Robots Learn by Imagining: A New Approach to Simulation

Author: Denis Avetisyan

Researchers are using generative AI to build more robust and adaptable robots by creating diverse and challenging simulation environments.

Given a robot’s demonstrated actions—be it a completed path or an overarching goal—a system can reconstruct plausible environments from which those actions originated, suggesting that behavior itself contains the seeds of its own context.

This work presents ReGen, a method for generating robot simulation scenarios via inverse design, leveraging large language models to condition environment creation on desired robot behaviors and improve corner case coverage.

Constructing realistic and diverse robot simulations remains a significant bottleneck in scaling robot learning, despite their crucial role in policy validation. This paper introduces ReGen: Generative Robot Simulation via Inverse Design, a framework that automates simulation creation through inverse design—inferring plausible environments based on desired robot behaviors. By leveraging large language models to reason about causal relationships, ReGen generates complex and controllable scenarios, enabling data augmentation and corner case testing. Could this approach unlock more robust and generalizable robot policies by bridging the reality gap in simulation?

The Erosion of Handcraft: Towards Generative Worlds

Traditional simulations demand extensive manual effort in constructing detailed environments, limiting scalability and fidelity. This reliance on handcrafted content bottlenecks the development and testing of complex systems, particularly where realism is paramount. Creating varied and unpredictable situations is often impractical, hindering thorough performance evaluation under edge cases.

Generative Simulation represents a paradigm shift, automating environment creation. Algorithms procedurally generate diverse scenarios, expanding the exploration of design spaces. By defining high-level parameters, complex, realistic environments emerge with minimal manual intervention, fostering rapid iteration and robust system evaluation.

ReGen demonstrates the capability to simulate complex scenarios representing internal actor states, such as a distracted driver's delayed reaction to a green light, and to model reasoning across multiple sensor types, including scenarios with GPS jamming and noisy GNSS measurements, while also enabling counterfactual analysis by altering event parameters, such as simulating a vehicle stopping with functioning versus broken brake lights. — ReGen demonstrates the capability to simulate complex scenarios representing internal actor states, such as a distracted driver’s delayed reaction to a green light, and to model reasoning across multiple sensor types, including scenarios with GPS jamming and noisy GNSS measurements, while also enabling counterfactual analysis by altering event parameters, such as simulating a vehicle stopping with functioning versus broken brake lights.

This automation is crucial for training AI systems to navigate unpredictable conditions. Exposing agents to a vast range of simulated scenarios – including rare and challenging events – creates robust, reliable systems. Generating scenarios on demand allows targeted training and validation, overcoming the limitations of real-world data collection.

Like a chronicle meticulously logged over time, these simulations offer not just snapshots, but the full unfolding of potential events—a testament to the enduring power of systems to reveal themselves through the passage of moments.

Shaping the Test: Inverse Design for Autonomous Systems

Inverse Design shifts simulation-based training by constructing environments conditioned on desired agent behaviors, rather than random generation. This approach overcomes limitations inherent in purely randomized environments, enabling the targeted creation of challenging, relevant scenarios.

ReGen can generate a diverse range of simulated driving environments for a given behavior, such as lane changing, encompassing scenarios like yielding to emergency vehicles, overtaking trucks, merging into open lanes, or avoiding obstacles, and can also model scenarios involving collision avoidance and responses to opening car doors.

Implementation frequently leverages existing physics engines and simulation platforms like CARLA Simulator and PyBullet. This facilitates translating abstract, behavior-conditioned designs into physically realistic, interactive simulations for testing and refining autonomous algorithms.

Applicable across robotic domains—autonomous driving, manipulation, navigation—specifying desired behaviors—successful lane changes, obstacle avoidance—automatically generates environments that demand and assess those capabilities, accelerating training and improving robustness.

LLM Guidance: Weaving Scenarios from Reasoning

LLM-Guided Graph Expansion utilizes a Large Language Model to iteratively refine a Directed Graph Representation of the simulation environment, automating the creation of complex scenarios by establishing relationships between entities and their properties. The LLM functions as a reasoning engine, suggesting and validating connections based on contextual understanding.

