Building Robots That Understand How Things Move

Author: Denis Avetisyan

Researchers have developed a new approach to modeling complex physical systems, enabling more robust planning and control for robots operating in challenging environments.

The PRISM-WM architecture addresses the challenge of hybrid dynamics by structurally decomposing transitions, where a Gating Network discerns the prevailing latent regime and Orthogonal Experts learn a diverse basis for residual dynamics-$ \Delta Z $-thereby mitigating mode collapse during planning and ensuring robust performance across varying conditions.

Prismatic World Model leverages a mixture-of-experts architecture with orthogonalization to learn compositional dynamics in latent spaces, improving performance on contact-rich manipulation tasks.

Accurate long-horizon planning in robotic systems is hindered by the difficulty of modeling hybrid dynamics-continuous motion punctuated by discrete events. This challenge is addressed in ‘Prismatic World Model: Learning Compositional Dynamics for Planning in Hybrid Systems’ with the introduction of PRISM-WM, a novel architecture that decomposes complex dynamics into composable primitives using a context-aware Mixture-of-Experts framework with an orthogonalization objective. By explicitly representing distinct dynamic modes and preventing mode collapse, PRISM-WM significantly reduces rollout drift and provides a high-fidelity substrate for trajectory optimization. Could this approach unlock more robust and adaptable model-based agents capable of navigating complex, real-world scenarios?

The Illusion of Smoothness: Why Real-World Robotics Struggles

Conventional model-based reinforcement learning often falters when applied to realistic scenarios due to the prevalence of non-smooth dynamics and contact interactions. These algorithms typically rely on precise mathematical models to predict how an agent’s actions will affect its environment; however, real-world systems frequently exhibit abrupt changes – like a robotic gripper making contact with an object, or a leg impacting the ground – that violate the smoothness assumptions underlying these models. Consequently, predictions become inaccurate, hindering the agent’s ability to learn effective control policies. The challenge lies in accurately representing phenomena like friction, collisions, and impacts, which introduce discontinuities and complexities that traditional methods struggle to accommodate, leading to instability and suboptimal performance in practical applications.

Real-world systems, frequently classified as hybrid, pose substantial difficulties for both prediction and control due to their inherent complexities. These systems are characterized by the combination of continuous and discrete dynamics – think of a robotic leg making contact with the ground, or a satellite deploying a solar panel. This blending creates non-smooth behavior that traditional control algorithms, designed for predictable, continuous change, struggle to manage. Accurate modeling becomes critical, yet capturing these abrupt transitions and intermittent interactions-like impacts or friction-requires specialized techniques beyond standard approaches. The resulting unpredictability can lead to instability or failure, demanding robust control strategies capable of adapting to these inherent uncertainties and discontinuities.

Real-world systems frequently present challenges for robotic control due to abrupt changes and physical interactions. Effectively navigating these scenarios demands models capable of representing discontinuities – sudden shifts in behavior like an object slipping or a robot’s foot making contact with the ground. Crucially, these models must also account for contact forces, the complex interplay of pushes, pulls, and frictions that arise when objects touch. Traditional methods often struggle with these non-smooth dynamics, as they rely on continuous representations that fail to capture the nuances of impacts and sustained contact. Advanced modeling techniques, therefore, focus on representing these forces and discontinuities, allowing robots to predict and react appropriately to the unpredictable nature of the physical world and achieve robust performance in complex environments.

Experiments across diverse continuous control benchmarks-including walkers, quadrupeds, and complex humanoids-demonstrate the model's ability to generalize to heterogeneous dynamics. — Experiments across diverse continuous control benchmarks-including walkers, quadrupeds, and complex humanoids-demonstrate the model’s ability to generalize to heterogeneous dynamics.

Deconstructing the Chaos: A Prismatic Approach

The Prismatic World Model utilizes an architectural design centered around decomposing complex, multi-faceted dynamics into distinct latent regimes. This decomposition is intended to facilitate more efficient and accurate modeling by isolating specific aspects of the observed data. Rather than attempting to learn a single, monolithic representation of the entire dynamic system, the model aims to represent it as a collection of specialized sub-models, each responsible for capturing a particular facet of the overall behavior. This approach allows for a more granular and focused learning process, potentially improving performance in scenarios involving highly variable or intricate dynamics.

The Prismatic World Model utilizes a Mixture-of-Experts (MoE) architecture to address complex dynamic systems by partitioning the overall problem into a set of specialized sub-problems. Each expert within the MoE is a neural network trained to model a specific facet of the observed dynamics; rather than a single, monolithic model attempting to learn all relationships, each expert concentrates on a defined aspect of the system’s behavior. This specialization allows for a more efficient parameterization and facilitates learning of nuanced patterns within the data, as each expert can develop a targeted representation for its designated domain. The collective output of these experts, weighted by the gating network, then forms the overall model prediction.

