Points2Plans: From Point Clouds to Long-Horizon Plans with Composable Relational Dynamics (2408.14769v2)

Abstract: We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point clouds. Given a language instruction and a point cloud of the scene, our framework initiates a hierarchical planning procedure, whereby a LLM generates a high-level plan and a sampling-based planner produces constraint-satisfying continuous parameters for manipulation primitives sequenced according to the high-level plan. Key to our approach is the use of a relational dynamics model as a unifying interface between the continuous and symbolic representations of states and actions, thus facilitating language-driven planning from high-dimensional perceptual input such as point clouds. Whereas previous relational dynamics models require training on datasets of multi-step manipulation scenarios that align with the intended test scenarios, Points2Plans uses only single-step simulated training data while generalizing zero-shot to a variable number of steps during real-world evaluations. We evaluate our approach on tasks involving geometric reasoning, multi-object interactions, and occluded object reasoning in both simulated and real-world settings. Results demonstrate that Points2Plans offers strong generalization to unseen long-horizon tasks in the real world, where it solves over 85% of evaluated tasks while the next best baseline solves only 50%.

• The paper presents a novel framework that uses composable relational dynamics to bridge continuous point clouds and symbolic planning for long-horizon robot manipulation.
• It employs a transformer-based delta-dynamics model and hierarchical planning to achieve over 85% success in real-world tasks while enhancing pose and predicate predictions.
• This approach reduces planning time and error accumulation, demonstrating robust performance in complex multi-object manipulation tasks.

Points2Plans: Composable Relational Dynamics for Long-Horizon Robot Manipulation

Introduction

The efficient execution of long-horizon manipulation tasks by robots in partially observable environments remains a formidable challenge, particularly when goals are specified through natural language instructions. Points2Plans offers a nuanced framework leveraging composable relational dynamics (RD) for hierarchical planning, capable of integrating high-dimensional perceptual inputs, such as partial-view point clouds. This framework bridges the gap between continuous and symbolic representations, enabling robots to undertake tasks that involve sophisticated geometric reasoning and interaction with multiple objects over extended horizons.

Model Architecture

The architecture of Points2Plans revolves around three core components: an encoder (Enc), a transformer-based dynamics model (T), and a decoder (Dec). The encoder processes segmented point clouds to generate object-centric latent states. The transformer-based dynamics model predicts the relative changes in the environment based on these latent states and the planned actions, thereby adopting a delta-dynamics approach that simplifies learning by focusing on relative changes. This aspect contrasts with absolute dynamics models used in prior works. The decoder interprets these latent state changes to update both the geometric (pose) and symbolic (predicate) states of the environment. This setup facilitates planning from high-dimensional inputs without pre-defining symbolic operators.

Hierarchical Planning Approach

Points2Plans employs a hierarchical planning strategy where a high-level task planner works in conjunction with a sampling-based planner. The task planner, guided by LLMs, generates candidate task plans and goals from natural language instructions. The sampling-based planner then determines the continuous parameters necessary to realize these plans, ensuring constraint satisfaction over the execution horizon. The interleaving of latent state predictions with geometric transformations during rollouts allows for improved prediction accuracy, mitigating the compounding of errors that can mar long-horizon planning in high-dimensional spaces.

Experimental Evaluation

The efficacy of Points2Plans is demonstrated through extensive experiments on tasks that require nuanced manipulative capabilities. These tasks include constrained packing, multi-object retrieval, and occluded object reasoning, tested in both simulated and real-world settings. The experimental results are substantial:

1. Success Rate: Points2Plans achieves over 85% success in real-world long-horizon tasks, outperforming the next best baseline, which only manages a 50% success rate.
2. Predicate and Pose Prediction: Utilizing a delta-dynamics approach alongside a hybrid rollout strategy significantly enhances prediction accuracy. This is evident in improved F1 scores for predicate prediction and reduced error in pose estimation, especially as task complexity increases.
3. Task Planning Time: Integrating LLMs as task planners substantially reduces planning time, showcasing a linear increase compared to the exponential increase observed with exhaustive search methods.

Implications and Future Directions

Points2Plans exhibits strong generalization capability to unseen long-horizon tasks, attributable to its reliance on single-step training data. This composable approach to skill sequencing offers a versatile infrastructure that can adapt to a variety of downstream tasks. The implications span both theoretical advancements in RD models and practical deployment in unstructured environments.

Future work can explore improvements along several axes:

1. Closed-Loop Execution: Integrating feedback mechanisms for closed-loop control will improve resilience against execution errors, allowing the robot to adapt dynamically to unexpected changes in the environment.
2. Enhanced Object Geometries: Extending the model to predict full object poses will enhance handling of more complex geometries, enabling manipulation of a broader array of objects.
3. Learned Predicates: Employing predicate learning techniques can reduce reliance on predefined operators, broadening the domain to open-world settings and enhancing robotic autonomy.

Conclusion

Points2Plans establishes a robust framework for long-horizon planning in robotics, harmonizing symbolic and geometric reasoning within a coherent structure. Its strength lies in its ability to utilize high-dimensional inputs effectively and its composable nature, which allows for flexibility and adaptability in sequential manipulation tasks. This research presents a significant step forward in enabling robots to perform intricate tasks in real-world environments, setting the stage for future innovations in robotic autonomy and intelligence.