Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models

Temel Kavramlar

Learning diffusion models as priors accelerates robot motion planning optimization by sampling from posterior trajectory distributions.

Özet

• Motion planning is crucial for autonomous robot systems, addressing pathfinding in configuration space.
• Sampling-based vs. optimization-based planners have trade-offs in efficiency and smoothness.
• Learning-based methods, like Motion Planning Diffusion, leverage prior trajectory distributions for improved planning.
• Diffusion models encode multimodal data effectively, enhancing trajectory generation.
• Experiments show the efficacy of Motion Planning Diffusion in various robot environments.
• Diffusion models offer strong priors for high-dimensional trajectory distributions.

İstatistikler

"Our main contributions are: (1) we learn a trajectory generative model with a diffusion model using expert trajectories generated with an optimal motion planning algorithm; (2) we formulate the motion planning problem as planning-as-inference by sampling from a posterior distribution leveraging guidance in diffusion models; (3) to validate our approach we present results in several environments with increasing difficulty; (4) we empirically demonstrate that learning and sampling from the diffusion model speeds up motion planning without an informed prior and is better than a commonly used generative model."
"The trajectory cost is cEE(τ) = PH−1 k=0 dSE(3) ((T (q[k]), Tg[k])), where T (q[k]) is the end-effector pose at waypoint k and Tg[k] the desired one."
"The diffusion models are trained for 25 diffusion steps with exponential scheduling (we found it to work better than linear scheduling [24])."

Alıntılar

"Our main contributions are: (1) we learn a trajectory generative model with a diffusion model using expert trajectories generated with an optimal motion planning algorithm; (2) we formulate the motion planning problem as planning-as-inference by sampling from a posterior distribution leveraging guidance in diffusion models; (3) to validate our approach we present results in several environments with increasing difficulty; (4) we empirically demonstrate that learning and sampling from the diffusion model speeds up motion planning without an informed prior and is better than a commonly used generative model."

Önemli Bilgiler Şuradan Elde Edildi

Motion Planning Diffusion

by Joao Carvalh... : arxiv.org 03-27-2024

https://arxiv.org/pdf/2308.01557.pdf

Daha Derin Sorular

How can diffusion models be further optimized for real-time applications in robotics

To optimize diffusion models for real-time applications in robotics, several strategies can be implemented. One approach is to optimize the architecture of the diffusion model itself. This can involve reducing the complexity of the model by adjusting the number of layers, neurons, or parameters to make the inference process faster. Additionally, utilizing more efficient training algorithms, such as distributed training or techniques like mixed-precision training, can significantly speed up the training process. Another optimization technique is to precompute certain components of the diffusion process to reduce the computational load during inference. This can involve precomputing certain intermediate results or using caching mechanisms to store and reuse computations. Furthermore, implementing hardware acceleration, such as utilizing GPUs or specialized hardware like TPUs, can greatly enhance the speed of inference for diffusion models in real-time robotics applications.

What are the potential limitations or drawbacks of relying heavily on diffusion models for motion planning

While diffusion models offer several advantages for motion planning in robotics, there are potential limitations and drawbacks to consider. One limitation is the computational complexity and time required for sampling from diffusion models, especially in high-dimensional spaces. This can impact the real-time applicability of diffusion models in scenarios where quick decision-making is crucial. Additionally, diffusion models may struggle with capturing complex dynamics or interactions in the environment, leading to suboptimal trajectories or solutions. Another drawback is the interpretability of diffusion models, as they are often considered black-box models, making it challenging to understand the reasoning behind the generated trajectories. Moreover, diffusion models require a significant amount of expert data for training, which may not always be readily available or feasible to collect in certain robotics applications.

How might the principles of Motion Planning Diffusion be applied to other fields beyond robotics

The principles of Motion Planning Diffusion can be applied to various fields beyond robotics, particularly in domains that involve trajectory optimization, generative modeling, and probabilistic inference. One potential application is in autonomous vehicles, where diffusion models can be used to learn and generate safe and efficient driving trajectories in complex environments. In healthcare, Motion Planning Diffusion concepts can be utilized for optimizing patient treatment plans or surgical procedures by generating optimal trajectories for medical devices or robotic-assisted surgeries. Furthermore, in finance, these principles can be applied to optimize trading strategies or portfolio management by generating diverse and risk-aware investment trajectories. Overall, the adaptability and flexibility of Motion Planning Diffusion make it a valuable tool for a wide range of applications beyond robotics.