World-building, the process of developing both the narrative and physical world of a game, plays a vital role in the game’s experience. Critically-acclaimed independent and AAA video games are praised for strong world-building, with game maps that masterfully intertwine with and elevate the narrative, captivating players and leaving a lasting impression. However, designing game maps that support a desired narrative is challenging, as it requires satisfying complex constraints from various considerations. Most existing map generation methods focus on considerations about gameplay mechanics or map topography, while the need to support the story is typically neglected. As a result, extensive manual adjustment is still required to design a game world that facilitates particular stories. In this work, we approach this problem by introducing an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline.Concretely, we define (plot) facility layout tasks as the tasks of assigning concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story. We present two methods for solving these tasks automatically: an evolutionary computation based approach through Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and a Reinforcement Learning (RL) based approach. We develop a method of generating datasets of facility layout tasks, create a gym-like environment for experimenting with and evaluating different methods, and further analyze the two methods with comprehensive experiments, aiming to provide insights for solving facility layout tasks.

The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.

Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the programs to form a 2D layout on each floor. Though 2D layout generation for a single story has been widely studied, there is no developed method for multi-story buildings. This paper focuses on volumetric design generation conditioned on an input program graph. Instead of outputting dense 3D voxels, we propose a new 3D representation named voxel graph that is both compact and expressive for building geometries. Our generator is a cross-modal graph neural network that uses a pointer mechanism to connect the input program graph and the output voxel graph, and the whole pipeline is trained using the adversarial framework. The generated designs are evaluated qualitatively by a user study and quantitatively using three metrics: quality, diversity, and connectivity accuracy. We show that our model generates realistic 3D volumetric designs and outperforms previous methods and baselines.

In this work we propose a learning approach to high-precision robotic assembly problems. We focus on the contact-rich phase, where the assembly pieces are in close contact with each other. Unlike many learning-based approaches that heavily rely on vision or spatial tracking, our approach takes force/torque in task space as the only observation. Our training environment is robotless, as the end-effector is not attached to any specific robot. Trained policies can then be applied to different robotic arms without re-training. This approach can greatly reduce complexity to perform contact-rich robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which extends Ape-X DDPG[1] with recurrency and makes two structural improvements on prioritized experience replay[2]. Our results show that RD2 is able to solve two fundamental high-precision assembly tasks, lap-joint and peg-in-hole, and outperforms two state-of-the-art algorithms, Ape-X DDPG and PPO with LSTM. We have successfully evaluated our robot-agnostic policies on three robotic arms, Kuka KR60, Franka Panda, and UR10, in simulation. The video presenting our experiments is available at https://sites.google.com/view/rd2-rl

Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despite the pervasiveness of parametric CAD and a growing interest from the research community, currently there does not exist a dataset of realistic CAD models in a concise programmatic form. In this paper we present the Fusion 360 Gallery , consisting of a simple language with just the sketch and extrude modeling operations, and a dataset of 8,625 human design sequences expressed in this language. We also present an interactive environment called the Fusion 360 Gym , which exposes the sequential construction of a CAD program as a Markov decision process, making it amendable to machine learning approaches. As a use case for our dataset and environment, we define the CAD reconstruction task of recovering a CAD program from a target geometry. We report results of applying state-of-the-art methods of program synthesis with neurally guided search on this task.

Automatic assembly has broad applications in industries. Traditional assembly tasks utilize predefined trajectories or tuned force control parameters, which make the automatic assembly time-consuming, difficult to generalize, and not robust to uncertainties. In this paper, we propose a learning framework for high precision industrial assembly. The framework combines both the supervised learning and the reinforcement learning. The supervised learning utilizes trajectory optimization to provide the initial guidance to the policy, while the reinforcement learning utilizes actor-critic algorithm to establish the evaluation system even the supervisor is not accurate. The proposed learning framework is more efficient compared with the reinforcement learning and achieves better stability performance than the supervised learning. The effectiveness of the method is verified by both the simulation and experiment. Experimental videos are available at [1].

In this paper, we propose a method to generate a simulated physical virtual environment in which a collection of images projected on a large screen are continuously moved around in response to spectators' movements and actions captured by a sensing device. The innovative feature of the project is the application of forces generated by a fluid physical model to spatially reposition each image in the virtual environment according to their position in relation to the source of the sensed forces.