DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability (2306.13196v3)

Abstract: Generative models such as diffusion models, excel at capturing high-dimensional distributions with diverse input modalities, e.g. robot trajectories, but are less effective at multi-step constraint reasoning. Task and Motion Planning (TAMP) approaches are suited for planning multi-step autonomous robot manipulation. However, it can be difficult to apply them to domains where the environment and its dynamics are not fully known. We propose to overcome these limitations by composing diffusion models using a TAMP system. We use the learned components for constraints and samplers that are difficult to engineer in the planning model, and use a TAMP solver to search for the task plan with constraint-satisfying action parameter values. To tractably make predictions for unseen objects in the environment, we define the learned samplers and TAMP operators on learned latent embedding of changing object states. We evaluate our approach in a simulated articulated object manipulation domain and show how the combination of classical TAMP, generative modeling, and latent embedding enables multi-step constraint-based reasoning. We also apply the learned sampler in the real world. Website: https://sites.google.com/view/dimsam-tamp

• The paper introduces diffusion models as effective samplers that generate constraint-satisfying samples for task and motion planning.
• It employs latent space representations learned from object observations to handle unseen and articulated objects in complex manipulation tasks.
• Experimental results show the approach outperforms regression and energy-based models in generating diverse, feasible trajectories under observability constraints.

Overview of the DiMSam Paper

The paper "DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability" explores leveraging diffusion models to enhance Task and Motion Planning (TAMP) in environments where full observability of the system state is not possible. The authors propose using deep generative models to learn constraints and samplers, which traditionally have been complex to design due to unknown dynamics in the environment. The research demonstrates how diffusion models, a specific type of generative model, can serve as effective samplers for generating samples that satisfy intricate constraints within a TAMP framework.

Contribution and Approach

The primary contributions of the paper are articulated as follows:

1. Diffusion Models as Samplers: The authors employ diffusion models to represent constraints in TAMP as probabilistic distributions, creating samplers that can generate samples which satisfy these constraints.
2. Latent Space Representation: The paper introduces the use of latent embeddings of object states, learned from observations like segmented point clouds, to represent the articulated objects during manipulation tasks. This enables the planning system to apply learned strategies to previously unseen objects.
3. Integration in TAMP Framework: The proposed approach integrates these learned samplers into a TAMP solver, augmenting its capability to operate in partially observable environments.

The methodological innovation lies in utilizing diffusion models to conditionally sample actions that fit the constraints of a desired motion or task, bypassing the need for the exhaustive manual specification of dynamics and geometries which is typical in traditional TAMP setups.

Experimental Evaluation

The research evaluates the implementation of DiMSam in a domain focused on articulated object manipulation, specifically using a robot interacting with a microwave under various constraints and goals. Performance is assessed by:

• Measuring the ability to generate constraint-satisfying plans using the learned diffusion samplers.
• Evaluating the approach against baseline techniques such as regression models and energy-based models (EBMs), demonstrating superior performance in generating diverse and feasible trajectories.
• Testing in real-world scenarios to validate the model's practical applicability and robustness.

The empirical results, detailed extensively in the paper, illustrate the method's effectiveness in improving planning success rates and enabling more efficient trajectory sampling under constraints like opening or closing doors and avoiding collisions.

Implications and Future Directions

The use of diffusion models as constraint-makers within TAMP highlights several significant implications for the field:

• Scalability: By abstracting state representations to latent spaces, the approach effectively handles increasing complexities without being limited by predefined model specifics.
• Application Versatility: The method's generalizability suggests potential extensions beyond the tested domain, potentially aiding in complex robotic tasks like multi-step manipulations and coverages in various autonomous systems.
• Foundation for Hybrid Systems: This research sets a foundation for developing hybrid planning systems that marry machine learning's adaptability with the structured predictability of classical planning approaches.

Speculatively, future research directions could focus on enhancing model robustness against varying environmental complexities, integrating additional feedback mechanisms for real-time adaptation, and expanding the diffusion model's application to broader robotics and automation problems. The paper serves as a promising step towards more adaptive, intelligent autonomous systems capable of operating with limited prior knowledge of their environments.