Knot So Simple: A Minimalistic Environment for Spatial Reasoning
This provides a domain-specific environment for testing AI in complex spatial tasks, but it is incremental as it builds on existing simulation frameworks.
The authors tackled the problem of spatial reasoning and manipulation by proposing KnotGym, an interactive environment for rope manipulation tasks with varying complexity based on knot crossings, and they evaluated methods like model-based RL and chain-of-thought reasoning to illustrate the challenges it presents.
We propose KnotGym, an interactive environment for complex, spatial reasoning and manipulation. KnotGym includes goal-oriented rope manipulation tasks with varying levels of complexity, all requiring acting from pure image observations. Tasks are defined along a clear and quantifiable axis of complexity based on the number of knot crossings, creating a natural generalization test. KnotGym has a simple observation space, allowing for scalable development, yet it highlights core challenges in integrating acute perception, spatial reasoning, and grounded manipulation. We evaluate methods of different classes, including model-based RL, model-predictive control, and chain-of-thought reasoning, and illustrate the challenges KnotGym presents. KnotGym is available at https://github.com/lil-lab/knotgym.