Performance evaluation indicates 0.98 ± 0.04 accuracy for event-to-event edge creation and 0.91 ± 0.02 for entity-to-event edge creation, demonstrating high precision in identifying causal relationships and linking relevant entities for realistic scenario generation.

A crucial component is the Low-Level State Translator, converting abstract reasoning within the graph into concrete physical state transitions managed by a Finite State Machine (FSM). This translation enables a closed-loop system for automated scenario creation and execution.

Constraints as Structure: Validating the Simulated World

The simulation’s foundation relies on an Asset Database, providing core components—objects and their properties—for constructing diverse, complex scenarios. This database facilitates modularity, streamlining the creation process.

A Constraint Programming Satisfiability (CP-SAT) Solver plays a critical role in resolving constraints, ensuring realistic, physically plausible interactions by verifying consistent adherence to defined rules and limitations throughout the generated scenarios.

This solver is essential for validating scenarios and guaranteeing feasibility before deployment, minimizing runtime errors and maintaining simulation integrity.

Adversarial Stress Tests: Pushing Systems to the Limit

Adversarial Policies represent a significant advancement in rigorous testing, generating Safety-Critical Scenarios to push systems to their operational limits within simulated environments. This proactive approach identifies and exploits potential failure points before deployment.

The approach demonstrates improved performance characteristics compared to existing tools, with an 18.50% increase in task diversity compared to ChatScene and DriveLM, stemming from a focus on generating scenarios with greater variability and complexity.

Compared to ChatScene, ReGen generates a more diverse set of corner cases through its ability to reason about varying causal factors.

Effective simulation requires processing rich sensory information. Multimodal Foundation Models are therefore crucial, training agents on Vision-Language-Action data to interpret multimodal inputs—integrating visual, linguistic, and action-based data—and react appropriately to complex, realistic conditions.

Like the slow wearing of stone by water, these systems reveal their true resilience—or fragility—only when subjected to the constant, subtle pressures of simulated time.

The pursuit of robust robotic systems, as detailed in this work on generative simulation, inherently acknowledges the inevitable march of entropy. The paper’s focus on generating diverse scenarios, particularly corner cases, through inverse design, isn’t simply about creating challenging tests—it’s about anticipating the unpredictable ways in which a system will age. As Barbara Liskov noted, “It’s one thing to program a computer; it’s another thing to design a system that will still work correctly in five years.” This sentiment echoes the core of ReGen; a system designed to learn from a wider range of simulated experiences will naturally possess a greater capacity to adapt and maintain functionality over time, effectively aging more gracefully within the complex landscape of robotic interaction.

What’s Next?

The pursuit of generative simulation, as exemplified by this work, isn’t about creating perfect replicas of reality. It’s about accelerating the inevitable process of discovery—uncovering failure modes not through exhaustive testing, but through directed generation. The system presented here shifts the burden from finding the needle in the haystack to crafting haystacks likely to contain needles. However, the fidelity of this crafted reality remains tethered to the LLM’s understanding – a probabilistic echo of the physical world, not the world itself. The generated ‘diversity’ is, at present, a measure of linguistic variation, and the link between linguistic nuance and true robustness remains largely unquantified.

The inherent limitation lies not in the inverse design methodology, but in the fundamental unpredictability of complex systems. Increased simulation diversity simply expands the scope of potential failures brought to light; it does not eliminate the possibility of unforeseen interactions. A system may appear stable across a multitude of generated scenarios, yet succumb to a previously unconsidered perturbation. Sometimes stability is just a delay of disaster.

Future work will inevitably focus on closing the gap between simulated and physical reality, perhaps through techniques that incorporate real-world data to refine the LLM’s generative priors. But the underlying truth remains: systems age not because of errors, but because time is inevitable. The question isn’t whether robots will fail, but how – and generative simulation offers a means to explore that ‘how’ more efficiently, if not definitively.

Original article: https://arxiv.org/pdf/2511.04769.pdf

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