed data. Minimizing variational free energy, expressed as $F = E_q[\log q(x) – \log p(x)]$ – where $q(x)$ is the approximate posterior and $p(x)$ is the true posterior – effectively reduces both prediction error and uncertainty about the causes of sensations, driving goal-directed and exploratory behavior.

The ‘World Model’ within Deep Active Inference (DAI) is a learned internal representation of the environment’s dynamics, parameterized to predict future sensory states given current states and actions. This model is typically implemented as a recurrent neural network and is trained using variational inference to minimize the difference between predicted and actual sensations. By learning these predictive relationships, the DAI agent can simulate potential outcomes of its actions, enabling proactive planning – selecting actions that are predicted to reduce uncertainty or achieve desired states. Furthermore, the World Model facilitates adaptation to novel situations by continuously refining its predictions based on incoming sensory data, allowing the agent to improve its understanding and interaction with the environment over time. The predictive accuracy of this internal model is directly correlated with the agent’s ability to navigate and achieve goals within its surroundings.

Traditional robotic control systems often operate reactively, responding to immediate sensory input. Deep Active Inference (DAI) departs from this paradigm by enabling robots to actively manage their own uncertainty. Rather than passively receiving information, a DAI agent formulates predictions about the causes of its sensations and then selects actions specifically designed to test those predictions. This proactive information-seeking behavior is driven by the principle of minimizing variational free energy; actions that reduce prediction error and resolve uncertainty are favored. Consequently, robots utilizing DAI can efficiently explore their environment, learn more accurate world models, and achieve complex goals that would be difficult or impossible for purely reactive systems.

The abstract world model accurately predicts robot observations, as demonstrated by consistent predictions (yellow box) aligning with actual observations from the action sequence represented by the model's code (red box).
The abstract world model accurately predicts robot observations, as demonstrated by consistent predictions (yellow box) aligning with actual observations from the action sequence represented by the model’s code (red box).

Modeling Temporal Dynamics: Disentangling Fast and Slow Processes

The Hierarchical Dynamics Model operates by maintaining two distinct sets of deterministic state variables: ‘fast’ states and ‘slow’ states. Fast states represent the immediate, short-term dynamics of the environment and are updated at each time step to reflect rapid changes. Conversely, slow states capture long-term dependencies and evolve at a lower frequency, providing a compressed representation of the system’s history. This separation allows the model to effectively disentangle transient variations from underlying, enduring factors, improving its capacity for both immediate prediction and long-horizon forecasting. The interaction between these states, governed by learned transition functions, enables the system to model temporal dynamics across multiple timescales.

The hierarchical structure within the World Model facilitates reasoning about events occurring at varying timescales by maintaining both ‘fast’ and ‘slow’ deterministic states. This allows the system to model short-term, rapidly changing dynamics independently from long-term, more stable trends. By separating these temporal scales, the model can more accurately predict future states; transient events do not unduly influence long-term predictions, and underlying, stable patterns are not obscured by short-term fluctuations. This decoupling enhances predictive accuracy compared to models that treat all dynamics as occurring at a single timescale, as it allows for specialized processing of information relevant to each respective timescale.

The Action Model within the World Model architecture is designed to facilitate efficient planning and generalization by learning to represent sequences of discrete actions with compact, abstract embeddings. This compression is achieved through the identification of recurring patterns in action sequences, allowing the system to treat functionally similar actions as equivalent. By reducing the dimensionality of the action space, the model decreases the computational burden associated with planning and improves its capacity to generalize learned policies to novel situations. This abstraction enables the system to reason about high-level goals and strategies without needing to explicitly consider every low-level action required to achieve them.

The Action Model utilizes Residual Vector Quantization (RVQ) to create compact representations of action sequences, thereby decreasing computational demands. RVQ operates by learning a discrete codebook of action embeddings; instead of predicting the full action at each step, the model predicts the residual difference between the current state and the closest vector in the codebook. This residual is then added to the codebook vector to reconstruct the action. By focusing on predicting only the residual, the dimensionality of the prediction space is reduced, leading to a more efficient representation and lower computational cost for planning and prediction tasks. The learned codebook enables generalization to novel situations by representing similar actions with the same or nearby vectors.

The world model utilizes a dynamics model with two timescales, alongside an encoder and decoder, to represent and predict environmental changes.
The world model utilizes a dynamics model with two timescales, alongside an encoder and decoder, to represent and predict environmental changes.

