Evaluating VLMs' Spatial Reasoning Over Robot Motion: A Step Towards Robot Planning with Motion Preferences

We present CoPlanVLM, which uses a Vision Language Model (VLM) to translate a single natural-language instruction and image into coordinated paths for a team of two robots. While VLMs offer a flexible, natural-language interface for commanding robot teams, their weak spatial reasoning has greatly limited VLM-based path planning. CoPlanVLM improves the VLM's spatial reasoning and path planning abilities through three components: (1) an image overlay marking both free space and obstacles, (2) task classification that selects the most appropriate low-level planner and reasoning prompt for each instruction, and (3) task-specific chain-of-thought reasoning that prompts the VLM to identify key objects, allocate roles, and verify routes are collision-free before committing to a plan. Together, these components enable the team to complete complex coordinated maneuvers requiring both robots and divide large tasks between robots to increase efficiency. CoPlanVLM also supports detailed maneuvers involving specific routes, loops, and coverage of large areas, as well as tasks requiring directional reasoning such as north/south or left/right. We validate CoPlanVLM in both Gazebo simulation and on physical Turtlebot 4 hardware. CoPlanVLM attains an average success rate of 84.4% compared to 50.0% for the closest baseline. On trials where both methods succeed, CoPlanVLM's paths are 27% shorter on average. Project code is available at https://github.com/AICPS/CoPlanVLM.

Long-horizon robot planning requires jointly reasoning over semantic task structure and geometric feasibility. To successfully execute a task, a robot must decompose goals, select task-relevant objects, and sequence actions, while ensuring that plans satisfy spatial constraints such as limited free space and object collisions. In this work, we propose APIVOT, a VLM-based planner that adaptively interleaves language and visual thoughts for long-horizon planning. APIVOT learns to leverage language for semantic reasoning, while using visual thoughts as imagined future states for internal verification of geometric feasibility. On long-horizon kitchen tasks, APIVOT outperforms general-purpose VLMs and prior planning frameworks, achieving the largest gains in spatially constrained settings. We find that APIVOT learns meaningful modality selection behavior, demonstrating that adaptive interleaving of vision-language thoughts improves both planning success and reasoning efficiency.

Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using them as high-level planners for embodied AI. However, physical tasks require models to integrate spatial information, reason over temporal states, and generate multi-step plans under various constraints, raising the question of whether current VLMs can reliably support such reasoning and planning independently of execution. We introduce Plan-Bench, a benchmark for evaluating the high-level reasoning and multi-step planning capabilities of VLMs relevant to embodied worlds, while isolating the influence of action execution. It covers three complementary dimensions, spanning 16 subclasses and 699 curated samples. Extensive evaluation of 28 VLMs reveals room for improvement, with even the strongest model achieving only 75.0% accuracy. We further find that model scaling generally improves these capabilities. Ablation studies also indicate larger models show increasing potential as high-level planners. Overall, Plan-Bench provides a lightweight probe for evaluating VLMs' reasoning and planning capabilities relevant to embodied environments and assessing their potential as decision makers for physical tasks. Our benchmark and code are available at https://github.com/MinneFeng/Plan-Bench.