Search Results for author:
Found 13 papers, 6 papers with code
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes.
STRIPS Planning in Infinite Domains
We introduce STRIPStream: an extension of the STRIPS language which can model these domains by supporting the specification of blackbox generators to handle complex constraints.
Learning compositional models of robot skills for task and motion planning
We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling.
Online Replanning in Belief Space for Partially Observable Task and Motion Problems
To solve multi-step manipulation tasks in the real world, an autonomous robot must take actions to observe its environment and react to unexpected observations.
Active model learning and diverse action sampling for task and motion planning
Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world.
Automated motion planning for robotic assembly of discrete architectural structures
While robotics for architectural-scale construction has made significant progress in recent years, a major challenge remains in automatically planning robotic motion for the assembly of complex structures.
Robotics
Learning to Rank for Synthesizing Planning Heuristics
We investigate learning heuristics for domain-specific planning.
Backward-Forward Search for Manipulation Planning
In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects.
Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion
There is increasing demand for automated systems that can fabricate 3D structures.
Integrated Task and Motion Planning
The problem of planning for a robot that operates in environments containing a large number of objects, taking actions to move itself through the world as well as to change the state of the objects, is known as task and motion planning (TAMP).
Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances
We present a strategy for designing and building very general robot manipulation systems involving the integration of a general-purpose task-and-motion planner with engineered and learned perception modules that estimate properties and affordances of unknown objects.
Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning
The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Task and Motion Planning (TAMP) approaches are effective at planning long-horizon autonomous robot manipulation.