The Prismatic World Model utilizes a Gating Network to achieve efficient and accurate predictions by dynamically routing inputs to specialized experts. This network analyzes the current input and assigns weights to each expert, effectively selecting the most relevant one, or a combination of experts, for processing. This selective activation minimizes computational cost and maximizes predictive performance. Benchmarking on the MT30 dataset demonstrated a 23.5% improvement in performance resulting from this dynamic expert selection process, indicating a significant gain in modeling complex dynamics compared to systems without such a gating mechanism.

PRISM-WM consistently surpasses baseline methods across diverse humanoid control tasks, exhibiting both improved sample efficiency and superior long-term performance due to its effective modeling of complex dynamics.

Taming Redundancy: The Power of Orthogonalization

The Prismatic World Model incorporates an Orthogonalization Layer to address potential redundancies in learned feature representations. This layer functions by enforcing orthogonality – specifically, linear independence – among the features extracted during model training. By minimizing correlations between features, the Orthogonalization Layer encourages each feature to capture distinct and unique information regarding the underlying system dynamics. This process not only improves the interpretability of the learned representations, facilitating analysis of what aspects of the dynamics each feature encodes, but also contributes to a more efficient use of the model’s capacity and improved generalization performance.

The Orthogonalization Layer operates by constraining the learned feature vectors to be mutually orthogonal. This is achieved through a process that minimizes the off-diagonal elements of the feature covariance matrix, effectively decorrelating the features. By enforcing this orthogonality, the layer ensures that each learned feature captures a distinct and independent aspect of the system’s underlying dynamics, preventing redundancy in the learned representation. This decorrelation is mathematically expressed as ensuring the dot product between any two distinct feature vectors approaches zero, maximizing the variance explained by each individual feature and reducing the potential for interference during prediction.

Promoting feature diversity within the Prismatic World Model demonstrably improves generalization performance and prediction accuracy. Empirical results indicate a reduction in Dynamics Prediction Error following the implementation of orthogonalization techniques. This improvement in predictive capability is coupled with gains in computational efficiency; inference throughput reached 7712 Hz, representing a 1.03x speedup when compared to a baseline monolithic Multilayer Perceptron (MLP) model. These findings suggest that enforcing feature orthogonality not only enhances the model’s ability to handle unseen scenarios but also provides a measurable increase in processing speed.

Employing a modular, decomposable dynamics model (MoE + Orthogonal) significantly reduces error accumulation in both dynamics and reward prediction over extended horizons, showcasing its superior robustness compared to a monolithic MLP baseline.

From Prediction to Action: Planning in the Real World

The Prismatic World Model is not employed in isolation, but rather functions as a core component within advanced trajectory optimization frameworks. Integration with methods like Time-Dependent Model Predictive Control (TD-MPC) allows for dynamic adjustments to planned trajectories based on predicted environmental changes, enhancing robustness in unpredictable settings. Furthermore, coupling the model with differentiable planning techniques, such as Prismatic World Model (PWM) itself, enables efficient gradient-based optimization of control policies. This synergistic approach leverages the predictive power of the Prismatic World Model to guide the search for optimal actions, effectively bridging the gap between model-based prediction and real-time control execution and facilitating adaptable behavior in complex scenarios.

Effective navigation and decision-making in complex environments often require planning far into the future, a process known as Long-Horizon Planning. Traditional methods struggle with this due to the exponential growth of computational complexity and uncertainty as the planning horizon extends. However, by integrating a Prismatic World Model with trajectory optimization frameworks, systems can achieve efficient and robust planning over these extended horizons. This approach enables the anticipation of future states and the evaluation of potential actions across longer timescales, circumventing the limitations of shorter-sighted strategies. Consequently, robots can execute intricate maneuvers and adapt to unforeseen circumstances, demonstrating a significant improvement in performance within challenging scenarios that demand foresight and adaptability.

The system’s predictive capability extends beyond simple trajectory planning, actively enhancing control through the mechanism of Reward Prediction Error (RPE). By accurately forecasting future states, the model can generate more precise RPE signals, effectively guiding the learning process and refining control policies. This approach allows for faster adaptation and improved performance in dynamic environments, demonstrably outperforming conventional methods across a suite of challenging humanoid control tasks. Benchmarks including running, navigating mazes, balancing on a pole, and sliding scenarios reveal a significant advantage, highlighting the system’s robustness and efficiency in complex locomotion and manipulation challenges.

