DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots (2405.19232v1)

Abstract: We present DiPPeST, a novel image and goal conditioned diffusion-based trajectory generator for quadrupedal robot path planning. DiPPeST is a zero-shot adaptation of our previously introduced diffusion-based 2D global trajectory generator (DiPPeR). The introduced system incorporates a novel strategy for local real-time path refinements, that is reactive to camera input, without requiring any further training, image processing, or environment interpretation techniques. DiPPeST achieves 92% success rate in obstacle avoidance for nominal environments and an average of 88% success rate when tested in environments that are up to 3.5 times more complex in pixel variation than DiPPeR. A visual-servoing framework is developed to allow for real-world execution, tested on the quadruped robot, achieving 80% success rate in different environments and showcasing improved behavior than complex state-of-the-art local planners, in narrow environments.

• The paper introduces DiPPeST, a diffusion-based planner that fuses global trajectories with real-time local refinements from RGB input without retraining.
• It innovatively integrates a global planner based on DiPPeR with a local ROI-driven method for dynamic navigational adjustments.
• DiPPeST achieves up to 92% obstacle avoidance in simulation and 80% success in real-world tests, highlighting its robust performance.

Insights into DiPPeST: A Diffusion-Based Path Planner for Quadruped Robots

The paper entitled “DiPPeST: Diffusion-based Path Planner for Synthesizing Trajectories Applied on Quadruped Robots” introduces an innovative approach to robotic path planning, particularly suited for quadrupedal robots operating in complex environments. The proposed method, DiPPeST, leverages a diffusion-based model to generate both global and local paths, demonstrating advanced adaptability and performance without requiring retraining when transitioning from simulation to real-world scenarios.

Key Contributions and Methodology

The central contribution of this work is the development of DiPPeST, which builds upon a previously established diffusion-based global path planner named DiPPeR. The novel aspect of DiPPeST is its zero-shot adaptation capability, allowing it to implement local path refinements in real-time based on RGB camera input. This is achieved without necessitating any additional training or fine-tuning. Importantly, the approach sidesteps traditional environment representation methods, such as semantic or geometric mappings, relying solely on visual input for trajectory planning.

DiPPeST's design incorporates an integrated global and local planning strategy. The global planner utilizes DiPPeR to generate an initial end-to-end trajectory without relying on a pre-mapped environment. The local planner, a new addition to this framework, adapts the trajectory in real-time by evaluating camera frames to navigate around obstacles, incorporating a novel Region of Interest (ROI) processing method that dynamically selects local goals within the current frame.

To facilitate real-world application, a robust visual servoing framework is implemented. This component translates the planned 2D image-based trajectories into actionable 3D movements for quadruped robots. Importantly, the methodology maintains constant inference speed, which is crucial for maintaining consistent performance during dynamic path planning.

Numerical Results and Performance

The paper reports DiPPeST achieving a 92% success rate in obstacle avoidance within nominal, relatively uncomplicated environments. This performance slightly decreases to an 88% success rate in more challenging environments, demonstrating its robustness against increased complexity and intensity variation in visual inputs. Furthermore, the real-world execution on a quadrupedal robot showcases an 80% success rate across various environments, marking an improvement compared to other state-of-the-art local planners, particularly in narrow environments.

The ability to maintain performance despite variations in camera perspective, input image size, and environmental conditions highlights the adaptability of DiPPeST. The framework's capacity to translate its learned behaviors from simulated environments to real-world applications without retraining is an essential characteristic that underscores its practical utility in robotics.

Implications and Future Directions

This research provides significant insights into the potential of diffusion models for robotic path planning, particularly illustrating how they can be adapted for real-time operation in unstructured environments. The ability to function effectively without retraining enhances the practicability of this approach for various real-world applications, from industrial automation to search and rescue operations.

Future developments could explore increasing the generalization capabilities of DiPPeST through adaptation strategies that incorporate more diverse training datasets, potentially integrating kinodynamic properties into the planning process to optimize both trajectory feasibility and execution. Furthermore, improving inference times through advancements in the diffusion process could facilitate more responsive replanning in dynamically changing environments.

In conclusion, DiPPeST represents a noteworthy advancement in the field of robotic path planning. Its innovative approach provides a strong foundation for future research and development aimed at enhancing the autonomy and adaptability of mobile robots operating in complex real-world settings. The paper distinctly contributes to the expanding body of knowledge on diffusion models in robotics, setting the stage for further exploration and optimization in this promising domain.