Jason Andrew Wolfe

ICAPS Conference 2010 Conference Paper

Combined Task and Motion Planning for Mobile Manipulation

• Jason Andrew Wolfe
• Bhaskara Marthi
• Stuart Russell 0001

We present a hierarchical planning system and its application to robotic manipulation. The novel features of the system are: 1) it finds high-quality kinematic solutions to task-level problems; 2) it takes advantage of subtask-specific irrelevance information, reusing optimal solutions to state-abstracted subproblems across the search space. We briefly describe how the system handles uncertainty during plan execution, and present results on discrete problems as well as pick-and-place tasks for a mobile robot.

ICAPS Conference 2008 Conference Paper

Angelic Hierarchical Planning: Optimal and Online Algorithms

• Bhaskara Marthi
• Stuart Russell 0001
• Jason Andrew Wolfe

High-level actions (HLAs) are essential tools for coping with the large search spaces and long decision horizons encountered in real-world decision making. In a recent paper, we proposed an "angelic" semantics for HLAs that supports proofs that a high-level plan will (or will not) achieve a goal, without first reducing the plan to primitive action sequences. This paper extends the angelic semantics with cost information to support proofs that a high-level plan is (or is not) optimal. We describe the Angelic Hierarchical A* algorithm, which generates provably optimal plans, and show its advantages over alternative algorithms. We also present the Angelic Hierarchical Learning Real-Time A* algorithm for situated agents, one of the first algorithms to do hierarchical lookahead in an online setting. Since high-level plans are much shorter, this algorithm can look much farther ahead than previous algorithms (and thus choose much better actions) for a given amount of computational effort.

ICML Conference 2008 Conference Paper

Fully distributed EM for very large datasets

• Jason Andrew Wolfe
• Aria Haghighi
• Dan Klein 0001

ICAPS Conference 2007 Conference Paper

Angelic Semantics for High-Level Actions

• Bhaskara Marthi
• Stuart Russell 0001
• Jason Andrew Wolfe

High-level actions (HLAs) lie at the heart of hierarchical planning. Typically, an HLA admits multiple refinements into primitive action sequences. Correct descriptions of the effects of HLAs may be essential to their effective use, yet the literature is mostly silent. We propose an angelic semantics for HLAs, the key concept of which is the set of states reachable by some refinement of a high-level plan, representing uncertainty that will ultimately be resolved in the planning agent's own best interest. We describe upper and lower approximations to these reachable sets, and show that the resulting definition of a high-level solution automatically satisfies the upward and downward refinement properties. We define a STRIPS-like notation for such descriptions. A sound and complete hierarchical planning algorithm is given and its computational benefits are demonstrated.