IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models
This addresses the challenge of enabling robots to operate effectively in cluttered spaces by allowing safe contact, which is incremental as it builds on existing motion planning methods with new semantic insights.
The paper tackles the problem of motion planning in cluttered environments where contact is unavoidable, by using Vision-Language Models to infer acceptable contact based on object properties, resulting in improved task success rates and reduced object displacements in simulation and real-world trials.
Motion planning involves determining a sequence of robot configurations to reach a desired pose, subject to movement and safety constraints. Traditional motion planning finds collision-free paths, but this is overly restrictive in clutter, where it may not be possible for a robot to accomplish a task without contact. In addition, contacts range from relatively benign (e.g. brushing a soft pillow) to more dangerous (e.g. toppling a glass vase), making it difficult to characterize which may be acceptable. In this paper, we propose IMPACT, a novel motion planning framework that uses Vision-Language Models (VLMs) to infer environment semantics, identifying which parts of the environment can best tolerate contact based on object properties and locations. Our approach generates an anisotropic cost map that encodes directional push safety. We pair this map with a contact-aware A* planner to find stable contact-rich paths. We perform experiments using 20 simulation and 10 real-world scenes and assess using task success rate, object displacements, and feedback from human evaluators. Our results over 3200 simulation and 200 real-world trials suggest that IMPACT enables efficient contact-rich motion planning in cluttered settings while outperforming alternative methods and ablations. Our project website is available at https://impact-planning.github.io/.