Compositional Diffusion-Based Continuous Constraint Solvers

This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on hand-engineering or learning generators for specific constraint types and then rejecting the value assignments when other constraints are violated. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP) derives global solutions to CCSPs by representing them as factor graphs and combining the energies of diffusion models trained to sample for individual constraint types. Diffusion-CCSP exhibits strong generalization to novel combinations of known constraints, and it can be integrated into a task and motion planner to devise long-horizon plans that include actions with both discrete and continuous parameters. Project site: https://diffusion-ccsp.github.io/

Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning

Robots planning long-horizon behavior in complex environments must be able to quickly reason about the impact of the environment's geometry on what plans are feasible, i.e., whether there exist action parameter values that satisfy all constraints on a candidate plan. In tasks involving articulated and movable obstacles, typical Task and Motion Planning (TAMP) algorithms spend most of their runtime attempting to solve unsolvable constraint satisfaction problems imposed by infeasible plan skeletons. We developed a novel Transformer-based architecture, PIGINet, that predicts plan feasibility based on the initial state, goal, and candidate plans, fusing image and text embeddings with state features. The model sorts the plan skeletons produced by a TAMP planner according to the predicted satisfiability likelihoods. We evaluate the runtime of our learning-enabled TAMP algorithm on several distributions of kitchen rearrangement problems, comparing its performance to that of non-learning baselines and algorithm ablations. Our experiments show that PIGINet substantially improves planning efficiency, cutting down runtime by 80% on average on pick-and-place problems with articulated obstacles. It also achieves zero-shot generalization to problems with unseen object categories thanks to its visual encoding of objects.

Let's Handle It: Generalizable Manipulation of Articulated Objects

In this project we present a framework for building generalizable manipulation controller policies that map from raw input point clouds and segmentation masks to joint velocities. We took a traditional robotics approach, using point cloud processing, end-effector trajectory calculation, inverse kinematics, closed-loop position controllers, and behavior trees. We demonstrate our framework on four manipulation skills on common household objects that comprise the SAPIEN ManiSkill Manipulation challenge.