Simulation plays a key role in scaling robot learning and validating policies, but constructing simulations remains labor-intensive. In this paper, we introduce ReGen, a generative simulation framework that automates this process using inverse design. Given an agent's behavior (such as a motion trajectory or objective function) and its textual description, we infer the underlying scenarios and environments that could have caused the behavior. Our approach leverages large language models to construct and expand a graph that captures cause-and-effect relationships and relevant entities with properties in the environment, which is then processed to configure a robot simulation environment. Our approach supports (i) augmenting simulations based on ego-agent behaviors, (ii) controllable, counterfactual scenario generation, (iii) reasoning about agent cognition and mental states, and (iv) reasoning with distinct sensing modalities, such as braking due to faulty GPS signals. We demonstrate our method in autonomous driving and robot manipulation tasks, generating more diverse, complex simulated environments compared to existing simulations with high success rates, and enabling controllable generation for corner cases. This approach enhances the validation of robot policies and supports data or simulation augmentation, advancing scalable robot learning for improved generalization and robustness.

The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out-of-distribution scenarios, and comprehensive safety validation to ensure that these systems can navigate safely and effectively under unpredictable conditions. Addressing Out-OfDistribution (OOD) driving scenarios is essential for enhancing safety, as OOD scenarios help validate the reliability of the models within the vehicle's autonomy stack. However, generating OOD scenarios is challenging due to their long-tailed distribution and rarity in urban driving datasets. Recently, Large Language Models (LLMs) have shown promise in autonomous driving, particularly for their zero-shot generalization and common-sense reasoning capabilities. In this paper, we leverage these LLM strengths to introduce a framework for generating diverse OOD driving scenarios. Our approach uses LLMs to construct a branching tree, where each branch represents a unique OOD scenario. These scenarios are then simulated in the CARLA simulator using an automated framework that aligns scene augmentation with the corresponding textual descriptions. We evaluate our framework through extensive simulations, and assess its performance via a diversity metric that measures the richness of the scenarios. Additionally, we introduce a new “OOD-ness” metric, which quantifies how much the generated scenarios deviate from typical urban driving conditions. Furthermore, we explore the capacity of modern Vision-Language Models (VLMs) to interpret and safely navigate through the simulated OOD scenarios. Our findings offer valuable insights into the reliability of language models in addressing OOD scenarios within the context of urban driving.

Several tasks in program analysis, verification, and testing are modeled as constraint solving problems, utilizing SMT solvers as the reasoning engine. In this work, we aim to investigate the reasoning capabilities of large language models (LLMs) toward reducing the size of an infeasible string constraint system by exploiting inter-constraint interactions such that the remaining ones are still unsatisfiable. We term this safe minimization. Motivated by preliminary observations of hallucination and error propagation in LLMs, we design SafeMin, a framework leveraging an LLM and SMT solver in tandem to ensure a safe and correct minimization. We test the applicability of our approach on string benchmarks from LeetCode in the computation of minimal unsatisfiable subsets (MUSes). We observed that SafeMin helps safely minimize 94.3% of these constraints, with an average minimization ratio of 98% relative to the MUSes. In addition, we assess SafeMin's capabilities in partially enumerating non-unique MUSes, which is baked into our approach via a “sample-and-enumerate” decoding strategy. Overall, we captured 42.1% more non-unique MUSes than without such LLM-based macro-reasoning. Finally, we demonstrate SafeMin's usefulness in detecting infeasible paths in programs.

Generating varied scenarios through simulation is crucial for training and evaluating safety-critical systems, such as autonomous vehicles. Yet, the task of modeling the trajectories of other vehicles to simulate diverse and meaningful close interactions remains prohibitively costly. Adopting language descriptions to generate driving behaviors emerges as a promising strategy, offering a scalable and intuitive method for human operators to simulate a wide range of driving interactions. However, the scarcity of large-scale annotated language-trajectory data makes this approach challenging. To address this gap, we propose Text-to-Drive (T2D) to synthesize diverse driving behaviors via Large Language Models (LLMs). We introduce a knowledge-driven approach that operates in two stages. In the first stage, we employ the embedded knowledge of LLMs to generate diverse language descriptions of driving behaviors for a scene. Then, we leverage LLM’s reasoning capabilities to synthesize these behaviors in simulation. At its core, T2D employs an LLM to construct a state chart that maps low-level states to high-level abstractions. This strategy aids in downstream tasks such as summarizing low-level observations, assessing policy alignment with behavior description, and shaping the auxiliary reward, all without needing human supervision. With our knowledge-driven approach, we demonstrate that T2D generates more diverse trajectories compared to other baselines and offers a natural language interface that allows for interactive incorporation of human preference. Please check our website for more examples: here

We present an approach to estimating camera rotation in crowded, real-world scenes from handheld monocular video. While camera rotation estimation is a well-studied problem, no previous methods exhibit both high accuracy and acceptable speed in this setting. Because the setting is not addressed well by other datasets, we provide a new dataset and benchmark, with high-accuracy, rigorously verified ground truth, on 17 video sequences. Methods developed for wide baseline stereo (e.g., 5-point methods) perform poorly on monocular video. On the other hand, methods used in autonomous driving (e.g., SLAM) leverage specific sensor setups, specific motion models, or local optimization strategies (lagging batch processing) and do not generalize well to handheld video. Finally, for dynamic scenes, commonly used robustification techniques like RANSAC require large numbers of iterations, and become prohibitively slow. We introduce a novel generalization of the Hough transform on SO(3) to efficiently and robustly find the camera rotation most compatible with optical flow. Among comparably fast methods, ours reduces error by almost 50% over the next best, and is more accurate than any method, irrespective of speed. This represents a strong new performance point for crowded scenes, an important setting for computer vision. The code and the dataset are available at https://fabiendelattre.com/robustrotation-estimation.

Redirected walking techniques have the potential to provide natural locomotion while users experience large virtual environments. However, when using redirected walking in small physical workspaces, disruptive overt resets are often required. We describe the design of an educational virtual reality experience in which users physically walk through virtual tunnels representative of the World War I battle of Vauquois. Walking in only a 15- by 5-foot tracked space, users are redirected through subtle, narrative-driven resets to walk through a tunnel nearly 50 feet in length. This work contributes approaches and lessons that can be used to provide a seamless and natural virtual reality walking experience in highly constrained physical spaces.