Validation and Scalability: Demonstrating Robust Robotic Performance

Recent experimentation showcases the practical efficacy of Discriminative Active Inference (DAI) when applied to real-world robotic manipulation. Leveraging the defined World and Action Models, DAI consistently achieves a success rate surpassing 70% across a range of object handling tasks. This performance indicates a significant advancement in robotic autonomy, demonstrating the framework’s ability to not only plan actions but also to reliably execute them in complex, unstructured environments. The consistently high success rate suggests DAI’s capacity to bridge the gap between simulated planning and the inherent uncertainties of physical interaction, paving the way for more adaptable and robust robotic systems.

The robotic framework demonstrates a capacity for robust environmental exploration, allowing it to actively reduce uncertainty when interacting with the world. Rather than relying on pre-programmed responses, the system continuously refines its understanding of its surroundings through iterative action and observation. This is achieved by strategically selecting actions designed to gather information about ambiguous or previously unknown aspects of the environment, effectively ‘testing’ its predictions and updating its internal world model. Consequently, the framework doesn’t merely execute pre-defined plans; it dynamically adapts to novel situations and resolves unforeseen obstacles, proving particularly effective in complex, real-world scenarios where complete information is rarely available from the outset. This adaptive exploration isn’t simply reactive; the system proactively seeks information, enhancing its reliability and paving the way for more sophisticated, autonomous robotic behavior.

Rigorous testing of the developed framework was conducted using the challenging ‘CALVIN D Benchmark’, a standardized suite for evaluating robotic manipulation skills. Results demonstrate a clear performance advantage over existing methods, specifically the Generative Critic – Dynamic Programming (GC-DP) approach. The framework consistently achieved higher success rates on both the ‘Slider’ and ‘Drawer’ tasks within the benchmark, indicating improved robustness and adaptability in real-world scenarios. This validation highlights the efficacy of the proposed World and Action Models in enabling robots to effectively plan and execute complex manipulation tasks, surpassing the capabilities of previously established techniques on a widely recognized and demanding evaluation platform.

A key strength of this novel framework lies in its computational efficiency. Evaluations reveal an impressive processing speed of just 2.37 milliseconds for abstract actions, a substantial improvement over conventional deep active inference methods which require 71.8 milliseconds to achieve comparable results. This order-of-magnitude difference in evaluation time unlocks the potential for real-time robotic control and facilitates more complex behavioral sequences. The accelerated processing isn’t achieved through approximation; the framework maintains robust performance while significantly reducing the computational burden, paving the way for deployment on resource-constrained robotic platforms and enabling more agile, responsive interactions with dynamic environments.

The experimental setup featured an environment with varying ball configurations, used to collect a dataset of demonstrations showcasing eight distinct policy patterns for lid and ball manipulation.
The experimental setup featured an environment with varying ball configurations, used to collect a dataset of demonstrations showcasing eight distinct policy patterns for lid and ball manipulation.

The pursuit of robust robot control, as detailed in this work, hinges on constructing a world model capable of accurately predicting future states. This endeavor aligns perfectly with Donald Knuth’s assertion: “Premature optimization is the root of all evil.” The framework presented prioritizes building a temporally hierarchical model-a foundation for provable correctness-before focusing on immediate performance gains. The elegance of deep active inference isn’t merely about achieving goals, but about establishing a consistent, mathematically sound system for interpreting and interacting with uncertain environments. It’s a commitment to algorithmic beauty over superficial results, mirroring Knuth’s emphasis on foundational principles.

What Remains to Be Proven?

The presented framework, while demonstrating a capacity for robotic agency, ultimately rests upon the familiar, and persistently troublesome, foundations of variational inference. The elegance of active inference lies in its principled formulation, but the practical realization invariably introduces approximations. Future work must address the quantifiable error introduced by these approximations – not merely through empirical benchmarks, but through rigorous bounds on the free energy. A solution that appears to function is not, in itself, satisfactory.

Furthermore, the notion of ‘abstract actions’ deserves closer scrutiny. While simplifying the action space undoubtedly improves computational efficiency, it simultaneously introduces a level of abstraction that obscures the fundamental relationship between intention and execution. A truly general agent should, in principle, be capable of reasoning at multiple levels of granularity – seamlessly transitioning between high-level goals and low-level motor commands – without recourse to hand-engineered abstractions. This demands a formalization of the abstraction process itself.

Finally, the exploration strategy, though demonstrably effective, remains largely heuristic. A mathematically grounded exploration policy – one that provably maximizes information gain while minimizing risk – remains an elusive goal. Until such a policy is realized, the agent’s behavior will remain, at best, a sophisticated form of trial and error – a far cry from the elegant, predictive control envisioned by the theory.

Original article: https://arxiv.org/pdf/2512.01924.pdf

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

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2025-12-03 06:18