Planning from Point Clouds over Continuous Actions for Multi-object Rearrangement

2025-09-04

AI Summary

To address the challenge of long-horizon continuous spatial planning in multi-object 3D rearrangement, this paper proposes SPOT—a novel method that jointly searches for action sequences and object pose transformations directly on raw, unsegmented point clouds, entirely bypassing discrete modeling of states, actions, or relational structures. SPOT integrates learning and search: a neural action proposer, conditioned on point-cloud input, generates high-quality candidate actions; these are refined via heuristic search over the continuous action space to optimize the sequence of object transformations. The learned model serves as a domain-informed prior, effectively guiding search in high-dimensional continuous space. Evaluated on both simulation and real-robot platforms, SPOT significantly outperforms end-to-end policy-learning baselines. Results demonstrate the efficacy and generalizability of the “learning-guided search” paradigm for long-horizon manipulation tasks.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Long-horizon planning for robot manipulation is a challenging problem that requires reasoning about the effects of a sequence of actions on a physical 3D scene. While traditional task planning methods are shown to be effective for long-horizon manipulation, they require discretizing the continuous state and action space into symbolic descriptions of objects, object relationships, and actions. Instead, we propose a hybrid learning-and-planning approach that leverages learned models as domain-specific priors to guide search in high-dimensional continuous action spaces. We introduce SPOT: Search over Point cloud Object Transformations, which plans by searching for a sequence of transformations from an initial scene point cloud to a goal-satisfying point cloud. SPOT samples candidate actions from learned suggesters that operate on partially observed point clouds, eliminating the need to discretize actions or object relationships. We evaluate SPOT on multi-object rearrangement tasks, reporting task planning success and task execution success in both simulation and real-world environments. Our experiments show that SPOT generates successful plans and outperforms a policy-learning approach. We also perform ablations that highlight the importance of search-based planning.

Problem

• Planning robot manipulation in continuous action spaces
• Overcoming discretization of state and action spaces
• Handling multi-object rearrangement with point clouds

Innovation

• Hybrid learning-and-planning with learned priors
• Searches continuous transformations between point clouds
• Learned suggesters sample actions without discretization