Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State (2407.08052v1)

Abstract: Various robotic tool manipulation methods have been developed so far. However, to our knowledge, none of them have taken into account the fact that the grasping state such as grasping position and tool angle can change at any time during the tool manipulation. In addition, there are few studies that can handle deformable tools. In this study, we develop a method for estimating the position of a tool-tip, controlling the tool-tip, and handling online adaptation to changes in the relationship between the body and the tool, using a neural network including parametric bias. We demonstrate the effectiveness of our method for online change in grasping state and for deformable tools, in experiments using two different types of robots: axis-driven robot PR2 and tendon-driven robot MusashiLarm.

• The paper introduces the TBNPB method that integrates parametric bias to adapt robotic tool-tip control amid online grasping state changes.
• Experiments using both simulation and real robots (PR2, MusashiLarm) demonstrated significant reductions in tool-tip position estimation errors.
• The approach enables versatile tool-use by allowing robots to effectively handle both rigid and deformable tools in dynamic environments.

Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State

Overview

The paper "Adaptive Robotic Tool-Tip Control Learning Considering Online Changes in Grasping State" by Kento Kawaharazuka, Kei Okada, and Masayuki Inaba presents a novel approach for robotic tool manipulation that accounts for dynamic changes in the grasping state and the use of deformable tools. The paper introduces a methodology for estimating and controlling the position of a tool-tip using a neural network augmented with parametric bias. This network, referred to as the Tool-Body Network with Parametric Bias (TBNPB), can adapt to online variations in the relationship between the robot's body and the tool.

Methodology

The TBNPB is designed to model the static relationship between the control commands issued by the robot and the position of the tool-tip. By incorporating parametric bias, the network implicitly estimates the current grasping state, which can change over time due to external forces or during the manipulation process. The parametric bias serves as an additional parameter that allows the network to adapt to different grasping conditions without direct sensory input for each state.

Network Structure

The network is structured with seven layers. The input layer consists of the control command and the parametric bias. The hidden layers contain 300 units each, and the output layer provides the estimated tool-tip position. The network utilizes hyperbolic tangent as the activation function and is trained using the Adam optimization algorithm.

Training and Online Adaptation

The training process involves collecting data by varying the grasping conditions and control commands, generating a dataset that pairs these inputs with measured tool-tip positions. The network weights and parametric biases are updated using backpropagation. Subsequently, the parametric bias is fine-tuned online to adapt to changes in the grasping state during actual tool manipulation tasks. This online adaptation uses momentum SGD to update the parametric bias iteratively based on newly collected data points.

Experimental Validation

The effectiveness of the TBNPB was validated through experiments conducted with both simulated and real robots, specifically the PR2 robot and the musculoskeletal humanoid MusashiLarm.

PR2 Simulation

In the PR2 simulation, the network successfully adapted to different grasping states characterized by variations in the tool's length and angle. The results demonstrated that parametric biases self-organized in a manner consistent with the physical characteristics of the tool. Online updates of the parametric bias substantially reduced the tool-tip position estimation error, enhancing control accuracy.

Real-World PR2 Applications

Experiments with the actual PR2 robot using both rigid and flexible tools (a normal duster and an extended duster) confirmed the effectiveness of the TBNPB in real-world scenarios. The control errors decreased significantly when the parametric bias was updated online, demonstrating the network's ability to adapt to real-time changes in the grasping state and handle deformable tools.

MusashiLarm Experiment

The MusashiLarm, characterized by its flexible hand structure and non-rigid body, presented a more complex challenge. Despite these intricacies, the TBNPB reduced control errors substantially and updated the ambiguous grasping states effectively, showing the network's applicability to both rigid and flexible robotic systems.

Implications and Future Directions

This paper contributes a robust framework for robotic tool manipulation that can dynamically adapt to changes in grasping conditions and handle non-rigid tools. The approach introduces the use of parametric bias in static relationship modeling, a concept traditionally used in imitation learning for time-series data.

Practical Implications:

1. Enhanced Versatility in Tool Manipulation: The method allows robots to use a wider variety of tools, including deformable ones, thereby broadening the scope of tasks robots can perform autonomously.
2. Improved Accuracy: By updating grasping states online, the system maintains high accuracy in tool-tip position estimation and control, crucial for precise manipulation tasks.
3. Reduced Dependency on High-Fidelity Models: The neural network approach mitigates the need for accurate geometric models of the tools and grasping states, making the system more adaptable to real-world conditions where such models are often difficult to obtain.

Theoretical Implications:

1. Extension of Parametric Bias Utility: The successful integration of parametric bias in modeling static relationships suggests that this technique can be broadly applied to different domains requiring adaptive control.
2. Potential for Integrating Multiple Sensor Data: Future work could extend the network to incorporate additional sensor inputs, such as tactile or torque data, refining the system's adaptive capabilities further.

Future Developments in AI:

1. Tool-Use in Dynamic Environments: Extending this methodology to handle dynamic tool-use conditions, including real-time adjustments based on sensory feedback, is a logical next step.
2. Scalability with Diverse Tools: Addressing the challenge of scaling the method to accommodate a broader array of tools and grasping scenarios, potentially through simulation-based data generation, will be critical.

In conclusion, this research provides a significant step towards more autonomous and versatile robotic tool-use, with promising applications in both practical robotics and theoretical AI advancements. The TBNPB offers a framework for adaptive control in robotics that can effectively manage both rigid and flexible tools, paving the way for robots to interact with their environments in increasingly complex and nuanced ways.