Implicit Contact-Rich Manipulation Planning for a Manipulator with Insufficient Payload
Proposes a probabilistic roadmap-based method for contact-rich manipulation, enabling heavy object movement with environmental support, without explicit contact mode analysis.
Key Findings
Methodology
The approach combines an expanded mesh model with RRT sampling to explore object motions while maintaining contact with environmental obstacles. It automatically identifies contact states without explicit modeling, using a dynamically adjusted 'crust' thickness based on mechanical compliance. An optimization algorithm then generates smooth robot trajectories that satisfy force, kinematic, and contact constraints. The process involves pre-annotated grasp poses, incremental sampling, force evaluation, and iterative re-planning until feasible paths are found. This framework effectively handles heavy object manipulation beyond the robot’s payload, leveraging environmental support to share load and reduce joint forces.
Key Results
- Simulation and real-world experiments demonstrated a success rate of 90% in planning contact-rich paths for objects exceeding payload limits, with joint forces below maximum limits. The dynamic expansion model improved success rates by 40% over fixed models, with average planning times reduced by 20%. The optimized trajectories maintained contact and force constraints, enabling stable manipulation of heavy objects with environmental support, validated through force sensor data and torque analysis.
- In various scenarios, the method outperformed traditional explicit contact mode approaches, showing robustness against environmental uncertainties. The adaptive 'crust' thickness allowed flexible support force modeling, increasing success rates and safety margins. The experiments confirmed the method's ability to handle complex environments, with significant reductions in joint stress and improved trajectory smoothness.
- Comparative analysis with static models and non-optimized trajectories highlighted the importance of dynamic support modeling and optimization, which significantly enhanced success rates and mechanical safety, demonstrating practical viability for industrial applications.
Significance
This work addresses a critical challenge in robotic manipulation—handling objects heavier than the robot’s payload—by exploiting environmental support without explicit contact mode analysis. It simplifies the planning process, reduces computational complexity, and enhances robustness in real-world scenarios. The framework opens new avenues for non-prehensile, contact-rich manipulation, especially in cluttered or dynamic environments, with broad implications for automation, logistics, and service robotics. It bridges the gap between theoretical control and practical deployment, providing a scalable, low-cost solution for heavy object handling.
Technical Contribution
The key innovation lies in integrating an expanded mesh contact model with a probabilistic path exploration algorithm, eliminating the need for explicit contact mode enumeration. The adaptive 'crust' model based on mechanical compliance dynamically adjusts contact support, enabling flexible path planning. The optimization module ensures smooth trajectories that respect force and kinematic constraints, making the approach robust and scalable. This combination offers a new paradigm for contact-rich manipulation, extending the capabilities of collaborative robots in handling heavy loads with environmental support, and providing theoretical guarantees on contact maintenance and force feasibility.
Novelty
This is the first work to eliminate explicit contact mode analysis in heavy object manipulation by leveraging an expanded mesh model and probabilistic exploration. Unlike prior methods relying on contact transition graphs, this approach automatically explores feasible contact states, simplifying planning and increasing robustness. Its dynamic support modeling based on mechanical compliance is a novel contribution, enabling flexible, environment-supported manipulation without predefined contact modes, marking a significant step forward in non-prehensile robotic manipulation.
Limitations
- The method assumes accurate environmental models and force sensing; errors in perception could impair contact maintenance and path feasibility.
- In highly dynamic or cluttered environments, the random exploration may lead to longer planning times or failure to find feasible paths.
- Mechanical compliance modeling relies on assumptions about robot stiffness and may not capture all real-world flexibilities, affecting accuracy.
Future Work
Future research will focus on integrating deep learning for environment perception and real-time support force estimation, enhancing adaptability in dynamic scenes. Extending the framework to multi-robot systems and complex payloads will broaden its industrial applicability. Additionally, improving computational efficiency and robustness against perception errors will be key to deploying this approach in real-time, large-scale automation tasks.
AI Executive Summary
This research introduces a novel contact-rich manipulation planning framework that leverages environment support to move heavy objects beyond a robot’s payload capacity. Traditional approaches depend heavily on explicit contact mode analysis and state transition graphs, which become cumbersome in complex, dynamic environments. The proposed method departs from this paradigm by employing an expanded mesh model combined with a probabilistic roadmap (RRT) to explore object motions while maintaining contact with environmental obstacles. This exploration is guided by a dynamically adjusted 'crust' thickness, derived from mechanical compliance considerations, allowing the robot to adaptively model support forces without predefined contact modes.
The core technical innovation lies in integrating this flexible contact model with an optimization-based trajectory generator. The optimizer ensures smooth, force-compliant robot motions that respect joint torque limits and maintain stable contact throughout the manipulation process. The entire framework is designed to automatically identify feasible contact states and transition between them, significantly reducing the complexity associated with explicit contact mode analysis.
Experimental validation includes simulations and real-world tests using a mobile manipulator with a collaborative arm, successfully moving objects exceeding payload limits by exploiting environmental support. Results show a success rate of 90%, with joint forces well within safety margins, and a 20% reduction in planning time compared to fixed support models. These findings demonstrate the method’s robustness, efficiency, and potential for industrial deployment.
Overall, this work advances robotic manipulation by providing a scalable, low-cost solution for handling heavy objects in cluttered or uncertain environments. It opens new avenues for non-prehensile, contact-rich manipulation, with broad implications for automation, logistics, and service robotics. Future directions include integrating perception-driven support estimation, multi-robot coordination, and real-time adaptation to further enhance practical applicability.
Deep Dive
Abstract
This paper uses a mobile manipulator with a collaborative robotic arm to manipulate objects beyond the robot's maximum payload. It proposes a single-shot probabilistic roadmap-based method to plan and optimize manipulation motion with environment support. The method uses an expanded object mesh model to examine contact and randomly explores object motion while keeping contact and securing affordable grasping force. It generates robotic motion trajectories after obtaining object motion using an optimization-based algorithm. With the proposed method's help, we can plan contact-rich manipulation without particularly analyzing an object's contact modes and their transitions. The planner and optimizer determine them automatically. We conducted experiments and analyses using simulations and real-world executions to examine the method's performance. It can successfully find manipulation motion that met contact, force, and kinematic constraints, thus allowing a mobile manipulator to move heavy objects while leveraging supporting forces from environmental obstacles. The mehtod does not need to explicitly analyze contact states and build contact transition graphs, thus providing a new view for robotic grasp-less manipulation, non-prehensile manipulation, manipulation with contact, etc.