IMPACT: Intelligent Motion Planning with Acceptable Contact Trajectories via Vision-Language Models
IMPACT framework uses Vision-Language Models for contact-rich motion planning, achieving a 73.75% success rate.
Key Findings
Methodology
IMPACT framework integrates Vision-Language Models with a contact-aware A* algorithm to generate anisotropic cost maps for contact-rich path planning. It employs GPT-4o to infer environmental semantics and identify contact-tolerant object parts. Experiments were conducted in 20 simulation and 10 real-world scenes, assessing task success rate, object displacement, and human feedback.
Key Results
- IMPACT demonstrated superior performance in 3200 simulation and 200 real-world trials, achieving a 73.75% success rate, significantly outperforming other methods.
- Compared to traditional collision-free planning, IMPACT effectively plans contact paths in complex environments, reducing path costs.
- Human evaluations indicate that IMPACT-generated paths align better with semantic acceptability, showing higher user preference.
Significance
The IMPACT framework significantly enhances robot motion planning capabilities in complex environments, addressing limitations of traditional collision-free path planning in densely cluttered settings. By intelligently identifying contact-tolerant objects, IMPACT opens new possibilities for robotic applications in real-world scenarios.
Technical Contribution
IMPACT leverages Vision-Language Models to infer environmental semantics, generating anisotropic cost maps combined with a contact-aware A* algorithm for contact-rich path planning. Unlike existing methods, IMPACT automates the identification of contact-tolerant objects, reducing manual intervention.
Novelty
IMPACT is the first to combine Vision-Language Models and contact-aware algorithms for semantically acceptable contact path planning. Compared to related work, IMPACT automates the identification of contact-tolerant objects, minimizing human input.
Limitations
- IMPACT may struggle to accurately identify all contact-tolerant objects in extremely complex environments, affecting path planning effectiveness.
- The inference of environmental semantics by Vision-Language Models may be limited by the diversity of training data.
Future Work
Future research could explore IMPACT's application in more complex environments, enhance Vision-Language Models' semantic inference capabilities, and optimize contact-aware algorithms to improve path planning efficiency.
AI Executive Summary
Traditional robot motion planning emphasizes collision-free paths, which can be restrictive in densely cluttered environments. The IMPACT framework uses Vision-Language Models to infer environmental semantics, identifying contact-tolerant object parts and generating anisotropic cost maps for contact-rich path planning. Core technologies include GPT-4o for semantic cost inference and a contact-aware A* algorithm. Experimental results show IMPACT's superior performance in 3200 simulation and 200 real-world trials, achieving a 73.75% success rate. This framework opens new possibilities for robotic applications in complex environments, though challenges remain in extremely cluttered settings. Future research could further optimize Vision-Language Models and contact-aware algorithms.
Deep Analysis
Background
The field of robot motion planning has long focused on finding collision-free paths to ensure task safety and effectiveness. However, in densely cluttered environments, this approach can be overly restrictive, leading to task failure. Existing research, such as Navigation Among Movable Obstacles and Minimum Constraint Removal, attempts to relocate obstacles to obtain feasible paths but still faces limitations.
Core Problem
Traditional motion planning struggles to achieve collision-free paths in densely cluttered environments, leading to task failure. Robots need to engage in appropriate contact in complex environments to improve task efficiency and success rates. Identifying which contacts are acceptable is a core challenge in this field.
Innovation
The IMPACT framework uses Vision-Language Models to infer environmental semantics, identifying contact-tolerant object parts and generating anisotropic cost maps for contact-rich path planning. Unlike existing methods, IMPACT automates the identification of contact-tolerant objects, reducing manual intervention and improving planning efficiency.
Methodology
- �� Use GPT-4o to infer environmental semantics and generate object semantic costs.
- �� Construct anisotropic cost maps encoding directional push safety.
- �� Combine with a contact-aware A* algorithm to find stable contact-rich paths.
- �� Conduct experiments in simulation and real-world settings, evaluating task success rate and human feedback.
Experiments
Experiments were conducted in 20 simulation and 10 real-world scenes using the PyBullet simulator. Scenes were constructed using the YCB and TRELLIS datasets, evaluating IMPACT's performance against other methods. Multiple quantitative metrics and human preference rankings were used for evaluation.
Results
IMPACT demonstrated superior performance in 3200 simulation and 200 real-world trials, achieving a 73.75% success rate. Compared to traditional collision-free planning, IMPACT effectively plans contact paths in complex environments, reducing path costs. Human evaluations indicate that IMPACT-generated paths align better with semantic acceptability.
Applications
IMPACT can be applied to robotic task planning in complex environments, such as home cleaning and industrial assembly. Its ability to automatically identify contact-tolerant objects makes it highly applicable in real-world scenarios.
Limitations & Outlook
IMPACT may struggle to accurately identify all contact-tolerant objects in extremely complex environments, affecting path planning effectiveness. The inference of environmental semantics by Vision-Language Models may be limited by the diversity of training data. Future research could explore enhancing model semantic inference capabilities.
Plain Language Accessible to non-experts
Imagine a kitchen with many items, and a robot needs to reach a specific spice jar. Traditional methods require the robot to avoid all obstacles, but in a crowded kitchen, this can be difficult. IMPACT acts like a smart assistant, knowing which items can be lightly touched, like a soft teddy bear, and which items need to be carefully avoided, like a fragile glass. This way, the robot can reach the spice jar faster without having to go around all obstacles.
ELI14 Explained like you're 14
Imagine you're playing a game where the task is to get a treasure from a messy room. Traditional methods say you have to avoid all obstacles, but sometimes that's too hard! IMPACT is like a smart game assistant that knows which things you can touch, like a soft teddy bear, and which things you can't, like a fragile glass. This way, you can get the treasure faster without having to go around everything. Isn't that cool?
Glossary
Vision-Language Model
A model that combines visual and language information to infer environmental semantics.
IMPACT uses GPT-4o as a Vision-Language Model to identify contact-tolerant objects.
Anisotropic Cost Map
A map encoding directional push safety for contact-rich path planning.
IMPACT generates anisotropic cost maps to achieve stable contact paths.
Contact-Aware A* Planner
A path planning algorithm incorporating contact information to find stable contact-rich paths.
IMPACT uses a contact-aware A* planner for path planning.
YCB Dataset
A standard dataset containing various objects for robotic research.
IMPACT uses the YCB dataset to construct scenes for experiments.
TRELLIS
A tool for generating 3D models of fragile objects.
IMPACT uses TRELLIS to generate fragile object models for experiments.
Open Questions Unanswered questions from this research
- 1 How to accurately identify all contact-tolerant objects in extremely complex environments? Current methods face limitations in data diversity.
- 2 How can Vision-Language Models improve semantic inference capabilities? Broader training data is needed.
Applications
Immediate Applications
Home Cleaning Robots
IMPACT can be used for home cleaning tasks, identifying contact-tolerant objects to improve cleaning efficiency.
Long-term Vision
Industrial Assembly Robots
IMPACT can be used for industrial assembly tasks, automatically identifying contact-tolerant components to improve assembly efficiency.
Abstract
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/.