PhyRoGen: Synthetic Generation of Physical Robot Manipulation Puzzles Using Procedural Content Generation
Robot manipulation of physical puzzles is important for automatic assembly and disassembly tasks. However, to enable robots to solve physical puzzles, manipulation skills need to be learned, which requires large training datasets, the generation of which is often time consuming and tedious. To overcome this problem, we propose the Physical Robot Manipulation Puzzle Generation framework (PhyRoGen), which leverages procedural content generation (PCG) for automated generation of synthetic datasets of manipulation puzzles. PhyRoGen is a general-purpose puzzle generator, which can generate physical puzzles with interlocking object dependencies, where one articulated object must be manipulated before another can be moved. Based upon PhyRoGen, we define six concrete generators which we use to generate 24 physical puzzles. By using a benchmarking framework, we are able to solve all puzzles in 1 to 300 seconds using sampling-based planning algorithms. Finally, we demonstrate that every generated puzzle is manipulatable by using a KUKA LBR iiwa robot in a physical simulation. This shows that our framework is able to procedurally generate unique, solvable robot manipulation puzzles, which is a crucial ingredient to benchmark manipulation algorithms and to develop robust foundation models.
Code (0)
등록된 구현이 없습니다.
Tasks
Robot ManipulationSimilar Papers 제목 키워드 기반
Enabling Robust Cloth Manipulation via Inference-Time Simulator-in-the-Loop Refinement
Simulator-in-the-loop optimization offers a promising inference-time mechanism for robot manipulation. It uses a physical simulator as a backend rollout engine to evaluate candidate trajectories in parallel and refine no…
Robot ManipulationHyperSim: A Holistic Sim-To-Real Framework For Robust Robotic Manipulation
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acquisition, transferring robotic manipulatio…
Synthetic Data GenerationDeformSmith: Physics Harness-Guided Hierarchical Generation of Deformable Assets for Robot Manipulation
Creating deformable assets for robot manipulation requires jointly specifying their geometry, appearance, and physical properties. This is especially challenging for deformable objects, since text and images provide limi…
Robot ManipulationRobot Learning from a Physical World Model
We introduce PhysWorld, a framework that enables robot learning from video generation through physical world modeling. Recent video generation models can synthesize photorealistic visual demonstrations from language comm…
Reinforcement LearningVideo GenerationABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment
Video-based world models offer a powerful paradigm for embodied simulation and planning, yet state-of-the-art models often generate physically implausible manipulations - such as object penetration and anti-gravity motio…
Video Generation