Robotic In-Hand Manipulation With Adaptive Grasp Transition Planning
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Solution Overview
Problem
Current technologies face challenges in efficiently planning and controlling in-hand manipulation tasks, particularly in transitioning grasps and applying forces to objects using robotic hands, as they often require explicit final grasps and are not data-efficient in adapting to disturbances.
Innovation Solution
A hybrid model and learning-based planning and control system that includes an object path planner, trajectory optimizer, and grasp sequence planner, utilizing Probabilistic Roadmap and deep reinforcement learning to optimize grasp transitions and force application, allowing for real-time adaptation and efficient data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional planning methods are used for in-hand manipulation, then the system requires explicit final grasps and detailed pre-planning, but this increases device complexity and reduces adaptability to disturbances
Solution Approach 1:
The planning system is segmented into two distinct components: a traditional planner that generates an initial trajectory and grasp sequence, and a deep reinforcement learning (DRL) policy that handles real-time adjustments. This segmentation allows each component to specialize - the traditional planner handles deterministic planning while the DRL policy handles adaptive response to disturbances, resolving the contradiction between reliability and complexity.
Solution Approach 2:
The DRL policy acts as an intermediary between the traditional planner and the robotic hand execution. It receives the pre-planned trajectory and grasp sequence, then dynamically adjusts the grasp forces and trajectory in real-time based on actual sensor feedback. This intermediary layer provides adaptability to disturbances without requiring the entire planning system to be complex.
2Productivity
If traditional planning methods are used, then complete pre-planning is required, but this increases loss of time and reduces productivity
Solution Approach 1:
The system performs preliminary action through the traditional planner that generates an initial trajectory and grasp sequence before execution. However, this pre-planning is incomplete by design - it provides a baseline plan that can be quickly adjusted by the DRL policy during execution, reducing the time investment required for complete pre-planning while maintaining productivity.
Solution Approach 2:
The system transitions from static pre-planning to dynamic adaptive planning. The DRL policy continuously updates the grasp sequence and trajectory based on real-time sensor feedback, allowing the system to adapt to disturbances without requiring time-consuming re-planning. This dynamic approach improves productivity by enabling faster response to unexpected conditions.
3Ease of operation
If detailed grasp sequences are pre-planned, then the system can follow a structured approach, but this reduces adaptability and increases device complexity
Solution Approach 1:
The system implements feedback through the DRL policy that continuously monitors sensor data during execution and adjusts the grasp sequence and trajectory accordingly. This feedback loop maintains ease of operation by following the structured pre-planned approach while simultaneously improving adaptability by making real-time adjustments based on actual sensor feedback, resolving the contradiction between structured operation and adaptability.
Data Source
AI summary
A robot for object manipulation may include sensors, a robot appendage, actuators configured to drive joints of the robot appendage, a planner, and a controller. Object path planning may include determining poses. Object trajectory optimization may include assigning a set of timestamps to the poses, optimizing a cost function based on an inverse kinematic (IK) error, a difference between an estimated required wrench and an actual wrench, and a grasp efficiency, and generating a reference object trajectory based on the optimized cost function. Grasp sequence planning may be model-based or deep reinforcement learning (DRL) policy based. The controller may implement the reference object trajectory and the grasp sequence via the robot appendage and actuators.