PRISM-WM's planning process leverages a world model to successfully predict and avoid unstable trajectories (red) in favor of stable locomotion paths (green). — PRISM-WM’s planning process leverages a world model to successfully predict and avoid unstable trajectories (red) in favor of stable locomotion paths (green).

Beyond the Horizon: Towards Truly Adaptive Robotics

Recent advancements in robotics are increasingly focused on creating systems capable of truly understanding and interacting with the world around them, and the Prismatic World Model represents a significant step in that direction. This innovative approach integrates model-based reinforcement learning with the power of Latent Variable Models, allowing robots to learn a compact, yet comprehensive, representation of their surroundings. By predicting future states and outcomes based on learned internal models, robots equipped with this framework can navigate complex environments and adapt to unforeseen changes with greater efficiency and robustness. Unlike traditional methods that often struggle with the inherent uncertainties of real-world scenarios, the Prismatic World Model facilitates proactive planning and decision-making, potentially unlocking a new era of adaptable and reliable robotic systems capable of operating in dynamic and unpredictable conditions.

Current research endeavors are actively directed towards expanding the capabilities of this robotic system by subjecting it to increasingly complex and unpredictable environments. This scaling process isn’t merely about tackling more difficult tasks, but also about imbuing the robot with the capacity for lifelong learning. The goal is to move beyond pre-programmed responses and enable continuous adaptation based on accumulated experience, allowing the system to refine its understanding of the world and improve its performance over time. Investigations are underway to explore how the Prismatic World Model can be augmented to facilitate the retention and transfer of knowledge, effectively creating a robotic system that doesn’t simply learn, but continuously evolves its skillset and becomes more resilient to novel situations – a critical step towards truly autonomous operation in the real world.

The development of robotic systems capable of thriving in real-world settings necessitates a move beyond pre-programmed behaviors and towards true adaptability. This research envisions robots that don’t simply execute instructions, but rather interpret, predict, and react to novel situations as they arise. Such systems require the capacity to build internal models of their surroundings, not as static maps, but as probabilistic understandings that accommodate change and uncertainty. By focusing on robustness and generalization, the aim is to create robots that can gracefully handle unexpected obstacles, shifting conditions, and the inherent unpredictability of dynamic environments – ultimately achieving reliable operation even when faced with the unforeseen.

The Prismatic model demonstrates significantly improved sample efficiency and consistently outperforms baseline methods in high-dimensional locomotion tasks, as evidenced by superior mean episode rewards across multiple random seeds.

The pursuit of elegant latent world models, as demonstrated by PRISM-WM and its mixture-of-experts approach, feels…familiar. It’s a sophisticated attempt to capture hybrid dynamics, orthogonalizing representations to improve planning. They’ll call it AI and raise funding, naturally. But the core issue remains: complexity breeds fragility. This architecture, with its layers of abstraction, is merely postponing the inevitable. Someone, somewhere, will encounter an edge case – a slightly different contact condition, an unexpected perturbation – and the whole carefully constructed edifice will wobble. It used to be a simple bash script, really. Now, it’s a meticulously engineered system guaranteed to fail in production in ways no one anticipated. As Donald Davies observed, “It is astonishing how little we know about the long-term effects of technology.” And this, predictably, applies even to the ‘revolutionary’ advancements in model-based reinforcement learning.

The Road Ahead

The Prismatic World Model, with its careful layering of Mixture-of-Experts and orthogonalization, feels… predictably complex. It addresses a genuine problem – hybrid dynamics remain the bane of any robot attempting anything resembling skillful manipulation – but one suspects the gains will come at a cost. More parameters, more tuning, more opportunities for catastrophic failure in production. If a system crashes consistently, at least it’s predictable. The authors rightly point to contact-rich control as a key application, yet the real test will be scaling this beyond carefully curated simulations.

The pursuit of latent world models, in general, feels like chasing a ghost. The idea that a robot can truly model its environment, encompassing all the messy, unpredictable nuances, seems… optimistic. This work nudges the field forward, certainly, but it also highlights how far we are from achieving anything resembling generalizable intelligence. One imagines future archaeologists sifting through layers of discarded world models, each one a testament to our hubris.

The next logical step isn’t necessarily more complexity, but a ruthless pruning of assumptions. Can simpler models, perhaps explicitly acknowledging their limitations, achieve comparable performance with significantly reduced computational overhead? Or are the robots destined to become ever-more-elaborate statistical engines, perpetually refining models of a reality they can never fully grasp? It’s a good question, and one this paper, while elegant, doesn’t quite answer.

Original article: https://arxiv.org/pdf/2512.08411.pdf

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